  In this study, we investigate a new self-supervised learning (SSL) approach
for complex work activity recognition using wearable sensors. Owing to the cost
of labeled sensor data collection, SSL methods for human activity recognition
(HAR) that effectively use unlabeled data for pretraining have attracted
attention. However, applying prior SSL to complex work activities such as
packaging works is challenging because the observed data vary considerably
depending on situations such as the number of items to pack and the size of the
items in the case of packaging works. In this study, we focus on sensor data
corresponding to characteristic and necessary actions (sensor data motifs) in a
specific activity such as a stretching packing tape action in an assembling a
box activity, and \textcolor{black}{try} to train a neural network in
self-supervised learning so that it identifies occurrences of the
characteristic actions, i.e., Motif Identification Learning (MoIL). The feature
extractor in the network is used in the downstream task, i.e., work activity
recognition, enabling precise activity recognition containing characteristic
actions with limited labeled training data. The MoIL approach was evaluated on
real-world work activity data and it achieved state-of-the-art performance
under limited training labels.
