Background Hand tremor typically has a negative impact on a person’s ability to complete many common daily activities. Previous research has investigated how to quantify hand tremor with smartphones and wearable sensors, mainly under controlled data collection conditions. Solutions for daily real-life settings remain largely underexplored. Objective Our objective was to monitor and assess hand tremor severity in patients with Parkinson disease (PD), and to better understand the effects of PD medications in a naturalistic environment. Methods Using the Welch method, we generated periodograms of accelerometer data and computed signal features to compare patients with varying degrees of PD symptoms. Results We introduced and empirically evaluated the tremor intensity parameter (TIP), an accelerometer-based metric to quantify hand tremor severity in PD using smartphones. There was a statistically significant correlation between the TIP and self-assessed Unified Parkinson Disease Rating Scale (UPDRS) II tremor scores (Kendall rank correlation test: z=30.521, P<.001, τ=0.5367379; n=11). An analysis of the “before” and “after” medication intake conditions identified a significant difference in accelerometer signal characteristics among participants with different levels of rigidity and bradykinesia (Wilcoxon rank sum test, P<.05). Conclusions Our work demonstrates the potential use of smartphone inertial sensors as a systematic symptom severity assessment mechanism to monitor PD symptoms and to assess medication effectiveness remotely. Our smartphone-based monitoring app may also be relevant for other conditions where hand tremor is a prevalent symptom.

Most off-the-shelf wearable devices do not provide reliable synchronization interfaces, causing multi-device sensing and machine learning approaches, e.g. for activity recognition, still to suffer from inaccurate clock sources and unmatched time. Instead of using active online synchronization techniques, such as those based on bidirectional wireless communication, we propose in this work to use the human heartbeat as a reference signal that is continuously and ubiquitously available throughout the entire body surface. We introduce PulSync, a novel approach that enables the alignment of sensor data across multiple devices utilizing the unique fingerprint-like character of the heart rate variability interval function. In an evaluation on a dataset from 25 subjects, we demonstrate the reliable alignment of independent ECG recordings with a mean accuracy of −0.71 ± 3.44 samples, respectively −2.86 ± 11.43 ms at 250 Hz sampling rate.

The optical measurement principle photoplethysmography has emerged in today's wearable devices as the standard to monitor the wearer's heart rate in everyday life. This cost-effective and easy-to-integrate technique has transformed from the original transmission mode pulse oximetry for clinical settings to the reflective mode of modern ambulatory, wrist-worn devices. Numerous proposed algorithms aim at the efficient heart rate measurement and accurate detection of the consecutive pulses for the derivation of secondary features from the heart rate variability. Most, however, have been evaluated either on own, closed recordings or on public datasets that often stem from clinical pulse oximeters in transmission instead of wearables' reflective mode. Signals tend furthermore to be preprocessed with filters, which are rarely documented and unintentionally fitted to the available and applied signals.We investigate the influence of preprocessing on the peak positions and present the benchmark of two cutting-edge pulse detection algorithms on actual raw measurements from reflective mode photoplethysmography. Based on 21806 pulse labels, our evaluation shows that the most suitable but still universal filter passband is located at 0.5 to 15.0 Hz since it preserves the required harmonics to shape the peak positions.

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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. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile devices; • Hardware → Digital signal processing.