Abstract
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.
Abstract (translated)
助行电动轮椅（EPW）已成为肌萎缩侧索硬化症（ALS）、中风后偏瘫和痴呆相关移动障碍等残疾人士的重要移动辅助工具。本文介绍了一种新型的多模式EPW控制系统，该系统旨在优先考虑患者需求并允许在控制模式之间无缝切换。四种互补接口——即操纵杆、语音、手势和眼电图（EOG）被整合进一个连续的生命体征监测框架中，此框架测量心率变异性、血氧饱和度（SpO2）以及皮肤温度。这种组合提高了患者的独立性，同时让护理人员能够进行实时监督并及时采取干预措施。通过与临床参考设备进行两点校准后，生物物理传感器的心率最大误差为每分钟2次搏动，皮肤温度的误差为0.5摄氏度，血氧饱和度的最大误差为1%。 实验评估涉及20名具有移动障碍的参与者执行总共500个室内导航命令。实现的命令识别准确性为：操纵杆控制99%，语音控制97%±2%，手势控制95%±3%，平均闭环延迟时间为20±0.5毫秒。护理人员通过一个安卓应用程序接收实时警报，生理数据则经过加密后上传至云端。 该原型将多模式移动控制与基于云的健康监测相结合，并考虑了报告延迟和能耗预算，从而解决了辅助机器人领域的关键挑战，有助于符合ISO 7176-31和IEC 80601-2-78安全标准的要求。此外，它还为未来的自适应机器学习增强奠定了基础。
URL
https://arxiv.org/abs/2601.02766
