Deep learning for environmental state prediction and sensor fusion for intelligent wearable robots

目前的假肢只能进行有限范围的活动。因此，需要多个假肢来执行不同的活动，例如行走和跑步。生产能够适应用户当前需求的设备具有改善用户生活质量的潜力。这项研究着眼于通过智能自适应控制器来改进假肢。这项工作的目的是对用户的活动、意图和效率进行分类，从而为自适应控制器的响应提供信息。对滚动机器学习的研究可以根据个人需求设置控制器增益;目的是随着时间的推移提高控制器效率，以及如何安全地部署控制器设置的动态变化。该系统的目的将是物理测试，使用小形状因子电子和嵌入式软件实现为一个真实的时间可穿戴设备。

Current prosthetics are only capable of performing a limited range of activities. Multiple prostheses are therefore required to perform different activities, such as walking and running. Producing a device that can adapt to the users current requirement has the potential to improve the users quality of life. The research looks into improvements in prosthetics that can be made through intelligent adaptive controllers. The work will aim to classify users activity, intent and efficiency informing an adaptive controllers response. Investigation into the roll machine learning can perform in setting controller gains based on an individuals requirements; with the aim of improving controller efficiency over time, and how dynamic changes to controller setup could be safely deployed. The system will be aim will be physically tested using small form factor electronics and embedded software to implement as a real time wearable device.