Locomotion Decoding (LocoD) An Open-Source Modular Platform for Researching Control of Lower Limb Assistive Devices.
Journal article, 2023
Methods: In the absence of shared tools to record and process lower limb bioelectric signals, such as electromyography (EMG), we developed an open-source software platform to unify the recording and processing (pre-processing, feature extraction, and classification) of EMG and non-biological signals amongst researchers with the goal of investigating and benchmarking control algorithms. We validated our locomotion decoding (LocoD) software by comparing the accuracy in the classification of locomotion mode using three different combinations of sensors (1 = IMU+EMG, 2 = EMG, 3 = IMU). EMG and non-biological signals (from the IMU and pressure sensor) were recorded while able-bodied participants (n = 21) walked on different surfaces such as stairs and ramps, and this data set is also released publicly along this publication. LocoD was used for all recording, pre-processing, feature extraction, and classification of the recorded signals. We tested the statistical hypothesis that there was a difference in predicted locomotion mode accuracy between sensor combinations using the Wilcoxon signed-rank test.
Results: We found that the sensor combination 1 (EMG+IMU) led to significantly more accurate and improved locomotion mode prediction (Accuracy=93.4 ± 3.9) than using EMG (Accuracy= 74.56 ± 5.8) or IMU alone (Accuracy=90.77 ± 4.6) with p-value < 0.001.
Conclusions: Our results support previous research and validate the functionality of LocoD as an open-source and modular platform to research control algorithms for prosthetic legs that incorporate bioelectric signals.
Opensource Software
Prostheses
Lower Limb Prosthetic Control
Biomedical Signal Processing
Electromyogram
Author
Bahareh Ahkami
Center for Bionics and Pain Research
Chalmers, Electrical Engineering, Systems and control
Kirstin Ahmed
Chalmers, Electrical Engineering, Systems and control
Center for Bionics and Pain Research
University of Gothenburg
Morten Kristoffersen
University of Gothenburg
Chalmers, Electrical Engineering, Systems and control
Center for Bionics and Pain Research
Max Jair Ortiz Catalan
Bionics Institute
Chalmers, Electrical Engineering, Systems and control
Sahlgrenska University Hospital
Center for Bionics and Pain Research
Computer Methods and Programs in Biomedicine
0169-2607 (ISSN) 18727565 (eISSN)
Subject Categories
Electrical Engineering, Electronic Engineering, Information Engineering
Computer Science
DOI
10.2139/ssrn.4575926