  Accelerometer-based (and by extension other inertial sensors) research for
Human Activity Recognition (HAR) is a dead-end. This sensor does not offer
enough information for us to progress in the core domain of HAR - to recognize
everyday activities from sensor data. Despite continued and prolonged efforts
in improving feature engineering and machine learning models, the activities
that we can recognize reliably have only expanded slightly and many of the same
flaws of early models are still present today. Instead of relying on
acceleration data, we should instead consider modalities with much richer
information - a logical choice are images. With the rapid advance in image
sensing hardware and modelling techniques, we believe that a widespread
adoption of image sensors will open many opportunities for accurate and robust
inference across a wide spectrum of human activities.
  In this paper, we make the case for imagers in place of accelerometers as the
default sensor for human activity recognition. Our review of past works has led
to the observation that progress in HAR had stalled, caused by our reliance on
accelerometers. We further argue for the suitability of images for activity
recognition by illustrating their richness of information and the marked
progress in computer vision. Through a feasibility analysis, we find that
deploying imagers and CNNs on device poses no substantial burden on modern
mobile hardware. Overall, our work highlights the need to move away from
accelerometers and calls for further exploration of using imagers for activity
recognition.
