Biosignal processing and activity modeling for multimodal human activity recognition

This dissertation’s primary goal was to systematically study human activity recognition and enhance its performance by advancing human activities’ sequential modeling based on HMM-based machine learning. Driven by these purposes, this dissertation has the following major contributions: The proposal of our HAR research pipeline that guides the building of a robust wearable end-to-end HAR system and the implementation of the recording and recognition software Activity Signal Kit (ASK) according to the pipeline; Collecting several datasets of multimodal biosignals from over 25 subjects using the self-implemented ASK software and implementing an easy mechanism to segment and annotate the data; The comprehensive research on the offline HAR system based on the recorded datasets and the implementation of an end-to-end real-time HAR system; A novel activity modeling method for HAR, which partitions the human activity into a sequence of shared, meaningful, and activity distinguishing states, called Motion Units (MUs analog to phonemes in speech recognition.