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Sensor fusion is an important and crucial topic in many industrial applications. One of the challenging problems is to find an appropriate sensor combination for the dedicated application or to weight their information adequately. In our contribution, we focus on the application of the sensor fusion concept together with the reference to the distance-based learning for object classification purposes. The developed machine learning model has a bi-functional architecture, which learns on the one side the discrimination of the data regarding their classes and, on the other side, the importance of the single signals, i.e., the contribution of each sensor to the decision. We show that the resulting bi-functional model is interpretative, sparse, and simple to integrate in many standard artificial neural networks.
Marker-based systems can digitally record human movements in detail. Using the digital biomechanical human model Dynamicus, which was developed by the Institut für Mechatronik, it is possible to model joint angles and their velocities such accurately that it can be used to improve motion analysis in competitive sports or for ergonomic evaluation of motion sequences. In this paper, we use interpretable machine learning techniques to analyze the gait. Here, the focus is on the classification between foot touchdown and drop-off during normal walking. The motion data for training the model is labeled using force plates. We analyze how we could apply our machine learning models directly on new motion data recorded in a different scenario compared to the initial training, more precise on a treadmill. We use the properties of the interpretable model
to detect drift and to transfer our model if necessary.