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Multivariate CNN Model for Human Locomotion Activity Recognition with a Wearable Exoskeleton Robot

Title
Multivariate CNN Model for Human Locomotion Activity Recognition with a Wearable Exoskeleton Robot
Author(s)
Son, Chang-SikKang, Won-Seok
Issued Date
2023-09
Citation
Bioengineering, v.10, no.9, pp.1082
Type
Article
Author Keywords
human activity recognitionhyperparameter optimizationmulti-head CNNsingle-head CNNtime series classificationwearable robot
ISSN
2306-5354
Abstract
This study introduces a novel convolutional neural network (CNN) architecture, encompassing both single and multi-head designs, developed to identify a user’s locomotion activity while using a wearable lower limb robot. Our research involved 500 healthy adult participants in an activities of daily living (ADL) space, conducted from 1 September to 30 November 2022. We collected prospective data to identify five locomotion activities (level ground walking, stair ascent/descent, and ramp ascent/descent) across three terrains: flat ground, staircase, and ramp. To evaluate the predictive capabilities of the proposed CNN architectures, we compared its performance with three other models: one CNN and two hybrid models (CNN-LSTM and LSTM-CNN). Experiments were conducted using multivariate signals of various types obtained from electromyograms (EMGs) and the wearable robot. Our results reveal that the deeper CNN architecture significantly surpasses the performance of the three competing models. The proposed model, leveraging encoder data such as hip angles and velocities, along with postural signals such as roll, pitch, and yaw from the wearable lower limb robot, achieved superior performance with an inference speed of 1.14 s. Specifically, the F-measure performance of the proposed model reached 96.17%, compared to 90.68% for DDLMI, 94.41% for DeepConvLSTM, and 95.57% for LSTM-CNN, respectively. © 2023 by the authors.
URI
http://hdl.handle.net/20.500.11750/47524
DOI
10.3390/bioengineering10091082
Publisher
MDPI AG
Related Researcher
  • 강원석 Kang, Won-Seok
  • Research Interests Digital Phenotyping; Data Mining & Machine Learning for Text & Multimedia; Brain-Sense-ICTConvergence Computing; Computational Olfaction Measurement; Simulation&Modeling
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Appears in Collections:
Division of Intelligent Robotics 1. Journal Articles

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