Rehabilitative mobility aids are being used extensively for physically impaired people. Efforts are being made to develop human machine interfaces (HMIs), manipulating the biosignals to better control the electromechanical mobility aids, especially the wheelchairs. Creating precise control commands such as move forward, left, right, backward and stop, via biosignals, in an appropriate HMI is the actual challenge, as the people with a high level of disability (quadriplegia and paralysis, etc.) are unable to drive conventional wheelchairs. Therefore, a novel system driven by optical signals addressing the needs of such a physically impaired population is introduced in this paper. The present system is divided into two parts: the first part comprises of detection of eyeball movements together with the processing of the optical signal, and the second part encompasses the mechanical assembly module, i.e., control of the wheelchair through motor driving circuitry. A web camera is used to capture real-time images. The processor used is Raspberry-Pi with Linux operating system. In order to make the system more congenial and reliable, the voice-controlled mode is incorporated in the wheelchair. To appraise the system’s performance, a basic wheelchair skill test (WST) is carried out. Basic skills like movement on plain and rough surfaces in forward, reverse direction and turning capability were analyzed for easier comparison with other existing wheelchair setups on the bases of controlling mechanisms, compatibility, design models, and usability in diverse conditions. System successfully operates with average response time of 3 s for eye and 3.4 s for voice control mode.

中文翻译：

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图像梯度方法的轮椅和语音控制人机界面系统

康复行动辅助设备已广泛用于肢体残障人士。人们正在努力开发人机界面（HMI），操纵生物信号以更好地控制电动助行器，特别是轮椅。实际的挑战是，在高水平的残疾人（四肢瘫痪和瘫痪等）无法驾驶的情况下，通过合适的HMI创建精确的控制命令，例如通过生物信号通过向前，向左，向右，向后和停止，这是实际的挑战。传统轮椅。因此，本文介绍了一种由光信号驱动的新型系统，可满足此类身体残障人群的需求。本系统分为两部分：第一部分包括对眼球运动的检测以及对光信号的处理，第二部分包括机械组装模块，即通过电动机驱动电路控制轮椅。网络摄像头用于捕获实时图像。使用的处理器是具有Linux操作系统的Raspberry-Pi。为了使系统更加舒适和可靠，在轮椅中采用了语音控制模式。为了评估系统的性能，执行了基本的轮椅技能测试（WST）。分析了基本技能，例如在平整和粗糙表面上向前，向后的运动以及转向能力，以便在控制条件，兼容性，设计模型和在各种条件下的可用性的基础上，与其他现有的轮椅设置进行轻松比较。系统成功运行，眼睛的平均响应时间为3 s，语音控制模式的平均响应时间为3.4 s。