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Zhang, Kuangen

University of British Columbia

Powered prostheses are effective for helping amputees walk on level ground, but these devices are inconvenient to use in complex environments. To address the above issue, the present thesis develops a sensor fusion system in a complete human-prosthesis-environment loop to recognize the environmental context, predict the motion intent of different subjects, and control the motion of the prosthesis. There are challenges for realizing this objective: 1. Complex information in the prosthesis-environment loop. 2. User-dependent and subjective information in the human-prosthesis loop. 3. Noisy decisions. 4. Disconnected neural system of the user. 5. Infeasibility of continuous control. 6. No accommodation of navigation in rough terrains. To resolve the mentioned problems, the present thesis develops several methods. First, the present thesis designs a vision-motion sensor fusion system and a side-view convolutional neural network, to recognize the environmental context. To resolve the user-dependent issue, the present thesis proposes an unsupervised cross-subject adaptation method to recognize the locomotion intent for target subjects at 95% accuracy even if the associated data are not labeled. Then the thesis fuses the sequential decisions based on a hidden Markov model to smooth the decisions. Next, the thesis fuses the sensing and control system of the prosthesis to control the powered prosthesis predictively. To improve the authority of the prosthesis user, the present thesis studies two more challenging areas: 1) Transformation from discrete-state control to continuous-phase control. The present thesis estimates the gait phase of human locomotion in complex terrains with 80% lower error than traditional methods; 2) Transformation from structured terrains to rough terrains. The thesis fuses sequential 3D gaze and the environmental context to predict the foot placements of the wearer in rough terrains. The developed methodology is implemented in a physical prototype, and the performance is validated using both computer simulation and experimentation. The developed system and algorithms can recognize the environmental context and predict the motion intent of different amputee users accurately (at an accuracy = 97%) and efficiently (computing time < 30 ms), which can predictively control the prosthesis to assist the wearer to walk in complex environments more conveniently and smoothly.

Text

2022-01-25

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/80727

Mechanical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/