Son Chang-Sik, Kang Won-Seok
Division of Intelligent Robot, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu 42988, Republic of Korea.
Department of Biomedical Science, Graduate School, Kyungpook National University, Daegu 41944, Republic of Korea.
Bioengineering (Basel). 2023 Sep 13;10(9):1082. doi: 10.3390/bioengineering10091082.
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.
本研究介绍了一种新颖的卷积神经网络（CNN）架构，包括单头和多头设计，旨在在用户使用可穿戴下肢机器人时识别其运动活动。我们的研究在2022年9月1日至11月30日期间，在日常生活活动（ADL）空间中纳入了500名健康成年参与者。我们收集前瞻性数据，以识别三种地形（平地、楼梯和斜坡）上的五种运动活动（平地行走、上楼梯/下楼梯以及上斜坡/下斜坡）。为了评估所提出的CNN架构的预测能力，我们将其性能与其他三种模型进行了比较：一种CNN模型和两种混合模型（CNN-LSTM和LSTM-CNN）。实验使用从肌电图（EMG）和可穿戴机器人获得的各种类型的多变量信号进行。我们的结果表明，更深层次的CNN架构显著超越了三种竞争模型的性能。所提出的模型利用来自可穿戴下肢机器人的诸如髋关节角度和速度等编码器数据，以及诸如横滚、俯仰和偏航等姿势信号，以1.14秒的推理速度实现了卓越的性能。具体而言，所提出模型的F值性能达到了96.17%，而DDLMI为90.68%，DeepConvLSTM为94.41%，LSTM-CNN为95.57%。
