Real-Time Estimation of Anterior-Posterior Ground Reaction Forces in Individuals with Stroke Using Wearable Sensors and Deep Learning
Year: 2025
Type: preprint
Abstract: Reduced limb propulsion is linked to impaired mobility and fall risk. Biofeedback retraining that targets anterior ground reaction forces (AGRFs) can enhance propulsion, but current approaches rely on costly lab-based equipment. Inertial measurement units (IMUs) offer a low-cost, portable alternative. We developed a hybrid convolutional–recurrent neural network to estimate AGRF time series during walking in 14 individuals with chronic hemiparesis post-stroke, using kinematic signals from wearable IMUs. The model was trained with leave-one-subject-out cross validation to emphasize generalizability and evaluated using mean absolute error (MAE), coefficients of determination (R²), normalized root mean squared error (NRMSE), and minimal detectable change (MDC). With IMUs across all lower-limb segments, the model achieved a MAE of 3.3 ± 1.2% BW; accuracy was preserved with only pelvis and thigh sensors (3.4 ± 1.5% BW), supporting clinical practicality with reduced sensor sets. Although group-level errors exceeded MDC thresholds, over half of trials fell within the between-session MDC, suggesting reliable within-subject tracking of AGRF. Outlier cases, primarily among participants with greater mediolateral sway, indicated gait stability as a key factor influencing accuracy. This highlights the potential need for personalized calibration or adaptive modeling in heterogeneous populations. Designed for streaming, frame-by-frame inference with low computational cost, the model supports real-time deployment on portable hardware. By enabling accurate AGRF estimation with a few wearable IMUs, this approach could facilitate in-clinic propulsion assessments and biofeedback-based gait training outside controlled labs, improving accessibility and ecological validity of rehabilitation.
Institution: George Mason University
Language: English
Subfield: Rehabilitation
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4414798702