Optimal Preprocessing of Raw Signals from Reflective Mode Photoplethysmography in Wearable Devices.

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 15.0Hz since it preserves the required harmonics to shape the peak positions.

Smartphone-Based Monitoring of Parkinson Disease: Quasi-Experimental Study to Quantify Hand Tremor Severity and Medication Effectiveness.

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.