Literature DB >> 29993444

Inertial Sensing for Gait Event Detection and Transfemoral Prosthesis Control Strategy.

Elissa D Ledoux.   

Abstract

OBJECTIVE: This paper presents a method for walking gait event detection using a single inertial measurement unit (IMU) mounted on the shank.
METHODS: Experiments were conducted to detect heel strike (HS) and toe off (TO) gait events of 10 healthy subjects and 5 transfemoral amputees walking at various speeds and slopes on an instrumented treadmill. The performance of three different algorithms [thresholding (THR), linear discriminant analysis, and quadratic discriminant analysis] was evaluated on both timing and frequency of gait event detections compared to data collected using force plates.
RESULTS: Though all algorithms could be used reliably (within 8.2% stride temporal error and 0.2% frequency error), THR was the most accurate, detecting 100% of gait events within an average of 2% stride for both the healthy subjects and the amputees. Furthermore, universal parameters could be used across all speeds and slopes within each demographic.
CONCLUSION: HS and TO for walking gait can be reliably detected in healthy and transfemoral amputee subjects using a single IMU. SIGNIFICANCE: This work provides a robust, simple, and inexpensive method of gait event detection that does not rely on a load cell and could be easily implemented in a lower-limb prosthesis.

Entities:  

Mesh:

Year:  2018        PMID: 29993444     DOI: 10.1109/TBME.2018.2813999

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  8 in total

1.  Gait event detection using inertial measurement units in people with transfemoral amputation: a comparative study.

Authors:  Emeline Simonetti; Coralie Villa; Joseph Bascou; Giuseppe Vannozzi; Elena Bergamini; Hélène Pillet
Journal:  Med Biol Eng Comput       Date:  2019-12-23       Impact factor: 2.602

2.  Real-Time Gait Phase Detection Using Wearable Sensors for Transtibial Prosthesis Based on a kNN Algorithm.

Authors:  Atcharawan Rattanasak; Peerapong Uthansakul; Monthippa Uthansakul; Talit Jumphoo; Khomdet Phapatanaburi; Bura Sindhupakorn; Supakit Rooppakhun
Journal:  Sensors (Basel)       Date:  2022-06-02       Impact factor: 3.847

3.  Estimation of gait parameters using leg velocity for amputee population.

Authors:  Zohaib Aftab; Rizwan Shad
Journal:  PLoS One       Date:  2022-05-13       Impact factor: 3.752

Review 4.  A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses.

Authors:  Huong Thi Thu Vu; Dianbiao Dong; Hoang-Long Cao; Tom Verstraten; Dirk Lefeber; Bram Vanderborght; Joost Geeroms
Journal:  Sensors (Basel)       Date:  2020-07-17       Impact factor: 3.576

5.  Detecting Toe-Off Events Utilizing a Vision-Based Method.

Authors:  Yunqi Tang; Zhuorong Li; Huawei Tian; Jianwei Ding; Bingxian Lin
Journal:  Entropy (Basel)       Date:  2019-03-27       Impact factor: 2.524

Review 6.  Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait.

Authors:  Ratan Das; Sudip Paul; Gajendra Kumar Mourya; Neelesh Kumar; Masaraf Hussain
Journal:  Front Neurosci       Date:  2022-04-15       Impact factor: 5.152

7.  ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses.

Authors:  Huong Thi Thu Vu; Felipe Gomez; Pierre Cherelle; Dirk Lefeber; Ann Nowé; Bram Vanderborght
Journal:  Sensors (Basel)       Date:  2018-07-23       Impact factor: 3.576

8.  Design and Speed-Adaptive Control of a Powered Geared Five-Bar Prosthetic Knee Using BP Neural Network Gait Recognition.

Authors:  Yuanxi Sun; Rui Huang; Jia Zheng; Dianbiao Dong; Xiaohong Chen; Long Bai; Wenjie Ge
Journal:  Sensors (Basel)       Date:  2019-10-27       Impact factor: 3.576

  8 in total

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