Literature DB >> 28269407

Real-time gait event detection for lower limb amputees using a single wearable sensor.

H F Maqbool, M A B Husman, M I Awad, A Abouhossein, P Mehryar, N Iqbal, A A Dehghani-Sanij.   

Abstract

This paper presents a rule-based real-time gait event/phase detection system (R-GEDS) using a shank mounted inertial measurement unit (IMU) for lower limb amputees during the level ground walking. Development of the algorithm is based on the shank angular velocity in the sagittal plane and linear acceleration signal in the shank longitudinal direction. System performance was evaluated with four control subjects (CS) and one transfemoral amputee (TFA) and the results were validated with four FlexiForce footswitches (FSW). The results showed a data latency for initial contact (IC) and toe off (TO) within a range of ± 40 ms for both CS and TFA. A delay of about 3.7 ± 62 ms for a foot-flat start (FFS) and an early detection of -9.4 ± 66 ms for heel-off (HO) was found for CS. Prosthetic side showed an early detection of -105 ± 95 ms for FFS whereas intact side showed a delay of 141 ±73 ms for HO. The difference in the kinematics of the TFA and CS is one of the potential reasons for high variations in the time difference. Overall, detection accuracy was 99.78% for all the events in both groups. Based on the validated results, the proposed system can be used to accurately detect the temporal gait events in real-time that leads to the detection of gait phase system and therefore, can be utilized in gait analysis applications and the control of lower limb prostheses.

Entities:  

Mesh:

Year:  2016        PMID: 28269407     DOI: 10.1109/EMBC.2016.7591866

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  6 in total

1.  A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts.

Authors:  Robbin Romijnders; Elke Warmerdam; Clint Hansen; Gerhard Schmidt; Walter Maetzler
Journal:  Sensors (Basel)       Date:  2022-05-19       Impact factor: 3.847

2.  Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson's Disease patients.

Authors:  Robbin Romijnders; Elke Warmerdam; Clint Hansen; Julius Welzel; Gerhard Schmidt; Walter Maetzler
Journal:  J Neuroeng Rehabil       Date:  2021-02-06       Impact factor: 4.262

3.  Fusion of Bilateral Lower-Limb Neuromechanical Signals Improves Prediction of Locomotor Activities.

Authors:  Blair Hu; Elliott Rouse; Levi Hargrove
Journal:  Front Robot AI       Date:  2018-06-26

4.  Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals.

Authors:  Blair Hu; Elliott Rouse; Levi Hargrove
Journal:  Front Robot AI       Date:  2018-02-19

Review 5.  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

6.  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

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.