The Detection of Gait Events Based on Smartphones and Deep Learning.

Kaiyue Xu, Wenqiang Yu, Shui Yu, Minghui Zheng, Hao Zhang

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Kaiyue XuCollege of Mechanical Engineering, Shandong Huayu University of Technology, Dezhou 253034, China.

Wenqiang YuCollege of Mechanical Engineering, Shandong Huayu University of Technology, Dezhou 253034, China.ORCID 0009-0005-9591-1249

Shui YuSchool of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.ORCID 0009-0009-0224-087X

Minghui ZhengCollege of Mechanical Engineering, Shandong Huayu University of Technology, Dezhou 253034, China.

Hao ZhangCollege of Information Engineering, Dalian University, Dalian 116622, China.ORCID 0009-0000-0665-7511

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 (

Indexed as

deep learninggait analysisgait eventmobile healthsmartphone

Identifiers

PMID40428110

DOI10.3390/bioengineering12050491

PMCPMC12109446

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.