An Open-Source and Wearable System for Measuring 3D Human Motion in Real-Time.

Patrick Slade,Jennifer L Hicks,Scott L Delp,Ayman Habib

doi:10.1109/tbme.2021.3103201

Patrick Slade, Jennifer L Hicks + Show 2 more

Open Access

https://doi.org/10.1109/tbme.2021.3103201

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Abstract

Objective:Analyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation for conditions like osteoarthritis, stroke, and Parkinson′s disease. Optical motion capture systems are the standard for estimating kinematics, but the equipment is expensive and requires a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture. Many wearable sensor systems require a computer in close proximity and use proprietary software, limiting experimental reproducibility.Methods:Here, we present OpenSenseRT, an open-source and wearable system that estimates upper and lower extremity kinematics in real time by using inertial measurement units and a portable microcontroller.Results:We compared the OpenSenseRT system to optical motion capture and found an average RMSE of 4.4 degrees across 5 lower-limb joint angles during three minutes of walking and an average RMSE of 5.6 degrees across 8 upper extremity joint angles during a Fugl-Meyer task. The open-source software and hardware are scalable, tracking 1 to 14 body segments, with one sensor per segment. A musculoskeletal model and inverse kinematics solver estimate Kinematics in real-time. The computation frequency depends on the number of tracked segments, but is sufficient for real-time measurement for many tasks of interest; for example, the system can track 7 segments at 30 Hz in real-time. The system uses off-the-shelf parts costing approximately $100 USD plus $20 for each tracked segment.Significance:The OpenSenseRT system is validated against optical motion capture, low-cost, and simple to replicate, enabling movement analysis in clinics, homes, and free-living settings.

Highlights

Researchers and clinicians measure kinematics during human movement to attain an understanding of diseases and rehabilitation, to improve the design of assistive devices, or to uncover the mechanisms by which athletes achieve peak performance
The OpenSenseRT system estimated lower-limb joint kinematics with an average root-mean-square error (RMSE) of 4.4 degrees compared to kinematics computed from optical motion capture data
Joint kinematic curves from the OpenSenseRT system were compared to optical motion capture for both walking (Fig. 3A) and running (Fig. 3B)

Summary

Introduction

Researchers and clinicians measure kinematics during human movement to attain an understanding of diseases and rehabilitation, to improve the design of assistive devices, or to uncover the mechanisms by which athletes achieve peak performance. Estimating kinematics in real-time is important for many real-world applications of movement analysis, such as rapid biomechanics assessment for injury prevention, corrective feedback for at-home rehabilitation, or realtime control of assistive devices to improve mobility. Motion capture systems use sensors to objectively measure the motion of body segments and estimate joint angles and other kinematic quantities. Motion capture systems typically rely on either stationary sensors such as cameras placed throughout a room or wearable sensors such as inertial measurement units (IMUs) attached to the.

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