
Many people now carry multiple mobile devices on a daily basis. Wearables, smartphones, tablets, and laptops all have their different advantages, but collectively they can increase a user's device management burden. Management problems include leaving a device behind accidentally, receiving notifications on the wrong device, and failing to secure all of the devices as needed. Reducing this burden requires detecting which of a user's devices are "personally collocated" -- those devices he currently wears, carries, or has under his immediate physical control. We present a lightweight method to detect personal collocation by comparing accelerometer-based footstep signatures across the devices over time. Through several experiments, we demonstrate that the technique is lower latency and lower power than state-of-the-art RSSI-based collocation techniques. We describe other advantages and limitations of our method and also provide several examples of higher-layer applications and services that can make use of personal collocation information.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 12 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
