次世代車椅子のための支援ロボティクス：マルチモーダルナビゲーションと生体モニタリング
Advancing Assistive Robotics: Multi-Modal Navigation and Biophysical Monitoring for Next-Generation Wheelchairs
ジョイスティック・音声・ジェスチャ・EOGの4つの操作インターフェースと心拍・SpO2・皮膚温の連続モニタリングを統合した電動車椅子システムを開発し、20名の被験者による500回の屋内ナビゲーションで高い認識精度と低遅延を実証した。
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著者: Md. Anowar Hossain, Mohd. Ehsanul Hoque
分類: cs.RO, cs.AR
原文アブストラクト
Assistive electric-powered wheelchairs (EPWs) have become essential mobility aids for people with disabilities such as amyotrophic lateral sclerosis (ALS), post-stroke hemiplegia, and dementia-related mobility impairment. This work presents a novel multi-modal EPW control system designed to prioritize patient needs while allowing seamless switching between control modes. Four complementary interfaces, namely joystick, speech, hand gesture, and electrooculography (EOG), are integrated with a continuous vital sign monitoring framework measuring heart rate variability, oxygen saturation (SpO2), and skin temperature. This combination enables greater patient independence while allowing caregivers to maintain real-time supervision and early intervention capability. Two-point calibration of the biophysical sensors against clinical reference devices resulted in root mean square errors of at most 2 bpm for heart rate, 0.5 degree Celsius for skin temperature, and 1 percent for SpO2. Experimental evaluation involved twenty participants with mobility impairments executing a total of 500 indoor navigation commands. The achieved command recognition accuracies were 99 percent for joystick control, 97 percent plus or minus 2 percent for speech, and 95 percent plus or minus 3 percent for hand gesture, with an average closed-loop latency of 20 plus or minus 0.5 milliseconds. Caregivers receive real-time alerts through an Android application following encrypted cloud transmission of physiological data. By integrating multi-modal mobility control with cloud-enabled health monitoring and reporting latency and energy budgets, the proposed prototype addresses key challenges in assistive robotics, contributes toward compliance with ISO 7176-31 and IEC 80601-2-78 safety standards, and establishes a foundation for future adaptive machine learning enhancements.
