We propose a method to automatically translate a preexisting activity recognition system, devised for a source sensor domain S, so that it can operate on a newly discovered target sensor domain T , possibly of different modality. First, we use MIMO system identification techniques to obtain a function that maps the signals of S to T . This mapping is then used to translate the recognition system across the sensor domains. We demonstrate the approach in a 5-class gesture recognition problem translating between a vision-based skeleton tracking system (Kinect), and inertial measurement units (IMUs). An adequate mapping can be learned in as few as a single gesture (3 seconds) in this scenario. The accuracy after Kinect → IMU or IMU → Kinect translation is 4% below the baseline for the same limb. Translating across modalities and also to an adjacent limb yields an accuracy 8% below baseline. We discuss the sources of errors and means for improvement. The approach is independent of the sensor modalities. It supports multimodal activity recognition and more flexible real-world activity recognition system deployments.
Abstract-Activity recognition using onbody sensors are prone to degradation due to changes on sensor readings. The changes can occur because of degradation or alteration in the behavior of the sensor with respect to the others. In this paper we propose a method which detects anomalous nodes in the network and takes compensatory actions to keep the performance of the system as high as possible while the system is running. We show on two activity datasets with different configurations of onbody sensors that detection and compensation of anomalies make the system more robust against the changes.
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