多模态控制+生理监测，让智能轮椅更安全自主
Advancing Assistive Robotics: Multi-Modal Navigation and Biophysical Monitoring for Next-Generation Wheelchairs
- 集成手柄、语音、手势、眼动四种操控方式，自由切换
- 生理数据误差小：心率≤2bpm，体温≤0.5℃，血氧≤1%
- 支持实时云端报警，适合行动障碍者及照护人员使用
电动轮椅（EPW）已成为肌萎缩侧索硬化症（ALS）、中风偏瘫及痴呆相关运动障碍患者的重要出行工具。本文提出一种新型多模态EPW控制系统，兼顾用户需求与模式无缝切换。集成手柄、语音、手势和眼电图（EOG）四种互补控制接口，并融合持续生命体征监测系统，实时测量心率变异性、血氧饱和度（SpO2）和皮肤温度。双点校准显示，生物传感器与临床设备相比，心率误差不超过2 bpm，体温误差不超过0.5℃，血氧误差不超过1%。共20名行动障碍者完成500次室内导航指令测试，命令识别准确率分别为：手柄99%，语音97%±2%，手势95%±3%，平均闭环延迟20±0.5毫秒。护理人员通过加密云传输的安卓应用接收实时警报。该原型整合多模态操控与云健康监测，在满足ISO 7176-31与IEC 80601-2-78安全标准的同时，为未来自适应机器学习升级奠定基础。
原文摘要 · Abstract (English)
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.
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