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。我的目标是设计和改善假肢和用户之间的接口，通过检测人类的运动意图。当执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引导手臂运动。来自眼睛和手臂肌肉的综合信号将有助于我们检测运动意图。例如，在伸手抓取运动中，我们首先用眼睛扫描环境，识别目标，提取其位置，大小，形状和纹理，然后伸手抓取。然而，大多数先前的运动意图检测算法是基于肌肉活动和大脑信号。据我们所知，利用眼指标来改善假肢控制很少被探索。这方面的知识差距需要填补眼睛运动与运动意图的相关性，以及如何融合眼睛指标和手臂肌肉信号可以用来改善假肢控制。我的研究计划的长期目标是通过纳入非侵入性和负担得起的技术，并通过改进对使用者运动意图的检测，以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（在未来5年内）将1）开发一种带有传感器的假肢袜子，以收集肌肉活动数据，2）检查肌肉和眼睛信号与运动意图之间的关系，3）使用包括眼睛跟踪技术在内的多个模块输入来改进抓握检测，4）使用来自多个通道的生物信号的机器学习并通过整合眼睛跟踪来准确地估计手指移动（瞳孔直径），以及5）开发机器学习模型以准确地预测顺序目标导向任务期间的一系列运动意图。传感器融合技术也将在我的研究中实施，以利用多个模块输入的优势。这项工作将产生重要的工具，将眼睛运动和肌肉活动信号转换为运动意图，这是一种创新的方法，运动意图检测直观的假肢控制。近年来，假体装置已经得到了很大的改进，而用于假体控制的人机界面并没有实现装置的潜力。弥合这一差距将大大推进假肢系统，提高使用者的生活质量。我的研究将有利于大量失去肢体的人和假肢行业（增加就业机会和研究资金）。我希望从这项工作中产生的知识将使我们能够设计一个智能假肢，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.