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，需要多个假体来执行不同的活动，如行走和跑步。生产一种能够适应用户当前需求的设备，有可能提高用户的生活质量。这项研究着眼于通过智能自适应控制器对假肢进行改进。这项工作的目的是对用户的活动、意图和效率进行分类，以通知自适应控制器的响应。研究滚筒机器学习可以根据个人要求设置控制器增益；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对控制器设置的动态更改。该系统将被AIM使用小型电子设备和嵌入式软件进行物理测试，以实现为实时可穿戴设备。

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.