Estimation of Fugl–Meyer Assessment Upper-Extremity Sub-Scores Using a Mixup-Augmented LSTM Autoencoder and Wearable Sensor Data

Stroke is a leading cause of long-term disability worldwide, necessitating efficient motor function assessment to guide personalized rehabilitation. The Fugl–Meyer Assessment for the Upper Extremity (FMA-UE) is a clinical gold-standard tool, but it is time consuming and requires trained clinicians,...

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Published in:Sensors (Basel, Switzerland) Vol. 25; no. 21; p. 6663
Main Authors: Liu, Minghao, Lu, Hsuan-Yu, Tong, Shuk-Fan, Liang, Dezhi, Sun, Haoyuan, Xing, Tian, Shi, Xiangqian, Yu, Hongliu, Tong, Raymond Kai-Yu
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.11.2025
Subjects:
Activities of daily living
Adult
Algorithms
Autoencoder
Automation
Data collection
Deep Learning
Elbow
Female
Fugl–Meyer assessment
Humans
IMU
Machine learning
Male
Middle Aged
Neural networks
Rehabilitation
remote assessment
Sensors
Stroke
Stroke - physiopathology
Stroke Rehabilitation - methods
Therapists
Upper Extremity - physiology
Upper Extremity - physiopathology
Wearable Electronic Devices
China
IMU
deep learning
Fugl–Meyer assessment
remote assessment
stroke
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Stroke is a leading cause of long-term disability worldwide, necessitating efficient motor function assessment to guide personalized rehabilitation. The Fugl–Meyer Assessment for the Upper Extremity (FMA-UE) is a clinical gold-standard tool, but it is time consuming and requires trained clinicians, which limits its frequency of use and accessibility. While wearable sensors and deep learning offer promising avenues for remote assessment, accurately estimating detailed sub-scores of specific motor functions remains a significant challenge. This work introduces a deep learning framework for automated estimation of FMA-UE total and subdivision scores. Data was collected from 15 participants using four inertial measurement units (IMUs) positioned on the arm and trunk. Each participant performed seven specialized functional motions designed for comprehensive joint synergy involvement within ten minutes. A therapist-rated FMA-UE provided true scores. The proposed model leverages the integration of an LSTM-based autoencoder and mixup augmentation to enhance generalization and robustness. Evaluated through a leave-one-subject-out cross-validation (LOSOCV), the estimator demonstrated strong performance, achieving R2 values exceeding 0.82. Pearson’s correlation coefficient r was more than 0.90, and the normalized root-mean-square errors (NRMSE) were below 0.14 for all subparts (A–D). Crucially, the total FMA-UE score was estimated with an NRMSE of 0.0678. These results show that a concise, sensor-based assessment can reliably predict detailed motor function scores.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25216663