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

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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.