SCH: Enabling Disabled People to Control the Full Dexterity of Wearable Assistive Robots

这一智能健康(SCH)奖将通过开发技术来帮助数百万遭受截肢或先天性上肢缺陷的人，从而促进国家健康和福利的进步。这些问题对受害者的生活质量造成了毁灭性的影响，并给美国人带来了巨大的经济负担。不幸的是，假手置换的最新水平无法与科幻电影中流行的假手形象相提并论，这使得没有上肢的人只能基本控制基本的抓取功能。这项工作将探索通过一种可在家中使用的自动化训练方案来帮助上肢缺陷者学习复杂假手的高级控制。通过远程学习功能实现医疗保健这一方面的自动化，可以帮助残疾人更快、更低成本地获得治疗。此外，这笔赠款的研究将用于为来自低收入家庭的高中生创造学习体验，以帮助培养下一代工程师和科学家。因此，这项研究可以为美国的社会和经济带来好处。目前，灵巧的可穿戴协作机器人助手有很多选择，比如假手，未来的设备会变得更加复杂。事实上，这些设备的灵巧性正在迅速超过人们直观控制它们的能力。这个问题的一个主要来源是无法可靠地解释操作员在几个月和几年的过程中的意图。另一个问题是，定制培训项目背后的科学尚未得到深入探索，这些培训项目旨在帮助残疾人充分利用假手等灵巧辅助机器人的潜力。该项目将通过三个主要努力来解决这些不足：(1)探索一种新型的双峰皮肤传感器，以克服现有传感器的局限性，(2)调查机器学习算法的效用，以对人类操作员的意图进行分类，以及(3)创建新的强化学习范式，随着时间的推移，引入定制的、特定于学科的肌肉训练练习，以最大限度地提高生物控制信号分类的准确性和独立生物控制通道的数量。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Smart Health (SCH) award will contribute to the advancement of the national health and welfare by developing technology to help the millions of people who have suffered an amputation or who have a congenital deficiency of an upper limb. These problems create a devastating impact on the quality of the victims’ lives and pose a significant financial burden to people in the U.S.A. Unfortunately, the state of the art in prosthetic hand replacements is not comparable to their popular portrayal in sci-fi movies, leaving upper limb-absent people with rudimentary control of basic grasp functions. This work will explore methods to help upper limb-absent people learn advanced control of sophisticated prosthetic hands with an automated training regimen that can be used at home. Automating this aspect of healthcare with remote learning functionality can help disabled people access treatment more quickly and at a lower cost. Furthermore, research from this grant will be used to create learning experiences for high school students from low-income households to help educate the next generation of engineers and scientists. Thus, this research can benefit the society and economy of the USA. Currently, there are numerous options for dexterous wearable co-robot assistants, such as prosthetic hands, with future devices becoming even more sophisticated. In fact, the dexterity of these devices is rapidly outpacing the ability for people to intuitively control them. One main source of this problem stems from the inability to reliably interpret the intentions of the human operator over the course of months and years. Another problem is that the science behind customizable training programs to empower disabled people to harness the full potential of dexterous assistive robots such as prosthetic hands has not been deeply explored. This project will address these deficiencies through three main endeavors: (1) exploration of a novel bimodal skin sensor to overcome limitations with current sensors, (2) investigation of the utility of machine learning algorithms to classify the intention of human operators, and (3) creation of a new reinforcement learning paradigm to sequentially introduce customized, subject-specific muscle training exercises over time to maximize biocontrol signal classification accuracy and the number of independent biocontrol channels.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.