Toward intelligent wearables: engineering self-powered triboelectric nanofiber sensors for the classification of risky ankle postures

Materials Horizons 2026

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Authors: Asma Akter, Gokana Mohana Rani, Md Mehedi Hasan Apu, Yun Suk Huh, Asma Yamani, Abduljabar Q. Alsayoud, Reddicherla Umapathi, Turki N. Baroud
DOI: 10.1039/d5mh02434j
Cited by: 1
References: 65

Abstract

sufficient to charge capacitors and power small electronics. Integrated into an ankle-worn platform, the fiber-based TENG device generated high-fidelity biomechanical signals. When analyzed using machine learning algorithms, the system achieves up to 99% accuracy across 700 datasets in detecting risky motions preceding sprains. This intelligence shifts the system from passive monitoring to proactive prevention, providing actionable feedback before injury onset. Beyond ankle injuries, this convergence of self-powered materials and artificial intelligence establishes a new class of intelligent wearables and paves the way for advanced musculoskeletal health monitoring and preventive medicine.

Fields of Study & Subjects

Advanced Sensor and Energy Harvesting Materials Prosthetics and Rehabilitation Robotics Lower Extremity Biomechanics and Pathologies

Publication details

Journal / source
Materials Horizons
Publisher
Royal Society of Chemistry (RSC)
Volume
13
Issue
15
Pages
7674–7689
Publication date
1 January 2026
Language
EN