Xu Kaiyue, Yu Wenqiang, Yu Shui, Zheng Minghui, Zhang Hao
College of Mechanical Engineering, Shandong Huayu University of Technology, Dezhou 253034, China.
College of Information Engineering, Dalian University, Dalian 116622, China.
Bioengineering (Basel). 2025 May 4;12(5):491. doi: 10.3390/bioengineering12050491.
This study aims to detect gait events using a smartphone combined with deep learning and evaluate the remote effects and clinical significance of this method in different elderly populations and patients with cerebral small vessel disease (CSVD). In total, 150 healthy individuals aged 20-70 years were asked to attach a smartphone to their thighs and walk six gait cycles at self-selected low, normal, and high speeds, using an insole pressure sensor as the reference standard for gait events. A deep learning model was then established using BiTCN-BiGRU-CrossAttention, and two models (TCN-GRU and BiTCN-BiGRU) were compared. In total, 48 elderly (25 healthy, 12 with mild cognitive impairment, 11 with Parkinson's disease) participated in an online home assessment, completing single-task and cognitive dual-task walking. Overall, 35 CSVD patients participated in an offline clinical assessment, completing single-task, cognitive dual-task, and physical dual-task walking. The BiTCN-BiGRU-CrossAttention model had the lowest MAE for detecting gait events compared to the other models. All models had lower MAEs for detecting heel strikes than toe-offs, and the MAE for low and high walking was higher than for normal speed walking. There were significant differences ( < 0.05) in gait parameters (Cadence, Stride time, Stance phase, Swing phase, Stance time, Swing time, Stride length, and walking speed) between single-task and cognitive dual-task walking for all online elderly participants. CSVD patients showed significant differences ( < 0.05) in gait parameters (Cadence, Stride time, Stance phase, Swing phase, Stance time, Stride length, and walking speed) between single-task and cognitive dual-task and between single-task and physical dual-task walking.
本研究旨在利用智能手机结合深度学习来检测步态事件，并评估该方法在不同老年人群和脑小血管疾病（CSVD）患者中的远程效应及临床意义。总共招募了150名年龄在20至70岁之间的健康个体，要求他们将智能手机绑在大腿上，并以自行选择的低速、正常速度和高速行走六个步态周期，使用鞋垫压力传感器作为步态事件的参考标准。然后使用BiTCN - BiGRU - CrossAttention建立深度学习模型，并比较了两个模型（TCN - GRU和BiTCN - BiGRU）。共有48名老年人（25名健康者、12名轻度认知障碍者、11名帕金森病患者）参与了在线居家评估，完成了单任务和认知双任务行走。总体而言，35名CSVD患者参与了线下临床评估，完成了单任务、认知双任务和身体双任务行走。与其他模型相比，BiTCN - BiGRU - CrossAttention模型在检测步态事件时的平均绝对误差（MAE）最低。所有模型检测足跟触地的MAE均低于检测足趾离地的MAE，且低速和高速行走时的MAE高于正常速度行走时的MAE。对于所有在线老年参与者，单任务和认知双任务行走之间的步态参数（步频、步幅时间、支撑相、摆动相、支撑时间、摆动时间、步幅长度和行走速度）存在显著差异（<0.05）。CSVD患者在单任务与认知双任务以及单任务与身体双任务行走之间的步态参数（步频、步幅时间、支撑相、摆动相、支撑时间、步幅长度和行走速度）存在显著差异（<0.05）。
