School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad 45200, Pakistan.
Department of Electrical Engineering, University of Engineering and Technology Lahore-FSD Campus, Faisalabad 38000, Pakistan.
Sensors (Basel). 2020 Sep 26;20(19):5510. doi: 10.3390/s20195510.
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.
康复移动辅助设备被广泛应用于身体机能受损的人群。人们正在努力开发人机接口（HMIs），通过操纵生物信号来更好地控制机电移动辅助设备，尤其是轮椅。通过生物信号在适当的 HMI 中创建精确的控制命令，如前进、左、右、后和停止，是一个实际的挑战，因为残疾程度较高的人（四肢瘫痪等）无法驾驶传统轮椅。因此，本文介绍了一种由光学信号驱动的新型系统，以满足身体机能受损人群的需求。本系统分为两部分：第一部分包括眼球运动的检测以及光学信号的处理，第二部分包括机械组件模块，即通过电机驱动电路控制轮椅。使用网络摄像头实时捕捉图像。所使用的处理器是带有 Linux 操作系统的 Raspberry-Pi。为了使系统更加友好和可靠，轮椅配备了语音控制模式。为了评估系统的性能，进行了基本轮椅技能测试（WST）。在控制机制、兼容性、设计模型以及在不同条件下的可用性等方面，对基本技能进行了分析，例如在平坦和粗糙表面上的前进、后退和转向能力。系统的平均响应时间为 3 秒用于眼部控制，3.4 秒用于语音控制模式。
