Proximity beacons provide simple, low-cost location data. However, beacon deployments remain rare. In this paper we introduce Bellrock, a framework that repurposes static personal devices (phones, laptops, etc.) as proximity beacons without revealing the location of the device owners, and provides conventional beacons with access control. This is done by using mutable pseudo-anonymous identifiers that can be unmasked by a cloud service. We develop Bellrock as a general framework, describing the repurposing scheme and the anonymisation techniques, before applying it to Bluetooth Low Energy beacons. We implement and demonstrate the scalability of the de-anonymisation server, which uses a series of heuristics. We implement a Bellrock client on Android and demonstrate negligible impact on battery lifetime. We evaluate Bellrock using extensive real-world office worker movements. We find that Bellrock was able to provide proximity locations for 8,542 of the 21,796 failed locations that would have occurred without it. We further find that office workers were in range of one or more of their co-workers over 90% of the time, indicating Bellrock can provide relative proximity information even in the absence of a conventional beacon deployment. Overall, we find that Bellrock is both feasible and practical, providing a beacon deployment where there was none, or supplementing existing deployments.

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. 1

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