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Following to an implantation of an artificial knee joint, patients have to perform rehabilitation exercises at home. The motivation to exercise can be low and if the exercises are not executed, an extended rehabilitation time or a follow-up operation is possibly required. Moreover, incorrect exercise executions over a long period can lead to injuries. Therefore, we present two Programming by Demonstration (PbD) algorithms, a NearestNeighbour (NN) model and the Alpha Algorithm (AlpAl), for measuring the quality of exercise executions, which can be used in order to give feedback in exergames. The models can locate an ideal posture depending on a patient’s posture in a dynamic movement. Furthermore, they work in real time and independent of the execution speed, in order to suggest the correct exercise movement. To validate the functionality of the algorithms, four correct and incorrect test movements of four persons were analyzed from the monitoring algorithms. Each localized ideal movement from the algorithms as well as each ground truth movement were compared with an imitated test movement by a Dynamic Time Warping (DTW) algorithm. Since we expect a linear dependency between the DTW-distances, we calculated the linear correlation, which was significant high. Hence, we think that the proposed algorithms are appropriate to monitor physiotherapeutic exercises while playing an exergame.

Dynamic Movement Monitoring - Algorithms for Real Time Exercise Movement Feedback