Advancing Location-Invariant and Device-Agnostic Motion Activity
  Recognition on Wearable Devices
Rebecca Adaimi Abdelkareem Bedri Jun Gong Richard Kang Joanna Arreaza-Taylor Gerri-Michelle Pascual Michael Ralph Gierad Laput
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.