比特派官方下载苹果版|imus
作者： 比特派官方下载苹果版
2024-03-07 19:19:31
IMU传感器，你所需要知道的全部 - 知乎
IMU传感器，你所需要知道的全部 - 知乎切换模式写文章登录/注册IMU传感器，你所需要知道的全部Encontro文章翻译自英文：看到这篇blog写得挺好，就随手用机翻转到知乎，侵删。如果习惯看英文，还是建议直接看原博客，机翻的效果肯定不好，以后有时间再慢慢修改润色。--------------------以下是原文------------------当我第一次听到IMU这个词的时候，我认为这是一种我无法理解的很酷的技术。但一旦我有了一些使用它的经验，并理解了它的物理测量方法，恐惧很快就消失了。在这篇文章中，我试图向初学者解释什么是IMU传感器，它们是如何工作的，在哪里以及如何使用这些传感器。那么让我们开始吧。什么是IMU传感器？IMU是惯性运动单元（inertial motion unit，原文中是这样，虽然我认为应该是inertial measurement unit）的缩写。IMU传感器是加速度计和陀螺仪传感器的组合。它被用来检测加速度和角速度以表示运动和运动强度。我相信，当我们开始学习一些技术，特别是传感器，一个好的起点是先学习它的应用领域，然后我们才真正了解技术如何内部工作的细节，以帮助我们实现这些应用。这样，当我们讲到它是如何工作的部分时，那些例子就会自动地在我们的头脑中运行，这些技术背后的想法也会更容易掌握。让我们先从应用开始。目录1 IMU传感器的应用2 IMU是如何工作的?3 IMU数据处理4 IMU购买技巧5 相关文章IMU传感器的应用让我们看一些熟悉的例子，看看这是如何有用的。应用1:智能手机和平板电脑我相信，当我们水平倾斜手机时，我们的智能手机会神奇地从纵向转向横向，很多人都想当然地认为这是理所当然的。它怎么知道我们拿手机的方向?？答案是它能感知重力。在物理学中，当我们讨论重力时，我们经常使用“重力加速度”这个术语。这是因为重力是以加速度来测量的。在我们的地球上，重力加速度为9.81m/s2(海平面)，在月球上为1.62 m/s²。这就是我们在月球上感觉失重的原因。如上所述，IMU用于检测运动，测量运动的加速度和旋转速度。所以IMU可以用来测量重力，就像它可以测量加速度一样。在我们的手机上，通常会使用带有三轴加速度计的IMU来感知重力作用的方向。请看下面的图片。正如你在图片中看到的，IMU芯片被放置在手机内部，它通常有3个加速度计放置在3个方向。一个是沿着手机的长边(X方向)测量加速度，一个是沿着手机的短边(Y方向)测量加速度，一个是沿着手机的轴。如果重力加速度测量的加速度计放置在X方向上,就意味着我们是拿着电话在肖像模式和同样的如果我们拿着手机在横向模式,然后将感觉到重力加速度在加速度计放置在Y方向上。当我们改变手持手机的模式时，加速度计的读数就会改变，这反过来也会改变屏幕的方向。这只是使用IMU的加速度计部分负责测量加速度。应用# 2:计步器这是IMU传感器使用的另一个应用。当我们走路或跑步时，我们创造了一些加速模式，当我们的脚着地时，我们减速或减速，当我们的脚离开地面时，我们加速。因为这些走和跑对我们来说是自然的，我们只是从来没有注意到这个微小的加速，但相信我，它们是存在的!此外，当我们在移动时挥手时，我们也会产生一些微小的旋转动作，这对感知移动模式很有用，可以看出一个人是在行走、跑步还是保持静止。计步器有一个IMU传感器，通常连接到一个微控制器，微控制器处理来自IMU传感器的信息，并尝试查看是否有这种加速度和旋转模式。如果它能看到这一点，它就会增加步长计数器。便宜的计步器通常使用一些简单的数据处理算法和不太敏感的传感器。此外，他们通常不使用IMU的陀螺仪部分来感知步骤。因此，他们计算行走的步数通常是非常不准确的。更昂贵的传感器通常学习和适应个人的加速度模式，通过处理旋转和加速度数据，使用更敏感的IMU传感器，以确定一个人是否刚刚迈出了一步。应用#3:虚拟现实头盔虚拟现实是一项即将问世的技术，它将给游戏行业带来革命性的变化。如果你之前没有尝试过使用VR头盔，我建议你尝试一下，这是进入数字世界的一种有趣的方式，就像《黑客帝国》!如果你有一个很好的预算,你可以得到一个眼睛裂谷VR耳机(链接到亚马逊),或者如果你是一个紧你可以尝试一个廉价的替代品如Xiomi耳机或谷歌纸板(链接到亚马逊),对智能手机的虚拟现实体验!虚拟现实头盔主要使用这些IMU传感器来跟踪你的头部位置，以改变它发出的视频信号。例如，当你向上看时，你实际上是在绕x轴旋转你的头，这将被放置在你VR头盔里的IMU传感器的陀螺仪所感知，反过来，这将给你天空的视频feed。当你往下看的时候，你将你的头转向相反的方向，你就可以看到地面了!应用#4:无人机、直升机和飞机IMU传感器的另一个应用是跟踪无人机、直升机和飞机的方向和航向。通常，这些解决方案使用IMU传感器和电子罗盘(即磁力计)的组合。这种组合有AHRS传感器的技术名称。(姿态和航向参考系统)基本上加速度计的角度告诉我们无人驾驶飞机在地面,陀螺仪使用此数据作为参考和计算,偏航和滚动不断无人机飞行约和磁强计告诉我们的方向无人机是领导对我们地球的磁场,这样我们就能在地图上找到它!IMU和MARG传感器有什么区别?MARG代表磁力计、角速度和重力，基本上是指IMU和指南针(磁力计)的组合。如本应用中所讨论的，这些主要用于飞机。应用# 5:相机在相机中，IMU传感器、加速度计和陀螺仪的2个半部分分别用于2个单独的使用案例。在照相机中使用陀螺仪如果你对摄影和相机感兴趣，你可能会遇到一种叫做光学图像稳定的技术，简称OIS。这基本上是如何工作的是当我们正在视频,它使用陀螺仪IMU的一部分传感器,看看我们旋转摄像头,换句话说,如果我们颤抖的手,这样它可以通过旋转图像做适当的修正以相反的方式,这样我们的视频保持稳定,如果我们使用三脚架!在照相机中使用加速度计当摄影师在人像模式下拍照时，他们有不同的手持相机的风格。有些人把快门按钮放在上面，有些人把快门按钮放在下面。不管我们是拿着快门键在顶部还是底部的相机，当我们下载到电脑上时，照片总是以正确的方式显示出来。在这个应用程序中，加速度计被用来感知重力的方向，以检测图片的方向!IMU是如何运作的吗?现在我们已经很好地了解了IMU传感器的使用，现在让我们进入“它是如何工作的?””部分。让我们以单个传感器、加速度计、陀螺仪和磁力计为例，看看它们是如何工作的加速度计是如何工作的?在我们开始研究加速计如何工作的技术细节之前，让我们先来一次想象中的公共汽车之旅来理解加速度。假设你坐在车里，假设是一列火车这样就有更多的空间来做我们的思想实验。现在想象一个氦气球被绑在你面前的座位上。当火车处于站立位置时，气球保持完全垂直。一旦火车开始移动，气球就会回到你身边。这是由于气球的惯性和火车的加速度。惯性是物理学领域的一个概念。它基本上意味着任何物体都会抵抗加速和减速。在我们的实验中，由于火车开始加速，我们在火车里的气球也开始加速。但是和其他物体一样，气球也有质量(就是空气和橡胶的重量)。所以物理学(和常识!)告诉我们，气球会抵抗这个加速度，因此它的加速度比火车稍慢，因此它向后移动了一点。这个概念也适用于我们。当车辆开始移动时，我们所感受到的最初的震动是加速度的影响。因为我们的身体，就像气球一样，有一定的质量(或者用外行人的话说就是重量)。一旦列车达到巡航速度，速度表上的指针停止移动，气球就会回到垂直位置，因为它已经赶上了火车。同样的故事发生在相反的方向，当火车减速并停下来。现在想象一个箱子，质量悬挂在两个弹簧之间，如图所示，被放在车里。假设它被固定在汽车的地板上。当车辆开始移动时，质量会向与车辆移动方向相反的方向移动，向箱体的背面移动。(到B侧).一旦我们的飞行器达到稳定的速度和巡航速度，就像我们的气球实验一样，质量将回到中心点c。当飞行器减速到，正如你猜的那样，它将向前移动到a侧!好了，快到了!现在让我们在A和b两边放两个电极板，并将电池连接到上面。它会变成一个电容器。一边带正电荷，另一边带负电荷，在它们之间形成电场。如果我们的质量是由介质组成的，它在电场中移动，它会稍微扰动它，从而改变我们设置的电容值。电容的变化正比于盒子加速的速度。利用这些数据，我们可以测量任何物体的加速度!所以我们的加速度计传感器基本上有上面的装置，里面有电极，弹簧和质量，但是是小型化的。多年来，随着技术的进步，上述设置已经被做得越来越小，现在我们是在MEMS时代。什么是MEMS IMU传感器?MEMS是微机电系统的缩写。微的:因为所有尺寸都在千分尺范围内电:因为我们在里面有电极来制造电容器，它们形成了电气系统机械:我们的质量和弹簧形成了机械系统系统:它们一起组成一个系统，服务于一个特定的目的，在我们的例子中是测量加速度。这就是加速度计的工作原理。现在让我们来看看陀螺仪，这更容易理解，因为我们已经掌握了MEMS系统的基础知识。陀螺仪是如何工作的?这一次，让我们拿着我们的气球，在我们当地的地面上进行一次想象中的跑步吧!这次让我们在400米跑道的弯曲部分奔跑，观察我们的气球的行为，从顶部看!如果你从顶部看气球，你会注意到它实际上是在向一边移动，朝着曲线的外部区域移动(假设任何方向都没有风，我们没有在加速度计上使用这个实验，因为一旦我们达到巡航速度，由于风的阻力，我们的气球将永远不会达到垂直位置)你也可以用我们想象的火车旅行来重复同样的实验，当火车走一条弯曲的路径时，气球会向半径的方向移动，如图所示。现在拿出气球,放在我们的盒子质量和弹簧与加速度计相似,唯一的区别是现在粘在地板上在另一个角度,这样质量可以走向火车的窗户,而不是前后运动。所以当火车向左转时，质量就向右移动，当火车向右转时，质量就向左移动。轨道越弯曲，火车移动越快，物体移动得越多。现在让我们再次放入电极，这就是我们的机电陀螺仪，它可以测量角速度，使它更小，我们有了MEMS陀螺仪!现在我们已经了解了加速度计和陀螺仪是如何工作的，让我们继续看一点关于数据处理的内容。IMU数据处理数据处理基本上就是从我们的传感器中获取数据，并从中找出意义。IMU传感器这些天给输出的数字值而不是模拟电容改变,他们的经验,使我们的工作变得更加容易,因为我们不必处理模拟到数字的转换(ADC)自己(你可以阅读更多关于本文的ADC单片机外围设备)所以我们要做的就是在需要的时候使用这些数字。很多大学都开发了很多算法，我们可以用在我们的项目上。我们需要做的就是在代码中实现数学公式。正如我们在上面看到的，一些应用程序需要结合加速计和陀螺仪读数，如计步器，VR头盔和无人机。通常情况下，我们会使用一种叫做四元数的数学量来简化这些数据，从而测量感兴趣的对象的方向，换句话说，就是计算我们的VR头盔是朝哪个方向的，比如向上、向下或一侧。什么是四元数，它们在哪里使用?四元数基本上是一种数学方法，它将来自IMU传感器的数据组合在一起，并以一种从所需的处理能力方面使其计算更容易的方式呈现出来。它们可以通过两种方式计算，一种是在不需要方向的应用程序上使用IMU传感器，另一种是使用MARG传感器阵列，方向组件是有用的。任何类型的传感器通常都会遇到的另一个问题是噪音。不是声音噪音，而是一些与其他数据相比没有意义的数据。什么是传感器噪声?让我们举一个熟悉的电子称重机的例子。现在开始量体重吧。假设你的体重是50.0公斤，现在拿一个小的重量，比如100克，比如一个手机，然后再测量一次你的体重。按照逻辑，机器应该显示50.1公斤，但可能你的秤仍然会显示你只有50公斤。这是因为过滤。传感器不会一直提供稳定准确的数据。所以即使是两次相同的输入，输出也会略有不同。因此，你的机器的制造商放入一些数学过滤器来显示相同的值，即使传感器的实际输出略有变化，比如+/- 1%。如何过滤传感器噪声?现在让我们回到IMU。MEMS陀螺仪的原始陀螺仪读数样本，当完全静止放置在你的桌子上时，可能看起来像这样0、1、23 0 10 2 0...第一个数字是角速度，单位是每秒角度绕x轴，第二个数字是绕y轴，第三个数字是绕z轴。现在就像我说的，传感器完全静止在桌子上，那么这些微小的变化是什么呢?它们被称为传感器噪声。之所以这样命名，是因为它们可能是由一百万个不同的因素引起的，通常从数学上过滤掉它们比纠正每一个因素更容易。假设我们使用了一个条件，如果值在0和3之间，我们将其作为零。现在，在我们应用这个过滤器之后，我们的读数是这样的0, 0, 00, 0, 00, 0, 0...这就解释了为什么它一动不动地躺着。上面的例子是一个简单的滤波器，但还有其他更复杂的滤波器可用，一个例子可能是卡尔曼滤波器。这是关于IMU传感器的数据处理方面。IMU购买提示在选择IMU传感器时，我们必须看到哪些参数?IMU传感器通常会给出我们上面看到的数字数据。在为应用程序选择IMU传感器时，我们需要考虑两个因素。ADC输出中使用的位数和传感器支持的范围/灵敏度组合。ADC输出的位数基本上，这些传感器使用模拟到数字转换，以改变模拟物理量，如电压和电流的数字可读值。这些值的准确性取决于转换的准确性。您可以阅读这篇文章，特别是模拟到数字转换部分，以了解它的过程。通常，这个位数是8、10、12或16位。简单地说，比特数越多，精确度就越高。范围/敏感性量程是指这些传感器可以测量的加速度或角速度。例如，加速度范围通常为+/- 2g、4g、8g和16g (1g = 9.81m/s2重力加速度)。这些范围也可以因传感器而异。有些传感器支持多个量程，我们可以改变传感器的设置，使其使用一个特定的量程。量程越大，灵敏度越低，反之亦然。例如，如果您将范围设置为+/= 16g，您可以测量可能高达0.1g的变化，但如果您将范围设置为+/- 2g，那么您可以测量可能高达0.05g的变化。不要太担心这些数字，因为您总是可以找到一个类似的应用程序，并确定要获得哪个。你可以使用这个MPU6050传感器开始，如果你发现你需要更多的灵敏度或数据输出，如四元数，然后你总是可以采取一个更专业的，像这个BNO080传感器博世。好了，我就讲到这里。我希望你们喜欢这篇文章。如果您有任何问题或建议，可以通过这个链接给我们发邮件或与我们联系。如果你喜欢这篇文章，请与你的朋友和同事分享!Related Articles编辑于 2023-12-14 22:57・IP 属地未知IMU嵌入式系统传感器赞同 1548 条评论分享喜欢收藏申请
IMUs Overview | Bosch Sensortec
IMUs Overview | Bosch Sensortec
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IMUs
A powerful combination: IMUs
Bosch Sensortec optimizes its IMUs (Inertial Measurement Units) for advanced smartphones, wearables, AR and VR, drones, gaming and robots applications. They are designed to provide maximum flexibility to customers. An IMU combines a gyroscope with an accelerometer in one system-in-package (SiP). It enables for examples real-time motion detection, indoor navigation, gesture and activity recognition as well as optical image stabilization (OIS).
BMI323
The BMI323 is a general purpose, low-power Inertial Measurement Unit (IMU) that combines precise acceleration and angular rate (gyroscopic) measurement with intelligent on-chip motion-triggered interrupt features.BMI323 targets fast and accurate inertial sensing in all applications. It is an easy-to-use IMU with an integrated feature set.
Easy-to-use standard IMU I3C, I2C, SPI Interface Low-noise gyroscope
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BMI270
The BMI270 is an ultra-low power smart IMU optimized for wearable and hearable applications. The IMU combines precise acceleration and angular rate measurement with intelligent on-chip motion-triggered interrupt features. The BMI270 includes intuitive gesture, context and activity recognition with an integrated plug-and-play step counter.
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The BMI088 is a high-performance Inertial Measurement Unit. Allowing for highly accurate measurements of orientation and detection of motion along three orthogonal axes. The BMI088 is the ideal IMU solution for drone and robotics applications.
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The BMI085 is a high-performance IMU specifically designed for Augmented/Virtual Reality (AR/VR) applications. Providing accurate and reliable 6-DoF motion tracking data even under demanding conditions, BMI085 is a perfect choice for orientation tracking applications even under demanding conditions.
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Nobody’s Fool Jamming and Spoofing Detection
PNT from and for space: What are the steps necessary to make LEO positioning a reality?
Positioning While Highly Dynamic
PPP Set Free: Precise Positioning Now for Everyone
Precision GNSS and Sensor Fusion in Autonomous Vehicles
On the Road to Autonomy: Predictions for the Future
Robust Dual-Antenna Receiver: Jamming/Spoofing Detection and Mitigation
Sensor Fusion in Autonomous Vehicles
Space weather's impact on GNSS
Testing the Limits: A behind-the-scenes look at extreme testing and GNSS+INS performance
Where precision meets reliability: Enabling autonomy through GNSS corrections
Why is “good enough” positioning not okay in ag autonomy
NovAtel at Intergeo 2020
High Precision Positioning for Automotive
Autonomous Vehicle Safety: How to Test, How to Ensure
Antennas for Autonomous Automotive Applications
Detection and Geolocation of GNSS Interference
Advancements in GNSS+INS Technology and Integration
GALILEO: Dawn of a New Age of GNSS Service
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SPAN® technology tightly couples NovAtel's GNSS receiver technology with an Inertial Measurement Unit (IMU). We market a variety of IMUs from leading IMU manufacturers to provide our customers the opportunity to select an IMU that delivers the accuracy or price point their application requires.
P-1750
The P-1750 IMU offers tactical grade performance in a compact and rugged package.
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EG320N
Commercial MEMS IMU integrates with SPAN® technology to deliver 3D position, velocity and attitude.
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EG370N
The OEM-IMU-EG370N is a high performing Micro Electro Mechanical Systems (MEMS) Inertial Measurement Unit (IMU) from Epson.
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STIM300
Commercial MEMS IMU which integrates with SPAN® technology to deliver 3D position, velocity and attitude.
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HG4930
The OEM-IMU-HG4930 is a small and affordable high-performance MEMS IMU that is combined with SPAN Technology to provide 3D position, velocity and attitude.
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HG1930
Small, affordable MEMS IMU pairs with SPAN® technology to deliver 3D position, velocity and attitude.
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HG1900
High end tactical grade Mems IMU enclosure for commercial and military guidance and navigation applications.
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µIMU-IC
High performing MEMS IMU offers continuous and stable positioning.
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LN200
Tactical grade, low noise IMU combines with NovAtel’s GNSS technology to provide 3D position, velocity and attitude solution.
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ISA-100C
High performance tactical grade IMU combines with NovAtel's GNSS technology to deliver 3D position, velocity and attitude solutions.
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HG1700 AG58
Economical, tactical grade OEM IMUs from Honeywell.
Learn More
What is an Inertial Measurement Unit (IMU)?
An IMU is made up of six complimentary sensors arrayed on three orthogonal axes, each axes has an accelerometer and gyroscope which allows an IMU to measure its precise relative movement in 3D space.
How do IMUs Work?
The accelerometers measure linear acceleration and the gyroscopes measure rotational acceleration. This information allows the IMU to measure its precise relative movement in 3D space. IMU measurements are also able to provide an angular solution about the three axes.
Related InformationSPAN BrochureSPAN Overview and Integration GuideInertial Navigation Systems and Vibration
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Accelerometers, Gyros, and IMUs: The Basics – ITP Physical Computing
Accelerometers, Gyros, and IMUs: The Basics – ITP Physical Computing
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Accelerometers, Gyros, and IMUs: The Basics
Introduction
Inertial Motion Units (IMUs) are sensors that measure movement in multiple axes. Accelerometers measure a changing acceleration on the sensor. They can be used to measure the tilt of the sensor with respect of the Earth, or the force of a hit. They are common in mobile devices and automobiles. Gyrometers measure changing angular motion. They can be used to measure rotation. Magnetometers measure the magnetic force on a sensor. These are usually used to measure the Earth’s gravitational field in order to determine compass heading. IMUs have become increasingly common in microcontroller projects, to the point where they are built into some microcontroller boards like the Arduino 33 IoT and BLE Sense, and the Arduino 101. In this lesson, you’ll learn a few principles of working with these sensors, and see some examples.
What You’ll Need to Know
To get the most out of this lab, you should be familiar with the following concepts and you should install the Arduino IDE on your computer. You can check how to do so in the links below:
What is a microcontrollerBeginning programming termsThe basics of electricityWhat is a solderless breadboard and how to use oneGetting Started with Arduino GuideThe basics of serial communication, both asynchronous and synchronous
Things You’ll Need
Figures 1-4 below show the parts you’ll need for this exercise. Click on any image for a larger view.
Figure 1. Microcontroller. Shown here is an Arduino Nano 33 IoT. An Uno will do for some of the examples here, though.
Figure 2. Jumper wires. You can also use pre-cut solid-core jumper wires.
Figure 3. A solderless breadboard
Figure 4. An accelerometer. Shown here is an ST Microelectronics LIS3DH accelerometer. Others will be mentioned below.
Orientation, Position, and Degrees of Freedom
“Orientation, or compass heading, is how you determine your direction if you’re level with the earth. If you’ve ever used an analog compass, you know how important it is to keep the compass level in order to get an accurate reading. If you’re not level, you need to know your tilt relative to the earth as well. In navigational terms, your tilt is called your attitude, and there are two major aspects to it: roll and pitch. Roll refers to how you’re tilted side-to-side. Pitch refers to how you’re tilted front-to-back.
“Pitch and roll are only two of six navigational terms used to refer to movement. Pitch, roll, and yaw refer to angular motion around the X, Y, and Z axes. These are called rotations. Surge, sway, and heave refer to linear motion along those same axes. These are called translations. These are often referred to as six degrees of freedom. Degrees of freedom refer to how many different parameters the sensor is tracking in order to determine your orientation.
“You’ll hear a number of different terms for these sensors. The combination of an accelerometer and gyrometer is sometimes referred to as an inertial measurement unit, or IMU… When an IMU is combined with a magnetometer, the combination is referred to as an attitude and heading reference system, or AHRS. Sometimes they’re also called magnetic, angular rate, and gravity, or MARG, sensors. You’ll also hear them referred to as 6-degree of freedom, or 6-DOF, sensors. There are also 9-DOF sensors that incorporate all three types of sensors. Each axis of measurement is another degree of freedom. [There are] even has 10-DOF sensor[s] that add barometric pressure sensor[s] for determining altitude.”From Making Things Talk, 3rd edition
Features of an IMU
Whether you’re dealing with an accelerometer, gyrometer, or magentometer, there are a few features you’ll need to consider:
Range – IMU sensors come in different ranges of sensitivity.
Acceleration is generally measured in meters per second squared (m/s^2) or g’s, which are multiples of the acceleration due to gravity. 1g = 9.8 m/s^2. Accelerometers come in ranges from 2g to 250g and beyond. the force of gravity is 1g, but a human punch can be upwards of 100g
Angular motion is measured in degrees per second (dps). Gyro ranges of 125dps to 2000 dps are not uncommon.
Magnetic force is measured in Teslas. In most direction applications, the important measurement, however is the relative magnetic field strength on each axis.
Number of axes – Almost all IMU sensors can sense their respective properties on multiple axes. Whatever activity you’re measuring, you’ll most likely want to know the acceleration, rotational speed, or magnetic force in horizontal and vertical directions. Most sensors give results for the X, Y, and Z axes. Z is typically perpendicular to the Earth, and the other two are parallel to it, but perpendicular to each other.Electrical Characteristics – as with any electronic sensor, you should pay attention to current consumption and make sure the rated voltage of your IMU is compatible with your microcontroller.Interface – IMUs come with a variety of interfaces. Some provide a changing analog voltage on each axis. Others will provide an I2C or SPI synchronous serial interface. Older IMUs will provide a changing pulse width that corresponds with the changing properties of the sensor. Nowadays, most IMUs are either I2C, SPI, or analog.Extra Features – in addition to the basic physical properties, many IMUs will have additional features, like freefall detection or tap detection, or additional control features like the ability to set the sensing rate.
For more on choosing an IMU, Sparkfun has an excellent introductory guide.
Most vendors of accelerometer modules do not actually make the sensors themselves, they just put them on a breakout board along with the reference circuit, for convenience. While you might buy your IMU from Sparkfun, Adafruit, Seeed Studio, or Pololu, for example, the chances are the actual sensor is manufactured by another company like Analog Devices, ST Microelectronics, or Bosch. When you shop for a sensor module, check out the manufacturer’s datasheet in addition to the vendor’s tech specs.
Analog IMUs
Analog IMU sensors typically have an output pin for each axis that outputs a range from 0 volts to the sensor’s maximum voltage. Most of them only have one form of sensor (accelerometer, gyrometer, magnetometer). Having multiple pins for each type of sensor would be unweildy.
Since it’s possible to have both positive and negative change in a given sensor’s range, the rest value for each output pin is usually in the middle of the voltage range. You need to understand this in order to read the sensor.
Analog Devices’ ADXL series of accelerometers are useful examples of this kind of sensor. There are two similar models, the ADXL335 and the ADXL377. The former is good for simple range of motion applications, and latter is designed for high-acceleration applications, like crashes, punches, and so forth. Both operate at 3.3V. Both output analog voltages for X, Y, and Z. Both output their midrange voltage, about 1.65V, on each axis when it’s at rest (at 0g). The ADXL335 is a +/-3g accelerometer, and the ADXL377 is a +/- 200g accelerometer. While you’d see a significant change on the ADXL335’s axes when you simply tilt the sensor, you’d see barely any when you simply tilt the ADXL377. Why? Consider the math:
The ADXL335 outputs 0V at -3g, 3.3V at +3g, and 1.65V at 0g. That means that 1g of acceleration changes the analog output by 1/6 of its range. If you’re using analogRead() on an Arduino with a 3.3V analog reference voltage, that means you’ve got a range of about 341 points per g of acceleration (1024 / 6). When you tilt the accelerometer to 90 degrees, you’re getting +1g on the X or Y axis. That’s a reading of about 682 using analogRead() on an Arduino. When you tilt it 90 degrees the other way, you’re getting -1g on the same axis, or about 341 using analogRead().
The ADXL377 outputs 0V at -200g, 3.3V at +200g, and 1.65V at 0g. That means 1g of acceleration changes the output by only 1/400 of its range. The same tilting action described above would give you a change from about 510 to 514 using analogRead(),using the same math as above.
This lesson applies whether you’re measuring acceleration, rotation, or magnetic field strength. Make sure to use a sensor that matches the required range otherwise you won’t see much change, particularly with an analog sensor.
Analog Accelerometer Example
Figure 5 shows the schematic for connecting an ADXL335 to an Arduino, and Figures 6 and 7 show the breadboard view for the Uno and the Nano, respectively. For both boards, the accelerometer’s Vcc pin is connected to the voltage bus, and its ground pin is connected to the ground bus. The X axis pin is connected to the Uno’s analog in 2, the Y axis pin is connected to the Uno’s analog in 1, and the Z axis pin is connected to the Uno’s analog in 0.
Other analog IMUs are wired similarly.
Figure 5. Schematic view of an Arduino connected to an ADXL3xx accelerometer. The accelerometer’s Vcc pin is connected to 3.3V on the Arduino, and its ground pin is connected to ground . The X axis pin is connected to the Arduino’s analog in 2, the Y axis pin is connected to the Arduino’s analog in 1, and the Z axis pin is connected to the Arduino’s analog in 0.
Figure 6. Breadboard view of an Arduino Uno connected to an ADXL3xx accelerometer. The Uno is connected to a breadboard, with its 3.3V pin (not 5V as in other examples) connected to the voltage bus and its ground pin connected to the ground bus. The accelerometer is connected to six rows in the left center section of the breadboard beside the Uno.
Figure 7. Breadboard view of an Arduino Nano connected to an ADXL3xx accelerometer. The Nano is connected as usual, straddling the first fifteen rows of the breadboard with the USB connector facing up. Voltage (physical pin 2) is connected to the breadboard’s voltage bus, and ground (physical pin 14) is connected to the breadboard’s ground bus. The accelerometer is connected to six rows in the left center section of the board below the pushbutton.
The code below will read the accelerometer and print out the values of the three axes:
void setup() {
Serial.begin(9600);
}
void loop() {
int xAxis = analogRead(A2); // Xout pin of accelerometer
Serial.print(xAxis);
int yAxis = analogRead(A1); // Yout pin of accelerometer
Serial.print(",");
Serial.print(yAxis);
int zAxis = analogRead(A0); // Zout pin of accelerometer
Serial.print(",");
Serial.println(zAxis);
}
Digital IMUs
Digital IMUs output a digital data stream via a serial interface, typically SPI or I2C. Unlike analog IMUs, these sensors can be configured digitally as well. Most support changing the sensitivity and the sampling rate, and some allow you to turn on and off features like tap detection or freefall detection.
Many digital IMUs are truly IMUs, in that they combine multiple sensors: accelerometer/gyrometer, accelerometer/gyrometer/magnetometer, and so forth.
Another advantage of digital IMUs is that they tend to convert their sensor readings at a higher level of resolution than a microcontroller’s analog input. While a microcontroller’s ADC is typically 10-bit (0-1023), many digital IMUs read their sensors into a 16-bit or even 32-bit result. This gives you greater sensitivity than an analog IMU.
Digital Accelerometer Example
Figure 8 shows the schematic for connecting a LIS3DH accelerometer to an Arduino, and Figures 9 and 10 show the breadboard view for the Uno and the Nano, respectively. For both the Uno and the nano, the accelerometer’s Vcc pin is connected to the voltage bus, and its ground pin is connected to the ground bus. The SDA pin is connected to the microcontroller’s analog in 4, the SCL pin is connected to the microcontroller’s analog in 5 , and the SDO is connected to the ground bus. Other digital IMUs are wired similarly.
Figure 8. Schematic view of an Arduino connected to an LIS3DH accelerometer. The accelerometer’s Vcc pin is connected to 3.3V on the Arduino, and its ground pin is connected to the Arduino’s ground. The SDA pin is connected to the Uno’s analog in 4, the SCL pin is connected to the Uno’s analog in 5, and the SDO is connected to ground.
Figure 9. Breadboard view of an Arduino Uno connected to an LIS3DH accelerometer. The Uno is connected to a breadboard, with its 3.3V pin (not 5V as in other examples) connected to the voltage bus and its ground pin connected to the ground bus. The accelerometer is straddling the center of the board.
Figure 10. Breadboard view of an Arduino Nano connected to an LIS3DH accelerometer. The Nano is connected as usual, straddling the first fifteen rows of the breadboard with the USB connector facing up. Voltage (physical pin 2) is connected to the breadboard’s voltage bus, and ground (physical pin 14) is connected to the breadboard’s ground bus. The accelerometer is straddling the center of the board below the Nano.
The following code will read the accelerometer and print out the acceleration on each axis in g’s. This accelerometer has a 14-bit range of sensitivity. The code below configures the accelerometer for a 2g range, and converts the 14-bit range to -2 to 2g. This is a simplification of one of the LIS3DH library examples by Kevin Townsend. It uses Adafruit’s LIS3DH library, but will work for any breakout board for the LIS3DH. It’s been tested with both Sparkfun’s and Adafruit’s boards for this sensor:
#include "Wire.h"
#include "Adafruit_LIS3DH.h"
Adafruit_LIS3DH accelerometer = Adafruit_LIS3DH();
void setup() {
Serial.begin(9600);
while (!Serial);
if (! accelerometer.begin(0x18)) {
Serial.println("Couldn't start accelerometer. Check wiring.");
while (true); // stop here and do nothing
}
accelerometer.setRange(LIS3DH_RANGE_8_G); // 2, 4, 8 or 16 G
}
void loop() {
accelerometer.read(); // get X, Y, and Z data
// Then print out the raw data
Serial.print(convertReading(accelerometer.x));
Serial.print(",");
Serial.print(convertReading(accelerometer.y));
Serial.print(",");
Serial.println(convertReading(accelerometer.z));
}
// convert reading to a floating point number in G's:
float convertReading(int reading) {
float divisor = 2 <<; (13 - accelerometer.getRange());
float result = (float)reading / divisor;
return result;
}
Here’s a link to some more examples for the LIS3DH which work with either Adafruit’s or Sparkfun’s LIS3DH board.
Built-in IMUs
Some microcontroller boards, like the Nano 33 IoT and Nano 33 BLE sense and the 101, have built-in IMUs. These are digital IMUs, and they’re connected to either the SPI or I2C bus of the microcontroller. This means you might have conflicts if you’re using them along with an external I2C or SPI sensor. For example, when you’re using them as an I2C sensor, you need to know their I2C address so you don’t try to use another I2C sensor with the same address.
Most built-in IMUs will come with a board-specific library, like the 101’s CurieBLE or the Nano 33 IoT’s Arduino_LSM6DS3 library. Otherwise, they will be identical to the digital IMUs, so even with a built-in accelerometer, you can get more information from the accelerometer’s datasheet or the library’s header files.
Nano 33 IoT Built-In IMU Example
The LSM6DS3 IMU that’s on the Nano 33 IoT is an accelerometer/gyrometer combination. You can get both acceleration and rotation from it. The IMU is built into the board, so there is no additional circuit.
The code example below will read the accelerometer in g’s and the gyrometer in degrees per second and print them both out:
#include "Arduino_LSM6DS3.h"
void setup() {
Serial.begin(9600);
// start the IMU:
if (!IMU.begin()) {
Serial.println("Failed to initialize IMU");
// stop here if you can't access the IMU:
while (true);
}
}
void loop() {
// values for acceleration and rotation:
float xAcc, yAcc, zAcc;
float xGyro, yGyro, zGyro;
// if both accelerometer and gyrometer are ready to be read:
if (IMU.accelerationAvailable() &&
IMU.gyroscopeAvailable()) {
// read accelerometer and gyrometer:
IMU.readAcceleration(xAcc, yAcc, zAcc);
// print the results:
IMU.readGyroscope(xGyro, yGyro, zGyro);
Serial.print(xAcc);
Serial.print(",");
Serial.print(yAcc);
Serial.print(",");
Serial.print(zAcc);
Serial.print(",");
Serial.print(xGyro);
Serial.print(",");
Serial.print(yGyro);
Serial.print(",");
Serial.println(zGyro);
}
}
What To Look For in an IMU Library
Different vendors will generally write their own libraries for the IMUs they sell. When you’re looking at a given vendor’s product, take a look at the properties of the sensor in the vendor’s datasheet, and the list of public functions in the library’s API. Does the library give you the functions of the sensor that you need? If the sensor supports multiple sensing ranges, does the library give you access to setting and getting the range? Is it well-documented, and well-commented? Are there simple, clear, well-commented examples?
For example, both Sparkfun and Adafruit make breakout boards for the LIS3SH accelerometer. This accelerometer is typical for a digital accelerometer; it’s got I2C and SPI interfaces, operates at 3.3V, and has a range of acceleration sensitivity, from 2g to 16g. The Adafruit getting started guide and the Sparkfun getting started guide get you up and running, but neither provides a summary of all the functions in their libraries. To see that, you need to look at the header files for each library. Adafruit’s header file is exhaustively commented, which can take time to get through. The key public functions start around line 344. It relies on their Unified Sensor library, which adds some complexity, but there are some nice additions, like the click functionality that the accelerometer supports. Sparkfun’s header file is less thoroughly commented, but shorter. The key public functions start about line 116. If you only need the basic acceleration functions, it’s easier to use because of less dependency on other libraries. Both are good libraries, though, and you should choose based on the features you want and how easy you find each to use.
You could use either library with either breakout board, but there is one catch: when you’re using the board’s I2C synchronous serial interface, you have to pay attention to the address you use in the the Adafruit board defaults to a different I2C address than the Sparkfun one. SparkFun defaults to 0x19, while Adafruit defaults to 0x18. In I2C mode, the SDO pin switches the I2C default address between 0x18 and 0x19. Taking this pin HIGH sets the address to 0x19, while taking it LOW sets it to 0x18, so by changing this pin, you can choose which library you prefer. Both libraries also have the ability to change the address they use for the accelerometer as well.
Determining Orientation
Determining orientation from an IMU takes some advanced math. Fortunately, there are a few algorithms for doing it. In 2010, Sebastian Madgwick developed and published a more efficient set of algorithms for determining yaw, pitch, and roll using the data from IMU sensors. Helena Bisby converted Madgwick’s algorithms into a Madgwick library for Arduino, improved upon by Paul Stoffregen and members of the Arduino staff. Though it was originally written for the Arduino 101, it can work with any IMU as long as you know the IMU’s sample rate and sensitivity ranges. Here’s an example that uses the Madgwick library and the Nano 33 IoT’s LSM6DS3 IMU to determine heading, pitch, and roll:
#include "Arduino_LSM6DS3.h"
#include "MadgwickAHRS.h"
// initialize a Madgwick filter:
Madgwick filter;
// sensor's sample rate is fixed at 104 Hz:
const float sensorRate = 104.00;
void setup() {
Serial.begin(9600);
// attempt to start the IMU:
if (!IMU.begin()) {
Serial.println("Failed to initialize IMU");
// stop here if you can't access the IMU:
while (true);
}
// start the filter to run at the sample rate:
filter.begin(sensorRate);
}
void loop() {
// values for acceleration and rotation:
float xAcc, yAcc, zAcc;
float xGyro, yGyro, zGyro;
// values for orientation:
float roll, pitch, heading;
// check if the IMU is ready to read:
if (IMU.accelerationAvailable() &&
IMU.gyroscopeAvailable()) {
// read accelerometer &and gyrometer:
IMU.readAcceleration(xAcc, yAcc, zAcc);
IMU.readGyroscope(xGyro, yGyro, zGyro);
// update the filter, which computes orientation:
filter.updateIMU(xGyro, yGyro, zGyro, xAcc, yAcc, zAcc);
// print the heading, pitch and roll
roll = filter.getRoll();
pitch = filter.getPitch();
heading = filter.getYaw();
Serial.print("Orientation: ");
Serial.print(heading);
Serial.print(" ");
Serial.print(pitch);
Serial.print(" ");
Serial.println(roll);
}
}
Conclusion
There are dozens of accelerometers, gyrometers, and IMUs on the market, and as they become more ubiquitous in electronic devices, they continue to get smaller, cheaper, and more power-efficient. The principles laid out here should give you a basis for getting to know new ones as needed.
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Page Contents1 Introduction2 What You’ll Need to Know3 Things You’ll Need4 Orientation, Position, and Degrees of Freedom5 Features of an IMU6 Analog IMUs6.1 Analog Accelerometer Example7 Digital IMUs7.1 Digital Accelerometer Example8 Built-in IMUs8.1 Nano 33 IoT Built-In IMU Example9 What To Look For in an IMU Library10 Determining Orientation11 ConclusionFind Content by Category:Find Content by Category:
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Clinicians’ perspectives on inertial measurement units in clinical practice | PLOS ONE
Clinicians’ perspectives on inertial measurement units in clinical practice | PLOS ONE
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Open Access
Peer-reviewed
Research Article
Clinicians’ perspectives on inertial measurement units in clinical practice
François Routhier ,
Roles
Conceptualization,
Methodology,
Project administration,
Writing – original draft,
Writing – review & editing
* E-mail: francois.routhier@rea.ulaval.ca
Affiliations
Department of Rehabilitation, Université Laval, Quebec City, QC, Canada,
Center for Interdisciplinary Research in Rehabilitation and Social Integration, Centre Intégré Universitaire de Santé et de Services Sociaux de la Capitale-Nationale, Quebec City, QC, Canada
https://orcid.org/0000-0002-5458-6233
⨯
Noémie C. Duclos,
Roles
Formal analysis,
Investigation,
Writing – review & editing
Affiliations
Handicap Activity Cognition Health Team-U1219 BPH, University of Bordeaux, Bordeaux, France,
Institut Universitaire des Sciences de la Réadaptation, University of Bordeaux, Bordeaux, France
https://orcid.org/0000-0003-1579-9823
⨯
Émilie Lacroix,
Roles
Formal analysis,
Investigation,
Writing – original draft,
Writing – review & editing
Affiliation
Center for Interdisciplinary Research in Rehabilitation and Social Integration, Centre Intégré Universitaire de Santé et de Services Sociaux de la Capitale-Nationale, Quebec City, QC, Canada
⨯
Josiane Lettre,
Roles
Formal analysis,
Writing – original draft,
Writing – review & editing
Affiliation
Center for Interdisciplinary Research in Rehabilitation and Social Integration, Centre Intégré Universitaire de Santé et de Services Sociaux de la Capitale-Nationale, Quebec City, QC, Canada
⨯
Elizabeth Turcotte,
Roles
Formal analysis,
Writing – review & editing
Affiliations
Department of Rehabilitation, Université Laval, Quebec City, QC, Canada,
Center for Interdisciplinary Research in Rehabilitation and Social Integration, Centre Intégré Universitaire de Santé et de Services Sociaux de la Capitale-Nationale, Quebec City, QC, Canada
⨯
Nathalie Hamel,
Roles
Conceptualization,
Investigation,
Methodology,
Writing – review & editing
Affiliation
IntRoLab–Laboratoire de Robotique Intelligente/Interactive/Intégrée/Interdisciplinaire, Institut Interdisciplinaire d’Innovation Technologique, Université de Sherbrooke, Sherbrooke, QC, Canada
⨯
François Michaud,
Roles
Conceptualization,
Investigation,
Methodology,
Writing – review & editing
Affiliations
IntRoLab–Laboratoire de Robotique Intelligente/Interactive/Intégrée/Interdisciplinaire, Institut Interdisciplinaire d’Innovation Technologique, Université de Sherbrooke, Sherbrooke, QC, Canada,
Department of Electrical Engineering and Computer Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada
⨯
Cyril Duclos,
Roles
Conceptualization,
Methodology,
Writing – review & editing
Affiliations
School of Rehabilitation, Université de Montréal, Montreal, QC, Canada,
Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, Institut Universitaire sur la Réadaptation en Déficience Physique de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal, Montreal, QC, Canada
⨯
Philippe S. Archambault,
Roles
Conceptualization,
Methodology,
Writing – review & editing
Affiliations
School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada,
Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, Jewish Rehabilitation Hospital, Centre Intégré de Santé et de Services Sociaux de Laval, Laval, QC, Canada
⨯
Laurent J. Bouyer
Roles
Conceptualization,
Investigation,
Methodology,
Writing – review & editing
Affiliations
Department of Rehabilitation, Université Laval, Quebec City, QC, Canada,
Center for Interdisciplinary Research in Rehabilitation and Social Integration, Centre Intégré Universitaire de Santé et de Services Sociaux de la Capitale-Nationale, Quebec City, QC, Canada
https://orcid.org/0000-0003-2034-4516
⨯
Clinicians’ perspectives on inertial measurement units in clinical practice
François Routhier,
Noémie C. Duclos,
Émilie Lacroix,
Josiane Lettre,
Elizabeth Turcotte,
Nathalie Hamel,
François Michaud,
Cyril Duclos,
Philippe S. Archambault,
Laurent J. Bouyer
x
Published: November 13, 2020
https://doi.org/10.1371/journal.pone.0241922
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AbstractInertial measurement units (IMUs) have been increasingly popular in rehabilitation research. However, despite their accessibility and potential advantages, their uptake and acceptance by health professionals remain a big challenge. The development of an IMU-based clinical tool must bring together engineers, researchers and clinicians. This study is part of a developmental process with the investigation of clinicians’ perspectives about IMUs. Clinicians from four rehabilitation centers were invited to a 30-minute presentation on IMUs. Then, two one-hour focus groups were conducted with volunteer clinicians in each rehabilitation center on: 1) IMUs and their clinical usefulness, and 2) IMUs data analysis and visualization interface. Fifteen clinicians took part in the first focus groups. They expressed their thoughts on: 1) categories of variables that would be useful to measure with IMUs in clinical practice, and 2) desired characteristics of the IMUs. Twenty-three clinicians participated to the second focus groups, discussing: 1) functionalities, 2) display options, 3) clinical data reported and associated information, and 4) data collection duration. Potential influence of IMUs on clinical practice and added value were discussed in both focus groups. Clinicians expressed positive opinions about the use of IMUs, but their expectations were high before considering using IMUs in their practice.
Citation: Routhier F, Duclos NC, Lacroix É, Lettre J, Turcotte E, Hamel N, et al. (2020) Clinicians’ perspectives on inertial measurement units in clinical practice. PLoS ONE 15(11):
e0241922.
https://doi.org/10.1371/journal.pone.0241922Editor: Filomena Papa, Fondazione Ugo Bordoni, ITALYReceived: April 21, 2020; Accepted: October 23, 2020; Published: November 13, 2020Copyright: © 2020 Routhier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Data Availability: All relevant data are within the manuscript and its Supporting Information files.Funding: This research was funded in part by the strategic cluster Ingénierie de technologies interactives en réadaptation (Fonds de recherche du Québec – Nature et technologies; grant number 265 381), and by the Sentinel North program of Université Laval (Canada First Research Excellence Fund; team grant 2.8). FR is supported by a Research Scholar grant from the Fonds de recherche du Québec – Santé (grant number 34699). ND was supported by post-doc scholarships from the Fonds de recherche du Québec – Santé and the Réseau Provincial de Recherche en Adaptation-Réadaptation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Competing interests: The authors have declared that no competing interests exist.
IntroductionThe use of technology is constantly increasing in our daily lives and also in clinical settings, making patients’ assessment, therapy and follow-ups potentially easier and faster for healthcare professionals. Rehabilitation is one of the fields benefitting the most from technological development when it comes to activity assessment. A recent review of the past 10-year exercise interventions for people with disabilities reveals that approximately 20% (27/132) of interventions involved technology, ranging from Information and Communication Technologies (ICTs) to devices such as accelerometers and pedometers worn by patients [1]. Study findings demonstrate that technology can be used to conduct tele-exercise sessions with participants in the comfort of their home; deliver Web-based content to a mass audience; measure and monitor activity; and provide fun, interactive and enjoyable forms of movement (i.e., active video games) [1].
Health-related measurement systems are continuously being developed and refined, leading to new opportunities for rehabilitation, such as the possibility to measure three-dimensional movements in various contexts [2]. For instance, an inertial measurement unit (IMU) is a measurement tool combining several triaxial sensors, in most cases an accelerometer, a gyroscope and/or a magnetometer [3, 4]. Other types of sensors can also be added to IMUs to improve or complement the information provided, such as a Global Positioning System (GPS) device [5]. IMUs are small, self-contained and cost-effective devices that may allow acquisition of human movement data (e.g., body posture, upper and lower extremity movements, trunk movements) in an ecological and unconstrained environment [2, 4, 6]. Due to these appealing characteristics, researchers have shown great interest in using these devices as wearable sensors on the human body to analyse multiple daily activities (e.g., walking, running, dressing and eating) [7–10]. Recently, the development of smaller, lower cost and lower power wearable wireless IMUs has increased the possibilities for use in rehabilitation contexts [2, 7]. These technological innovations may allow clinicians to continuously monitor online objective data [11]. Thus, IMUs have the potential to facilitate goal setting and progress monitoring, including effectiveness of the treatment, as well as to support clinical decision-making [2, 11–13]. For instance, objective data collected over a significant period of time can bolster the simple quantitative data collected at limited time-points [2, 11] and information obtained through traditional subjective assessment measures with their inherent limitations related to reliability [2, 13, 14]. Furthermore, as compliance with rehabilitation treatment is often an issue, these objective data can provide patients with possibility to track their own evolution, which may enhance self-motivation and self-efficacy [2, 11].
However, despite the accessibility and potential advantages of IMUs, their uptake and acceptance by health professionals remain a big challenge [15–17]. This is certainly related to the fact that they have mostly been developed considering research and/or engineering requirements without the involvement of clinical end-users [2, 5, 11, 16, 18]. Furthermore, clinical validations of IMU-based systems are rarely conducted [2, 6]. The time required to manage the device and the gathered data is among the main difficulties identified by clinicians when it comes to the use of IMUs [2]. Another major concern is that the data needs to be processed before being clinically usable, which requires a level of technical expertise that clinicians do not necessarily possess [19]. Furthermore, each model of IMU/IMU system has its own specificities, which makes it difficult to use different models of IMUs simultaneously/interchangeably as they may not be compatible. Consequently, it can become cumbersome to combine data from different IMU systems to perform one complete analysis [19]. To facilitate adoption of IMUs by clinicians, collaboration between them and the developers is essential, as perceived ease-of-use and usefulness are key elements to promote successful adoption of such new technologies [2, 20]. Some interdisciplinary teams, such as Gait Up [21], Xsens [22], FeetMe [23] and Sysnav [24], have developed systems that are now widely used. However, very few publications describe the use of a user-centered design approach for their process development [25], or this is done only partially or superficially, and very little publications have described rehabilitation professionals’ perceptions and use of such wearable devices [2, 11, 16]. As the uptake of these types of systems depends largely on meeting the needs of end-users, an upstream consultation with clinicians to document their views and insights appears crucial. As with other technological developments in health practices, we first need to understand how end-users (clinicians) feel technology can support their practice. Without such an approach, future adoption and implementation could suffer [26].
It is in this context that our research team is working on the development of IMUs and associated interface for data visualization and analysis specifically designed to be usable and relevant in clinical settings. Given the challenges related to clinical implementation and acceptability of this technology, as well as the potential new opportunities it can provide for clinical practice, it was important for the team to involve clinicians from the get-go, i.e. as early as in the design process [11, 16, 26, 27]. Therefore, the study reported here was first conducted to guide the development of IMUs to ensure their usefulness and use in a clinical physical rehabilitation context, by: 1) exploring clinical needs and potential use of IMUs, and 2) collecting insights on the data analysis and visualization interface designed for the IMUs. To do so, a user-centered design approach was set by encouraging clinicians to highlight potential needs, refinements or issues in implementation, and to express the criteria they would require to maximise implementation and patients’ engagement. In this instance, the clinicians were the end-users the research team wanted to reach.
Materials and methods
Study design
We followed a user-centered design method to promote the active participation of key stakeholders, by emphasizing their considerations, needs, concerns and requirements [28]. Such a co-design approach is used to facilitate collaboration between developers and end-users [29]. It also encourages continuous contact with prospective users and uses an iterative process to ensure usability [28].
Focus group methodology was the preferred means of data collection for this study. Focus groups allow participants to share their views among each other on open-ended questions on a topic of common interest [30]. Therefore, focus groups are used to gain information on collective opinions and yield a rich understanding of participants' experiences and beliefs [31]. For the purpose of this study, the research team conducted two series of focus groups: 1) on IMUs and their potential clinical usefulness (objective 1), and 2) on the IMUs data analysis and visualization interface (objective 2).
Participants
To inform and recruit clinicians, the research team contacted the department heads of three rehabilitation centers in the Montreal area (Canada): 1) Institut de réadaptation Gingras-Lindsay-de-Montréal (IRGLM: Institut universitaire sur la réadaptation en déficience physique de Montréal (IURDPM), Centre intégré universitaire de santé et de services sociaux du Centre-Sud-de-l’Île-de-Montréal (CCSMTL)), 2) Centre de réadaptation Lucie-Bruneau (CRLB: IURDPM, CCSTML), and 3) Hôpital juif de réadaptation (HJR: Centre intégré de santé et de services sociaux de Laval). They were asked to invite their teams to attend a presentation on IMUs. A 30-minute presentation was given by research team members with a healthcare background in the Fall of 2017 to clinicians (n = 24), who were offered to attend either in-person or by video conferencing. The goals of the presentation were to: 1) present the research team and the approach used (user-centered design), 2) familiarize clinicians with IMU-based systems and their components for measuring three-dimensional movements, 3) familiarize clinicians with their potential uses (in the clinic, outdoors and at home), and 4) inform clinicians about the study and of its upcoming two focus groups. Clinicians were then invited to contact the research team if they were interested in taking part in one of the focus groups. Clinicians from a rehabilitation center in Quebec City (Canada), the Institut de réadaptation en déficience physique de Québec (IRDPQ: Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale (CIUSSS-CN)), had gone through a similar process in 2016, as part of a pilot project. Those who had participated in a previous group discussion on IMUs back in 2016 (i.e., the first focus group), which led to the current structured study, were contacted through email to explain the study and the goal of the second series of focus groups, to which they were invited to participate if interested, by contacting the research team back.
In total, clinicians were recruited at four clinical sites in the province of Quebec: IRGLM, CRLB, HJR and IRDPQ. In order to participate in a focus group, clinicians had to: 1) be at least 18 years old, 2) speak French, 3) work with a rehabilitation clientele, and 4) have an interest in using new technologies in their regular clinical practice. Clinicians who were eligible and who had sent an email to express their interest in participating were grouped by site.
Data collection
All clinicians who took part in the study signed a consent form approved by the local Behavioural Research Ethics Board (CIUSSS-CN #MP-13-2018-453 and RIS_EMP-2017-553). They also had to complete a questionnaire on their occupational profile, which included information on their work experience, clientele they are working with and experience with technologies in their practice, completed by sociodemographic data.
The first series of focus groups was conducted shortly after the general presentations on IMUs at each site. They were facilitated by two research team members who have a healthcare background and who were not directly involved in the technological development of the IMUs. The facilitators started the meetings by making a quick summary of the general presentation on IMUs and asked the participants if they had any questions before proceeding. The focus groups were held in a semi-guided format, following a discussion guide that included potential applications of IMUs to specific clinical problems. A prioritization exercise aimed at identifying the most significant measures for clinical practice was also attempted. S1 Appendix provides the complete discussion guide used for the first series of focus groups. The discussion guide was developed through consultation with the researchers involved in the study to ensure that they would obtain the answers required to guide IMUs’ development. One of the facilitators noted on a board the discussed content and categorized items according to the topics covered, while the other was making sure every question of the guide was covered and that all participants could voice their opinions. Each group discussion lasted about one hour and was audio recorded. It is important to note here that audio recordings from the group discussion conducted in 2016 with clinicians from the IRDPQ were not available. Therefore, no data will be included for this first focus group conducted to this site.
The second series of focus groups was about the interface that could be used for data analysis and visualization. To inform the discussion, a short-narrated video presenting a first version of the software interface was shown to the clinicians at the four sites. The video showed its features and characteristics. After collecting information about the clinicians’ first impressions, the facilitators (the same as for the first series of focus groups) proceeded with the focus group discussion. As for the first series of focus groups, the facilitators followed a discussion guide covering all the topics the developers needed for interface optimization. Clinicians shared their needs and preferences regarding data visualization, analysis, and report format, but also on the overall data presentation (i.e., beyond the interface shown). S2 Appendix provides the complete discussion guide of the second series of focus groups. The discussion guide was also developed through consultation with the researchers involved in the study. Each group discussion lasted about one hour and was audio recorded.
Data analysis
Descriptive statistics were used to characterize the group of participants (means, standard deviations, response frequencies). Audio recordings from the focus groups were transcribed verbatim in a Word document and analyzed using NVivo qualitative data analysis version 10 software (QSR International Pty Ltd). The study included a total of three verbatim transcripts on IMUs (first series of focus groups) and four on the interface (second series of focus groups), that were coded separately. Two individuals (ÉL, ET and/or ND) performed a thematic analysis [32] by repeatedly reviewing and extracting relevant text from the transcripts and categorizing data into emerging themes and subthemes. More precisely, an early coding template was created to start the data categorization. The coding template was data-driven, which is specific to an inductive approach, and therefore no theoretical framework was used for its design [33]. As the process progressed, the coding guide was adjusted and refined according to the themes and subthemes that were discussed, which is characteristic of a continuous thematic analysis. Any discrepancies were resolved via discussions, and any necessary adjustments were made. For each subtheme that emerged, occurrences, i.e. the number of times the topic was brought up by the participants, were calculated. Percentages, representing the proportion of each subthemes in relation to all the subthemes of a given theme, were also calculated.
Results
Participants
Fifteen clinicians took part in the first series of focus groups (clinicians from the IRDPQ site who took part in the 2016 pilot discussion are not taken into account) and 23 in the second one. All participants of the first series took part in the second one. Participants of the first series of focus groups had an average of 16.6±9.6 years of clinical experience. Seven of them (46.7%) had at some point used technological measurement tools in their clinical practice, and only one (6.7%) clearly reported having already used IMUs in the past. Participants of the second series of focus groups had an average of 17.4±9.7 years of clinical experience. Nine of them (39.1%) had at some point used technological measurements tools in their clinical practice, and only one (4.3%) clearly reported having already used IMUs in the past. Table 1 presents demographic and descriptive data in more details.
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PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 1. Characteristics of the focus groups’ participants.
https://doi.org/10.1371/journal.pone.0241922.t001
Categories of variables to measure and characteristics of IMUs
Relevant text from the verbatim transcripts of the first series of focus groups was categorized into two main themes: 1) categories of variables that would be useful to measure with IMUs in clinical practice, and 2) desired characteristics of the IMUs.
Categories of variables to measure with IMUs.Clinical needs that could be addressed with IMUs were varied and site dependent. Clinicians identified ten main categories of variables that would be useful to measure with IMUs in clinical practice: 1) gait (n = 24 occurrences; 24.0%), 2) posture (n = 18; 18.0%), 3) activities (n = 16; 16.0%), 4) falls and losses of balance (n = 8; 8.0%), 5) muscular activity (n = 7; 7.0%), 6) limbs orientation (n = 6; 6.0%), 7) physiological data (n = 6; 6.0%), 8) compensations (n = 5; 5.0%), 9) wheelchair use (n = 5; 5.0%), and 10) movement quality (n = 5; 5.0%). Table 2 presents responses of clinicians from each of the three sites, with some examples of items discussed for each identified variable. These examples are mainly summarized quotations or ideas, expressed by one or many participants, that illustrated each subtheme.
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PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 2. Categories of variables that would be useful to measure with IMUs in clinical practice.
https://doi.org/10.1371/journal.pone.0241922.t002
Desired characteristics of the IMUs.According to participants, a clinically usable IMU system needs to: 1) be easy to install/uninstall (n = 12 occurrences; 25.0%), 2) display some parameters/feedback when in operation (n = 10; 20.8%), 3) be easy to wear for the patient (n = 10; 20.8%), 4) be easy to use (n = 8; 16.7%), 5) be sturdy (n = 5; 10.4%), and 6) be affordable (n = 3; 6.3%). Table 3 presents clinicians’ responses from each of the three sites, with some examples of items discussed for each desired characteristic.
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PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 3. Desired characteristics of the IMUs.
https://doi.org/10.1371/journal.pone.0241922.t003
IMUs data analysis and visualization interface
Relevant text from the verbatim transcripts of the second series of focus groups was categorized into fours main themes: 1) functionalities, 2) display options, 3) clinical data and associated information, and 4) data collection duration.
Functionalities.Clinicians expressed their thought regarding the functionalities that the interface must present to meet their needs. Those most frequently mentioned were: 1) speed of results generation (n = 24 occurrences; 28.9%), and 2) flexibility of raw data transformation (n = 13; 15.7%). Table 4 presents clinicians responses from each of the four sites, with some examples of items discussed for each functionality.
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PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 4. Functionalities.
https://doi.org/10.1371/journal.pone.0241922.t004
Display options.Several display options related to data visualization were discussed by clinicians, including: 1) simultaneous visualization of several measurement times on the screen (n = 9 occurrences; 16.4%), 2) presentation of comparisons (n = 9; 16.4%), and 3) processed data presentation (n = 9; 16.4%). Table 5 presents responses of clinicians from each of the four participating rehabilitation centers, with some examples of items discussed for each display options.
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PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 5. Display options.
https://doi.org/10.1371/journal.pone.0241922.t005
Clinical data reported and associated information.Participants identified several clinical data and some associated information they would like to visualize in the report generated using the interface. Many of them mentioned pre-post results (n = 19 occurrences; 15.3%), gait/walking pattern (n = 16; 12.9%), filtered data (n = 13; 10.5%), normative data (n = 3; 10.5%), and joint angle measurements (n = 13; 10.5%). Table 6 presents responses of clinicians from each of the four sites.
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PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 6. Clinical data and associated information.
https://doi.org/10.1371/journal.pone.0241922.t006
Data collection duration.Respones related to data collection duration wanted with IMUs ranged from a few minutes to one week. Table 7 presents responses of clinicians from each of the four sites.
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PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 7. Data collection duration.
https://doi.org/10.1371/journal.pone.0241922.t007
Influence of IMUs on clinical practice and added value
The potential usefulness and influence of IMUs on clinical practice was addressed by clinicians in both series of focus groups. They discussed two main aspects. First, this technology could allow them to establish a quantitative profile of activities performed outside of the care/rehabilitation sessions (e.g., use of limb(s) on the affected side by hemiplegic patients, community/home walking pattern, falls or loss of balance, number of occurrences of a non-recommended movement, return to work following an injury). Second, such systems could allow them to gather quantitative data related to variables that are normally difficult to assess due to their complexity (e.g., gait). According to the participants, this would be useful at follow-up, to discuss progress and compliance with patients. It could also provide patients with motivation and assist self-management.
Clinicians also mentioned several criteria that would be added values to the system and its interface so that they would be more likely to use it. Among these, they mentioned ease of use (n = 7 occurrences; 21.2%), short analysis time (n = 4; 12.1%) and system validation (n = 3; 9.1%). Table 8 presents responses of clinicians from each of the four sites.
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PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 8. Added value.
https://doi.org/10.1371/journal.pone.0241922.t008
DiscussionThis study explored clinical needs and potential use of IMU-based systems in clinical practice, and collected feedback on the data analysis and visualization interface designed by our team for the IMUs. Regarding the use of IMUs, clinicians expressed an interest, had a good understanding of the broad spectrum of possible uses, and articulated needs that were diverse and based on specific measures. However, their expectations in terms of characteristics of IMUs, data processing and data visualization were very high before considering using IMUs in their clinical practice. Our results showed that they wanted a reliable and valid system, in which they could have confidence, as also suggested by Schall et al. [34]. This finding is perfectly legitimate. The system must bring something more than the tools or systems they already use, while still being easy to use. It must also generate results and reports that are visually easy to interpret. In both series of focus groups, clinicians mentioned many aspects related to categories of variables to measure with IMUs, characteristics of IMUs, data analysis and visualization interface to ensure clinical usefulness of IMUs and their added value. This is in accordance with the Technology Acceptance Model, which states that the level of acceptability of technologies depends mainly on the users’ perceived ease of use and perceived usefulness [20].
Despite a wide variety of needs expressed, measure and analysis of gait were largely discussed by many of the clinicians who took part in this study. They argued that this is a very complex variable that involves a multitude of related sub-variables (e.g., weight-bearing, multi-segment coordination, joint angles excursion, symmetry). As they normally based their assessment on simple quantitative data (e.g., walking distance, walking speed, static muscular strength) and on qualitative/subjective observations (e.g., movement quality), they clearly stated that IMUs could be useful to enrich this variable. Interestingly, a recent study conducted by Bernhard et al. [35] showed the feasibility of assessing gait and balance features with IMUs in clinical settings for neurological patients. If properly implemented, clinicians from the current study saw also the IMUs’ potential to monitor objectively activities performed outside of the care/rehabilitation settings. Indeed, current clinical practice offers few opportunities in this regard [11]. Similarly, they discussed their potential to document remotely objective outcome measures and to promote compliance to treatment and self-motivation, which have been shown to be possible with IMUs [36, 37]. This is in accordance with the literature that documented perspectives of health professionals on wearable technologies and their added value for assessment and intervention [2, 11, 38].
We believe that the development of IMUs that meet needs clearly expressed by clinicians will provide them with new opportunities in their clinical practice. Like several other studies, our work highlights the importance of a user-centered design approach and of a close collaboration between engineers, researchers and end-users to promote IMUs acceptance [11, 16, 26, 27, 39, 40]. Developers cannot assume what the clinicians’ needs and preferences are [28, 29]. Furthermore, considering and addressing barriers, facilitators and concerns of clinicians and of clinical settings (e.g., time, financial and material resources) is warranted [34]. This study identified several important aspects and features to promote the appropriation of IMUs by clinicians, which may inform the development of future IMU-based technologies for rehabilitation. As the actual end-users of such systems were consulted in an upstream process, this study is an added value to the existing literature. Indeed, studies exploring clinicians’ perspectives on IMUs and their visualization interface prior to their development are missing.
As part of the first series of focus groups, a prioritization exercise of the variables that would be most useful to measure with IMUs in clinical practice was attempted. Indeed, responses were very different depending on the rehabilitation center, the clinical program and the participants’ experience and expertise. As relevant technologies continue to improve, and considering the increased possibilities for use in rehabilitation contexts, it is important to deepen the discussion over variables that are most significant for clinical practice, including a prioritization exercise. In the near future, an online survey will be developed on the basis of the results of this study to obtain the opinion of a larger number of clinicians and clinical settings. Such an identification and prioritization process will provide insights to inform IMU systems design that will correspond to health professionals’ expectations and needs, and could therefore contribute to promote better uptake and acceptance. This will be the subject of a separate paper. A mixed methods approach is recommended when conducting a study that focuses on a multifaceted or complex phenomenon, as in the case of a user-centered design [41]. Meaningful integration of qualitative and quantitative techniques when performing user-centred design allows to collect rich and comprehensive data, and to reflect more accurately end-users’ perspectives [42]. The data can be compared and combined, thus providing a more complete overview and understanding of their needs and priorities [42, 43]. The survey results will therefore complement the data collected through the focus groups. It will also be an opportunity for clinicians, researchers and developers to interact with each other, and to conduct a participatory research project [44]. Assessing IMUs’ usability with clinicians and rehabilitation patients, two end-user groups with different needs, with a prototype that meets most of their needs and includes a basic interface is a logical next step to explore key aspects of technology uptake such as efficiency, effectiveness and satisfaction [45]. Real-world validation, which is often missing, needs also to be performed [4].
This study is not without some limitations. Time constraints for focus groups (i.e., maximum 60 minutes per focus group) made it difficult to deepen the discussion regarding implementation and confidence in use of the technology as well as the rationale behind the participants’ answers, and to prioritize the characteristics that were brought up. Although it was decided not to present an IMU system prototype to clinicians prior to the focus groups in order not to bias their thinking, this choice made it harder for them to visualize the use of the developed technology (i.e., how and in which context). In contrast, showing them a short-narrated video presenting a first version of an interface may have biased or influenced their thoughts and comments. Presentations before both rounds of focus groups may have limited participants’ perspectives, but were necessary for those who were unfamiliar with IMUs and for all to have the same understanding. Because of the inclusion criteria, the rehabilitation professionals who took part in the study had an interest in using new technologies in their regular clinical practice. This might explain why we did not get any response that IMUs were perceived as useless. Due to specificities of clinicians involved in this study (e.g., influence of the rehabilitation center, clinical program and background), we obtained individual perceptions: a very limited number of participants were sharing the same vision. Part of this can be attributed to the small sample size. Therefore, our results cannot be directly generalized to other practice settings. In addition, some health professionals were not represented, such as physiatrists, while some health professionals were over-represented, such as physical therapists. However, in Canada, clinicians who perform assessments related to movement analysis, including gait analysis, are mostly physical therapists. Also, the clinicians all came from institutions with similar practices and cultures, thus supporting the need for a large survey designed for a greater number of health professionals working in different contexts. Finally, rehabilitation patients were not included in this study. We consider that they could offer a unique perspective that could be the subject of a separate study.
ConclusionsDespite the accessibility and potential advantages of IMUs, their uptake and acceptance by clinicians remain a big challenge. Thus, through a user-centered design approach and a focus group methodology, we conducted a study to help guide the development process of IMUs to ensure their usefulness and use in the clinical context. We gathered clinicians’ views on categories of variables that would be useful to measure with IMUs in clinical practice, characteristics they should present, and their potential influence on clinical practice. We also collected information on data analysis and visualization interface needs (i.e., functionalities, display options, clinical data reported, data collection duration). Clinicians expressed positive opinions regarding the clinical use of IMUs, but their expectations were high before considering using IMUs in their practice. The lack of consensus around prioritization of development indicates that more interdisciplinary concertations should be conducted. Our hope is that the results from this study will improve IMU systems development and increase the functionality and applicability of IMUs in clinical contexts, as these systems have the potential to facilitate goal setting and progress monitoring, to support clinicians’ decision-making and to provide patients with the motivation to achieve clinical goals.
Supporting informationS1 Appendix. Discussion guide of the first series of focus groups.https://doi.org/10.1371/journal.pone.0241922.s001(DOCX)
S2 Appendix. Discussion guide of the second series of focus groups.https://doi.org/10.1371/journal.pone.0241922.s002(DOCX)
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Access DeniA comparative evaluation of low-cost IMUs for unmanned autonomous systems | IEEE Conference Publication | IEEE Xplore
A comparative evaluation of low-cost IMUs for unmanned autonomous systems | IEEE Conference Publication | IEEE Xplore
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