Evidence map›Paper›PMID 42147486›Full record

ArticleFrontiers in bioengineering and biotechnology2026

AI-based wearable system for fall risk prediction in older adults using sEMG and plantar pressure data.

Xikun Ma, Yuandan Liao, Gang Tan, Boquan Qin, Guohui Chen, Xi Liu, Peifang Li, Jiali Chen, Xiao Ke, Hui Zhang

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In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Xikun Ma *Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Yuandan Liao *Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Gang TanDepartment of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Boquan QinDepartment of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Guohui ChenDepartment of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Xi LiuDepartment of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Peifang LiDepartment of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Jiali ChenDepartment of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Xiao KeSchool of Artificial Intelligence, Shenzhen Technology University, Shenzhen, China.
Hui ZhangDepartment of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Falls among older adults pose a major healthcare and social burden, making early identification of high-risk individuals essential for prevention. This study presents a portable, non-invasive AI-based wearable system that predicts fall risk using surface electromyography (sEMG) and plantar-pressure measurements collected during overground walking. sEMG electrodes were placed bilaterally over eight key lower-limb muscles-tibialis anterior, peroneus longus, medial and lateral gastrocnemius, rectus femoris, vastus medialis, vastus lateralis, and biceps femoris-while pressure insoles captured loading at eight anatomical foot regions. Ninety-four older adults (mean age 69.6

Indexed as

AI-based wearable systemfall risk assessmentmachine learningplantar pressure analysissurface electromyography

Identifiers

PMID42147486
PMCPMC13171544

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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.