BMAR: Barometric and Motion-based Alignment and Refinement for Offline Signal Synchronization across Devices

TL;DR

BMAR is a novel offline synchronization method for wearable devices that uses barometric and motion signals to accurately align data without communication or user input, achieving median errors around 33 ms in active scenarios.

Contribution

BMAR introduces a device-only, offline synchronization approach leveraging barometric and acceleration data, eliminating the need for wireless communication or user intervention.

Findings

Median synchronization error of 33.4 ms during activities

Reliable rejection of non-overlapping traces

Error increases to 3.06 s during sleep activities

Abstract

A requirement of cross-modal signal processing is accurate signal alignment. Though simple on a single device, accurate signal synchronization becomes challenging as soon as multiple devices are involved, such as during activity monitoring, health tracking, or motion capture - particularly outside controlled scenarios where data collection must be standalone, low-power, and support long runtimes. In this paper, we present BMAR, a novel synchronization method that operates purely based on recorded signals and is thus suitable for offline processing. BMAR needs no wireless communication between devices during runtime and does not require any specific user input, action, or behavior. BMAR operates on the data from devices worn by the same person that record barometric pressure and acceleration - inexpensive, low-power, and thus commonly included sensors in today's wearable devices. In its first stage, BMAR verifies that two recordings were acquired simultaneously and pre-aligns all data traces. In a second stage, BMAR refines the alignment using acceleration measurements while accounting for clock skew between devices. In our evaluation, three to five body-worn devices recorded signals from the wearer for up to ten hours during a series of activities. BMAR synchronized all signal recordings with a median error of 33.4 ms and reliably rejected non-overlapping signal traces. The worst-case activity was sleeping, where BMAR's second stage could not exploit motion for refinement and, thus, aligned traces with a median error of 3.06 s.