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The extensive ubiquitous availability of sensors in smart devices and theInternet of Things (IoT) has opened up the possibilities for implementingsensor-based activity recognition. As opposed to traditional sensor time-seriesprocessing and hand-engineered feature extraction, in light of deep learning'sproven effectiveness across various domains, numerous deep methods have beenexplored to tackle the challenges in activity recognition, outperforming thetraditional signal processing and traditional machine learning approaches. Inthis work, by performing extensive experimental studies on two human activityrecognition datasets, we investigate the performance of common deep learningand machine learning approaches as well as different training mechanisms (suchas contrastive learning), and various feature representations extracted fromthe sensor time-series data and measure their effectiveness for the humanactivity recognition task.

Investigating Deep Neural Network Architecture and Feature Extraction Designs for Sensor-based Human Activity Recognition