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.

翻译：辅助性电动轮椅已成为肌萎缩侧索硬化症、中风后偏瘫及痴呆相关行动障碍等残疾人士不可或缺的移动辅具。本研究提出一种创新的多模态电动轮椅控制系统，其设计核心在于优先满足患者需求，同时实现控制模式间的无缝切换。该系统整合了四种互补交互界面——操纵杆、语音、手势及眼电图控制，并搭载持续生命体征监测框架，可同步检测心率变异性、血氧饱和度与皮肤温度。该集成方案在提升患者自主性的同时，使护理人员能够保持实时监护与早期干预能力。生物物理传感器经临床标准设备两点校准后，其均方根误差达到：心率≤2次/分，皮肤温度≤0.5摄氏度，血氧饱和度≤1%。实验评估邀请20名行动障碍参与者执行共计500次室内导航指令，系统识别准确率为：操纵杆控制99%，语音控制97%±2%，手势控制95%±3%，平均闭环延迟为20±0.5毫秒。生理数据经加密云端传输后，护理人员可通过Android应用程序接收实时警报。本原型系统通过融合多模态移动控制、云端健康监测及延迟与能耗报告机制，解决了辅助机器人领域的关键挑战，助力符合ISO 7176-31与IEC 80601-2-78安全标准，并为未来自适应机器学习升级奠定了技术基础。