Wearables are increasingly popular and are being accepted in healthcare applications, such as monitoring activity and heart rate. Like other wireless sensors, time synchronization is an important issue to ensure the credibility of data. While studies in this area are not uncommon, most methods try to synchronize the clock on individual devices and require access to the hardware and modification to the firmware. We consider a case for consumer wearables, where there is zero access to both firmware and hardware of the devices, except listening to the streaming data passively, and all synchronization can only be done on the receiving end. We present a two-part approach to synchronize streaming data received from wearable sensors. First, the fixed time offset due to device processing time is corrected by a one-time calibration. Second, the random time offset due to wireless communication is corrected by finetuning the sampling rate. Instead of adjusting the time on separate devices, the method attempts to align data from different devices to the accuracy of one sample time. The synchronization method also considers situations of data loss, congested Bluetooth channels, and multiple receiving hubs. With further interpolation on the received data, a synchronization accuracy of 1 ms is achieved. Unlike existing methods that rely on embedded timestamps, synchronized start events, or firmware modifications, our technique operates entirely on the receiver side, achieving single sample accuracy even under packet loss and clock drift.

Cuffless Blood Pressure (BP) monitoring using photoplethysmography (PPG) is attractive for continuous wearable assessment, however, it is highly susceptible to motion artifacts and poor signal quality. We propose a simple and interpretable multi-stage signal-quality (SQ) framework that embeds three sequential quality checks directly into the PPG → BP estimation pipeline: (1) raw-signal metrics (signal jitters, zero crossings), (2) beat-level plausibility (peak-to-peak amplitude and inter-beatinterval changes), and (3) multi-sensor consistency (pulse arrival time variability). Each detected pulse is labelled good / bad, aggregated over a one-minute window, and gated by a tunable Good Signal Threshold (GST). GST was calibrated on a small test set, and GST$=0.7$was chosen because it retained 99 % of stationary windows while rejecting 90 % of motion-contaminated windows. In a feasibility study with subjects undergoing oneday measurements of 24 hours, PPG-derived heart rate (HR) was compared against ECG ground truth. Ablation analysis showed incremental benefits from each SQ stages: during sleep, error reduced from 2.08 bpm to 1.22 bpm with 80 % coverage; while during daytime activity, error decreased from 3.94 bpm to 1.69 bpm, albeit with reduced coverage of 50 %. These results confirm that the SQ framework substantially improves pulse fidelity, achieving$40-55 \%$error reduction, while maintaining interpretable design and low computational cost. The proposed approach is thus well-suited for low-power wearable systems and provides a practical foundation for cuffless BP monitoring.

In this paper, we explore the application of cooperative communications in ultra-wideband (UWB) wireless body area networks (BANs), where a group of on-body devices may collaborate together to communicate with other groups of on-body equipment. Firstly, time-domain UWB channel measurements are presented to characterize the body-centric multipath channel and to facilitate the diversity analysis in a cooperative BAN (CoBAN). We focus on the system deployment scenario when the human subject is in the sitting posture. Important channel parameters such as the pathloss, power variation, power delay profile (PDP), and effective received power (ERP) crosscorrelation are investigated and statistically analyzed. Provided with the model preliminaries, a detailed analysis on the diversity level in a CoBAN is provided. Specifically, an intuitive measure is proposed to quantify the diversity gains in a single-hop cooperative network, which is defined as the number of independent multipaths that can be averaged over to detect symbols. As this measure provides the largest number of redundant copies of transmitted information through the body-centric channel, it can be used as a benchmark to access the performance bound of various diversity-based cooperative schemes in futuristic body sensor systems.