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DOI 10.1093/milmed/usag198

Медицинская библиотека · Military medicine · 2026

Sensor Design for Ankle Resiliency Training.

Iyer Ashwin, Silder Amy, Lassard Hunter, Roy Serge H, De Luca Gianluca, Kline Joshua, Chiodini John P

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Аннотация

INTRODUCTION

Lateral ankle sprains (LASs) are among the most common musculoskeletal injuries and are a leading cause of limited duty days among service members. Rehabilitation and prevention strategies focus on strengthening the peroneus longus (PL) muscle as a protection mechanism, however clinical assessments of PL engagement during training are qualitative and require expert evaluation. Existing surface electromyography (sEMG) technologies allow noninvasive estimations of muscle activation, but are limited when energy from neighboring muscles (crosstalk) reaches detection sites at anatomically constrained muscles like the PL. To address this gap, we introduce the Ankle REsiliency System (ARES), a hardware-software platform that integrates high-density sEMG with custom signal processing algorithms to isolate and quantify PL workload during functional exercises.

MATERIALS AND

METHODS

ARES consists of a high density sEMG electrode array equipped with custom signal processing firmware and software which streams high fidelity sEMG data and quantifies PL workload during functional exercises. System validation was conducted with 5 healthy male participants (21-35 years). Algorithm validation was performed on isolated tasks of eversion and dorsiflexion, where PL workload estimates were compared against true labels provided by bipolar sEMG recordings. Full system validation was then performed during 5 biomechanically relevant exercises designed to challenge the PL across varying intensity levels.

RESULTS

For isolated tasks, ARES achieved 93.3% ± 1.1% accuracy in distinguishing PL activity from crosstalk. During dynamic exercises, ARES detected workload increases between low and high force exercises (P < .05).

CONCLUSIONS

Our work describes the design of ARES by integrating sEMG hardware and signal processing algorithms into a tool to assess PL workload during functional exercises. Results show an ability to robustly differentiate between crosstalk and PL activity highlighting potential utility to assist in injury-prevention or rehabilitation outcomes.

Для цитирования: Iyer Ashwin, Silder Amy, Lassard Hunter, Roy Serge H, De Luca Gianluca, Kline Joshua, Chiodini John P. Sensor Design for Ankle Resiliency Training.. Military medicine. 2026. DOI: 10.1093/milmed/usag198.

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