A Compact Low Cost Wearable Sensor System for Quantitative Gait Measurement

2014 ◽  
Vol 627 ◽  
pp. 212-216
Author(s):  
Ming Gui Tan ◽  
Cheng Boon Leong ◽  
Jee Hou Ho ◽  
Hui Ting Goh ◽  
Hoon Kiat Ng
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Vision System ◽  
Low Cost ◽  
Sensor System ◽  
Wearable Sensor ◽  
Gait Parameters ◽  
Emg Electrodes ◽  
3D Motion Capture ◽  
Quantitative Gait Analysis

The demand for quantitative gait analysis increases due to increasing number of neurological disorder patients. Conventional gait analysis tools such as 3D motion capture systemsare relatively expensive. Therefore, there is a need to develop a low cost sensor system to obtain the spatial temporal gait parameters without compromising too much on the accuracy. This paper describesthe development of a wearable low cost sensor system which consists ofrelatively less sensing elements with 2 accelerometers, 4 force sensitive resistors (FSR) and 2 EMG electrodes. Thesensor output was validated by a vision system and the relative error was less than 5% formost of the gait parameters measured.

Rehabilitation Assistance Systems for Three-Dimensional Gait Analysis Using Motion Capture Devices

2020 ◽  
Vol 38 ◽  
pp. 209-214
Author(s):  
Yasushi Yuminaka ◽  
Motoaki Fujii ◽  
Syogo Nakazato ◽  
Setsuki Tsukagoshi ◽  
Yoshio Ikeda ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Low Cost ◽  
Body Movement ◽  
Three Dimensional ◽  
Motor Disorders ◽  
Markerless Motion Capture ◽  
Assistance Systems ◽  
3D Motion Capture ◽  
Medical And Healthcare

Patients with Parkinson’s disease or stroke show symptoms of motor disorders that disturb gait and mobility. Although the objective and/or quantitative assessment of the rehabilitation to evaluate the degree of improvement is significantly important, three-dimensional (3D) motion capture systems to evaluate body movement are very expensive and require many markers attached to patients. The purpose of this study was to investigate the feasibility of medical and healthcare ICT-supported rehabilitation assistance systems for 3D gait analysis using low-cost markerless motion capture devices in response to practical clinical needs. The clinical data obtained by our system showed that there were significant differences between the patient group and the healthy subject group.

Development of a wearable sensor system for quantitative gait analysis

Measurement ◽  
2009 ◽  
Vol 42 (7) ◽  
pp. 978-988 ◽  
Author(s):  
Tao Liu ◽  
Yoshio Inoue ◽  
Kyoko Shibata
Keyword(s):  
Gait Analysis ◽  
Sensor System ◽  
Wearable Sensor ◽  
Quantitative Gait Analysis

613 Development of a Wearable Sensor System for Gait Analysis

2006 ◽  
Vol 2006.6 (0) ◽  
pp. 23-24
Author(s):  
Tao LIU ◽  
Yoshio INOUE ◽  
Kyoko SHIBATA
Keyword(s):  
Gait Analysis ◽  
Sensor System ◽  
Wearable Sensor

Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis

2014 ◽  
Vol 38 (5) ◽  
pp. 274-280 ◽  
Author(s):  
Alexandra Pfister ◽  
Alexandre M. West ◽  
Shaw Bronner ◽  
Jack Adam Noah
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Microsoft Kinect ◽  
3D Motion ◽  
3D Motion Capture

An Integrated Calibration Technique for Multi-Camera Vision Systems

2010 ◽  
Author(s):  
Anup M. Vader ◽  
Abhinav Chadda ◽  
Wenjuan Zhu ◽  
Ming C. Leu ◽  
Xiaoqing F. Liu ◽  
...  
Keyword(s):  
Motion Capture ◽  
System Development ◽  
Vision System ◽  
Computer Software ◽  
Vision Systems ◽  
Calibration Technique ◽  
Extrinsic Parameters ◽  
Integrated Technique ◽  
Calibration Techniques ◽  
3D Motion Capture

This paper presents the integration and evaluation of two popular camera calibration techniques for multi-camera vision system development for motion capture. An integrated calibration technique for multi-camera vision systems has been developed. To demonstrate and evaluate this calibration technique, multiple Wii Remotes (Wiimotes) from Nintendo were used to form a vision system to perform 3D motion capture in real time. This integrated technique is a two-step process: it first calibrates the intrinsic parameters of each camera using Zhang’s algorithm [5] and then calibrates the extrinsic parameters of the cameras together using Svoboda’s algorithm [9]. Computer software has been developed for implementation of the integrated technique, and experiments carried out using this technique to perform motion capture with Wiimotes show a significant improvement in the measurement accuracy over the existing calibration techniques.

scholarly journals Two-dimensional video-based analysis of human gait using pose estimation

2020 ◽  
Author(s):  
Jan Stenum ◽  
Cristina Rossi ◽  
Ryan T. Roemmich
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Pose Estimation ◽  
Gold Standard ◽  
Three Dimensional ◽  
Human Gait ◽  
Large Set ◽  
Two Dimensional ◽  
Gait Parameters ◽  
Human Gait Analysis

ABSTRACTWalking is the primary mode of human locomotion. Accordingly, people have been interested in studying human gait since at least the fourth century BC. Human gait analysis is now common in many fields of clinical and basic research, but gold standard approaches – e.g., three-dimensional motion capture, instrumented mats or footwear, and wearables – are often expensive, immobile, data-limited, and/or require specialized equipment or expertise for operation. Recent advances in video-based pose estimation have suggested exciting potential for analyzing human gait using only two-dimensional video inputs collected from readily accessible devices (e.g., smartphones, tablets). However, we currently lack: 1) data about the accuracy of video-based pose estimation approaches for human gait analysis relative to gold standard measurement techniques and 2) an available workflow for performing human gait analysis via video-based pose estimation. In this study, we compared a large set of spatiotemporal and sagittal kinematic gait parameters as measured by OpenPose (a freely available algorithm for video-based human pose estimation) and three-dimensional motion capture from trials where healthy adults walked overground. We found that OpenPose performed well in estimating many gait parameters (e.g., step time, step length, sagittal hip and knee angles) while some (e.g., double support time, sagittal ankle angles) were less accurate. We observed that mean values for individual participants – as are often of primary interest in clinical settings – were more accurate than individual step-by-step measurements. We also provide a workflow for users to perform their own gait analyses and offer suggestions and considerations for future approaches.

scholarly journals Two-dimensional video-based analysis of human gait using pose estimation

2021 ◽  
Vol 17 (4) ◽  
pp. e1008935
Author(s):  
Jan Stenum ◽  
Cristina Rossi ◽  
Ryan T. Roemmich
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Pose Estimation ◽  
Mean Absolute Error ◽  
Three Dimensional ◽  
Absolute Error ◽  
Human Gait ◽  
Two Dimensional ◽  
Gait Parameters ◽  
Human Gait Analysis

Human gait analysis is often conducted in clinical and basic research, but many common approaches (e.g., three-dimensional motion capture, wearables) are expensive, immobile, data-limited, and require expertise. Recent advances in video-based pose estimation suggest potential for gait analysis using two-dimensional video collected from readily accessible devices (e.g., smartphones). To date, several studies have extracted features of human gait using markerless pose estimation. However, we currently lack evaluation of video-based approaches using a dataset of human gait for a wide range of gait parameters on a stride-by-stride basis and a workflow for performing gait analysis from video. Here, we compared spatiotemporal and sagittal kinematic gait parameters measured with OpenPose (open-source video-based human pose estimation) against simultaneously recorded three-dimensional motion capture from overground walking of healthy adults. When assessing all individual steps in the walking bouts, we observed mean absolute errors between motion capture and OpenPose of 0.02 s for temporal gait parameters (i.e., step time, stance time, swing time and double support time) and 0.049 m for step lengths. Accuracy improved when spatiotemporal gait parameters were calculated as individual participant mean values: mean absolute error was 0.01 s for temporal gait parameters and 0.018 m for step lengths. The greatest difference in gait speed between motion capture and OpenPose was less than 0.10 m s−1. Mean absolute error of sagittal plane hip, knee and ankle angles between motion capture and OpenPose were 4.0°, 5.6° and 7.4°. Our analysis workflow is freely available, involves minimal user input, and does not require prior gait analysis expertise. Finally, we offer suggestions and considerations for future applications of pose estimation for human gait analysis.

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