Voice-Driven Assistance and Resistance Modulation in a Soft Hip Exosuit Using a Transformer-Based Speech Recognition Model

Abstract

Intuitive human–robot interfaces are essential to increase usability and personalization in wearable robotic assistive technologies. However, most current systems rely on pre-programmed or sensor-driven strategies that offer limited active user control online. To address this limitation, we present a voice-driven control framework for a soft hip exosuit, enabling on-demand modulation of assistance and resistance via short spoken commands. The system combines a fully embedded transformer-based automatic speech recognition model (Whisper) with a gait-phase estimator to synchronize actuation with the user’s motion. Users can switch between assistive and resistive modes and select discrete gain levels (low, medium, high). Experiments with six healthy participants demonstrate high recognition accuracy (95-100%) and low latency (∼9 ms). Metabolic measurements show that assistive commands reduced walking energy cost by 20.9±4.8% (LOW) and 9.7±5.5% (MEDIUM) relative to baseline, while resistive commands increased cost by 13.1±3.5% (MEDIUM) and 14.9±5.1% (HIGH). These results highlight the feasibility of intuitive, voice-driven modulation in wearable robotics.