Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents our preliminary work on a depth camera based intent recognition system intended for future use in robotic prosthetic legs. The approach infers the activity mode of the subject for standing, walking, running, stair ascent and stair descent modes only using data from the depth camera. Depth difference images are also used to increase the performance of the approach by discriminating beten static and dynamic instances. After confidence map based filtering, simple features such as mean, maximum, minimum and standard deviation are extracted from rectangular regions of the frames. A support vector machine with a cubic kernel is used for the classification task. The classification results are post-processed by a voting filter to increase the robustness of activity mode recognition. Experiments conducted with a healthy subject donning the depth camera to his lor leg shod the efficacy of the approach. Specifically, the depth camera based recognition system was able to identify 28 activity mode transitions successfully. The only case of incorrect mode switching was an intended run to stand transition, where an intermediate transition from run to walk was recognized before transitioning to the intended standing mode.
['Huseyin Atakan Varol', 'Yerzhan Massalin']
I am writing a research paper on a idea A feasibility study of depth image based intent recognition for lower limb prostheses
[{"from": "human", "value": "I am writing a research paper on a idea A feasibility study of depth image based intent recognition for lower limb prostheses"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents our preliminary work on a depth camera based intent recognition system intended for future use in robotic prosthetic legs. The approach infers the activity mode of the subject for standing, walking, running, stair ascent and stair descent modes only using data from the depth camera. Depth difference images are also used to increase the performance of the approach by discriminating beten static and dynamic instances. After confidence map based filtering, simple features such as mean, maximum, minimum and standard deviation are extracted from rectangular regions of the frames. A support vector machine with a cubic kernel is used for the classification task. The classification results are post-processed by a voting filter to increase the robustness of activity mode recognition. Experiments conducted with a healthy subject donning the depth camera to his lor leg shod the efficacy of the approach. Specifically, the depth camera based recognition system was able to identify 28 activity mode transitions successfully. The only case of incorrect mode switching was an intended run to stand transition, where an intermediate transition from run to walk was recognized before transitioning to the intended standing mode."}]