Retraining Random Forest Algorithm For Lower Limb Prosthesis Tracking Using An Rgb-D Camera

Lower limb prosthesis has the purpose of recovering mobility in amputees, giving autonomy to patients to do several activities. Mobility degree quantification and correct use of the prosthesis is necessary to reduce the risk of desertion. An adequate measurement of movements when patients are walking can help the physiotherapists evaluate the performance. For that reason, this work presents a new tracking method based on the extraction of texture and shape features that feed the retraining Random Forest classifier. The aim is to use a depth camera to track people with lower limb prosthesis when walking between parallel bars. Two experiments were performed with the proposed system: the first one under three patients with lower limb prostheses in order to apply the tracking algorithm. The second was carried out in three healthy control subjects with the purpose of validating the proposed algorithm and comparing the results with a motion capture system (MoCap). In this test the participants carried out two different activities; the results present errors from 3.3 to 4.9 mm according to the root mean square error. This suggests that the system can be used to track human joints under different conditions; however, it is necessary to solve the problem of occlusion artifacts by using human body models or by employing several depth cameras.