IBSync: Intra-body Synchronization of Wearable Devices Using Artificial ECG Landmarks

The synchronization of wearable devices in distributed, multi-device systems is a persistent challenge. Particularly machine learning approaches suffer from the devices' inaccurate clock sources and unmatched time. While the online synchronization based on radio transmission is energy-intensive, offline approaches originated in activity recognition suffer from inaccurate motion patterns. In recent years, intra-body communication emerged as a promising technique that uses the human body as a limited and hence more efficient medium. Due to the absence of commercial platforms, applications are rare and underinvestigated. To boost their development and to enable the precise synchronization, we introduce IBSync and propose to repurpose the ECG sensor in commercial wearable devices to detect artificial signals induced into the skin. The shorttime Fourier transform and Pearson's normalized cross-correlation are used to detect, precisely locate, and assign synchronization landmarks within the measurements. Based on a total of 105 min of recordings, we evaluated the concept and demonstrate its general feasibility with a promising accuracy of 0.203 +/- 1.633 samples (1.587 +/- 12.755 ms) in typical proximity to the transmitter.