Single BSN-based multi-label activity recognition

Wearable physical sensor signal processing-based activity recognition has profound impacts on context-aware remote healthcare and cognitive reasoning. Different nodes of Body Sensor Network (BSN) are involved with different contexts that limits single BSN sensor based context recognition models into a sole extreme. On the other hand, multiple physical sensors based context recognition systems are suffered with disrupted BSN network connectivity, multi-modal sensor signal processing complexity, multi-sensor device implantation and fault intolerance. To mitigate these problems, we postulate a sparse-deconvolution aided single BSN sensor-based multi-label physical activity recognition framework. We first consider a single physical sensor device is attached to individual's wrist node i.e., one of the upper extreme body nodes. We hypothesize that the final sensor signals of upper extreme nodes are affected by the lower extreme nodes contexts with an approximately sparse factor (ASF). Based on the hypothesis, we postulate (a) a sparse deconvolution method on the upper extreme node signals to disaggregate ASF and original signals; and design (b) a hybrid classification model to detect both upper extreme (such as, hand waving, hand shaking etc.) and lower extreme (such as, walking, standing, running etc.) activities. We evaluate the performance of our proposed framework with three real-time dataset with distinct characteristics (a real-time collected activity dataset in a controlled lab environment, a real-time smart home system deployed in a retirement community center -(IRB #HP-00064387) and a publicly available dataset) which corroborates a radical improvement in recognizing multi-label human activities.