  While traditional feature engineering for Human Activity Recognition (HAR)
involves a trial-anderror process, deep learning has emerged as a preferred
method for high-level representations of sensor-based human activities.
However, most deep learning-based HAR requires a large amount of labelled data
and extracting HAR features from unlabelled data for effective deep learning
training remains challenging. We, therefore, introduce a deep semi-supervised
HAR approach, MixHAR, which concurrently uses labelled and unlabelled
activities. Our MixHAR employs a linear interpolation mechanism to blend
labelled and unlabelled activities while addressing both inter- and
intra-activity variability. A unique challenge identified is the
activityintrusion problem during mixing, for which we propose a mixing
calibration mechanism to mitigate it in the feature embedding space.
Additionally, we rigorously explored and evaluated the five
conventional/popular deep semi-supervised technologies on HAR, acting as the
benchmark of deep semi-supervised HAR. Our results demonstrate that MixHAR
significantly improves performance, underscoring the potential of deep
semi-supervised techniques in HAR.
