Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices
This addresses the problem of sensing heterogeneities for practitioners in HCI and ubiquitous computing, offering a more generalizable solution.
The paper tackles the challenge of motion activity recognition across different sensor placements on wearable devices by introducing a large multi-location dataset and a single model achieving 91.41% F1-score, regardless of location.
Wearable sensors have permeated into people's lives, ushering impactful applications in interactive systems and activity recognition. However, practitioners face significant obstacles when dealing with sensing heterogeneities, requiring custom models for different platforms. In this paper, we conduct a comprehensive evaluation of the generalizability of motion models across sensor locations. Our analysis highlights this challenge and identifies key on-body locations for building location-invariant models that can be integrated on any device. For this, we introduce the largest multi-location activity dataset (N=50, 200 cumulative hours), which we make publicly available. We also present deployable on-device motion models reaching 91.41% frame-level F1-score from a single model irrespective of sensor placements. Lastly, we investigate cross-location data synthesis, aiming to alleviate the laborious data collection tasks by synthesizing data in one location given data from another. These contributions advance our vision of low-barrier, location-invariant activity recognition systems, catalyzing research in HCI and ubiquitous computing.