Motor Learning for the Control of an Assistive Device

描述（由申请人提供）：这些研究的目的是了解如何利用和训练身体的运动来控制电动轮椅。先进的轮椅技术往往被大量潜在的轮椅使用者视为障碍。提出了一种方法，以消除这一障碍的基础上适应的辅助技术，以剩余的不受约束的流动性的患者和增强运动学习。这一探索性项目旨在确定这种方法的可行性，并在识别自然运动和使用虚拟现实（VR）的基础上开发培训方法。拟议的研究将在完全或不完全颈部损伤的四肢瘫痪脊髓损伤患者中进行。健康志愿者也将参与这些研究，以微调实验仪器，并提供参考基线，以评估学习和协调。受试者将穿着新型上身感应服装。服装产生的总共52个电信号将通过手腕、肘部、肩膀和躯干的运动进行调制。这些信号将被映射到模拟轮椅的速度命令。受试者将佩戴VR护目镜和头部跟踪器，从模拟轮椅的角度为他们提供计算机生成环境的沉浸式视图。虚拟现实环境和可穿戴信号技术的结合将为评估实际轮椅不可行的训练协议提供一个框架。拟议的研究分为两个具体目标：（目标1）。为了识别运动原语的控制虚拟轮椅的不受限制的上身运动三个完善的信号处理技术-主成分分析，独立成分分析和Isomap -将被用来提取低维信号模式的服装信号（目的2）进行比较。识别促进运动学习的地图和程序。从目标1中提取的信号模式将用于设计和测试从受试者运动到轮椅命令的新转换。一种众所周知的机器学习技术--最小均方梯度下降--将被测试用于将受试者的自然运动基元与一组适当的控制信号匹配到轮椅。最后，安全的VR环境将允许我们测试通过逐渐加快轮椅运动还是逐渐减慢轮椅运动来学习是否最有效。这些研究的结果有望指导基于人类运动学习和自适应控制工程的辅助设备的新技术的开发。许多残疾人在利用辅助技术方面面临着困难的挑战。特别地，动力轮椅的安全和有效使用受到患者学习操作其控制设备的需要的限制。拟议的研究将调查扭转这种情况的可能性，并利用先进技术使控制装置适应患者的剩余技能。

DESCRIPTION (provided by applicant): The goal of these studies is to understand how movements of the body can be harnessed and trained to control electrically powered wheelchairs. Advanced wheelchair technology is often perceived to be a barrier by a large number of potential wheelchair users. An approach is proposed for the removal of this barrier based on adapting the assistive technology to the residual unconstrained mobility of the patients and on enhancing motor learning. This exploratory project aims at establishing the feasibility of such an approach and at developing training methods based on the identification of natural motions and on the use of virtual reality (VR). The proposed studies will be carried out on quadriplegic spinal cord injured patients with complete or incomplete cervical injuries. Healthy volunteers will also participate in these study to fine-tune the experimental apparatus and to provide a reference baseline to assess learning and coordination. Subjects will wear a novel upper-body sensing garment. A total of 52 electrical signals generated by the garment will be modulated by movements of the wrist, elbow, shoulder and torso. These signals will be mapped into the velocity commands for a simulated wheelchair. Subjects will wear VR-goggles and a head tracker, which will provide them with a immersive view of a computer-generated environment from the perspective of the simulated wheelchair. The combination of virtual reality environments and wearable signal technology will provide a framework for evaluating training protocols that would not be feasible with actual wheelchairs. The proposed studies are organized in two specific aims: (Aim 1.) To identify motor primitives for the control of a virtual wheelchair by unrestricted upper body motions Three well-established signal processing techniques - Principal Component Analysis, Independent Component Analysis and Isomap - will be used and compared for extracting low-dimensional signal patterns from the garment signals (Aim 2.) To identify maps and procedures that facilitate motor learning. The signal patterns extracted from Aim 1 will be used to design and test new transformations from subject motions to wheelchair commands. A well- known machine learning technique -least mean squares gradient descent - will be tested for matching the natural motor primitives of the subjects with an appropriate set of control signals to the wheelchair. Finally the safe VR environment will allow us to test whether it is most efficient to learn by gradually speeding up wheelchair motions or by gradually slowing them down. The results of these studies are expected to guide the development of new technology for assistive devices based on human motor learning and on engineering of adaptive control. Many disabled individuals are facing difficult challenges to take advantage of assistive technologies. In particular the safe and efficient use of powered wheelchair is limited by the need for patients to learn to operate their control apparatus. The proposed studies will investigate the possibility to reverse this situation and take advantage of advanced technologies for adapting the control apparatus to the residual skills of the patients.