Clustering of Human Activities from Wearables by Adopting Nearest Neighbors
Abrar Ahmed, Harish Haresamudram, Thomas Plötz · 2022
The ubiquitous availability of wearable sensing platforms renders recording activity data a straightforward endeavor. For many scenarios, however, obtaining accurate annotations for these sensor data can be infeasible due to practical constraints on logistics and costs, as well as concerns regarding privacy and ethics. Thus, we turn to unsupervised clustering which explores techniques which are capable of learning to group data in the absence of ground truth annotations. Our work focuses on learning to cluster movement data by exploiting the consistent patterns which we expect them to contain. This could allow vast amounts of unlabelled sensor data, which can be collected at scale, to be leveraged to bootstrap powerful classifiers. We adapt the Semantic Clustering by Adopting Nearest Neighbors (SCAN) framework to human activity recognition, which allows for the grouping of unlabelled windows of multi-sensor recordings based on the similarity of underlying movements. Using SCAN, we outperform the existing state-of-the-art unsupervised clustering technique for human activity recognition by clear margins on four diverse benchmark datasets.