Movement intention detection for intuitive and non-intrusive prosthetic arm control

假肢有可能改善失去手臂的人的生活。然而，它们的功能是有限的，因为它们没有连接到人类的神经系统。我的目标是通过检测人类的运动意图来设计和改进义肢与用户之间的界面。当进行目标导向的运动时，人类使用他们的眼睛和其他感官信息来指导手臂运动。来自眼睛和手臂肌肉的综合信号将帮助我们检测运动意图。例如，在伸手抓握运动中，我们首先用眼睛扫描环境，识别目标，提取目标的位置、大小、形状和纹理，然后伸手抓。然而，之前的大多数运动意图检测算法都是基于肌肉活动和大脑信号。据我们所知，利用眼球测量来改善假肢控制很少被探索。这一知识空白需要在眼动如何与运动意图相关联以及如何融合眼动指标和手臂肌肉信号来改善假肢控制方面得到填补。我的研究项目的长期目标是通过纳入非侵入性和经济实惠的技术，通过改进对用户运动意图的检测，更好地控制人工身体部位，来推进包括人体假体在内的辅助设备。我的短期目标（未来5年内）是：1)开发一种带有传感器的假肢手臂袜子，用于收集肌肉活动数据；2)检查肌肉和眼信号与运动意图之间的关系；3)使用包括眼动追踪技术在内的多个模块输入来改进抓握检测；4)使用机器学习从多个生物信号通道中准确估计手指运动，并通过整合眼动追踪（瞳孔直径）。5)开发一种机器学习模型，以准确预测序列目标导向任务中的一系列运动意图。传感器融合技术也将在我的研究中实施，以利用多模块输入的优势。这项工作将产生将眼动和肌肉活动信号转化为运动意图的重要工具，这是一种针对假肢直观控制的运动意图检测的创新方法。近年来，假肢装置已经有了很大的进步，但用于假肢控制的人机界面还没有充分发挥设备的潜力。弥合这一差距将极大地推动假肢系统的发展，提高使用者的生活质量。我的研究将使大量失去肢体的人和假肢行业受益（增加就业和研究经费）。我希望从这项工作中产生的知识将使我们能够设计出智能假肢手臂，并进一步提高我们对人机/机器人交互，远程操作和辅助设备控制的理解。

Prosthetic arms have the potential to improve the lives of individuals who have lost arms. Yet their functioning is limited as they are not connected to the human neural system. My goal is to design and improve the interface between a prosthetic arm and the user by detecting human movement intention. When performing a goal-directed movement, humans employ their eyes and other sensory information for guiding arm movement. Comprehensive signals from both eyes and arm muscles would help us to detect movement intention. For example, during a reach-and-grasp movement, we firstly use our eyes to scan the environment, identifying a target, extracting its location, size, shape and texture, and then reaching and grabbing. However, most previous algorithms for motion intention detection were based on muscle activities and brain signals. To the best of our knowledge, utilizing eye-metrics for improving prosthesis control has rarely been explored. This knowledge gap needs to be filled in terms of how eye-motions correlate to movement intention and how fusion of eye-metrics and arm muscles signals can be employed to improve prosthesis control. The long-term objective of my research program is to advance assistive devices including human prosthesis through the inclusion of non-invasive and affordable technologies, and by improving the detection of movement intention of the users for better control of the artificial body part. My short-term objectives (within the next 5 years) are to 1) develop a prosthetic arm sock with sensors to collect muscle activities data, 2) examine the relationship between muscle and eye signals to movement intention, 3) improve grasp detection using multiple module inputs including eye-tracking technology, 4) accurately estimate finger movement using machine learning from multiple channels of bio-signals and by integrating eye-tracking (pupil diameter), and 5) develop a machine learning model to accurately predict a series of movement intentions during a sequential goal-directed task. Sensor fusion technologies will also be implemented throughout my research to utilize the advantages of multiple modules input. This work will generate important tools to convert eye motion and muscle activity signals into movement intention, which is an innovative approach to movement intention detection for intuitive prosthesis control. Prosthetic devices have been greatly improved in recent years whereas man-machine interfaces for prosthesis control do not fulfill the device potential. Bridging this gap would greatly advance prosthesis systems and improve quality of life among users. My research will benefit both the large number of people who have lost limbs and the prosthesis industry (increase in jobs and research funding). I expect that the knowledge generated from this work will enable us to design a smart prosthetic arm and further improve our understanding of human-machine/robot interaction, remote manipulation and assistive device control.