The quest for raw signals

Abstract

Photoplethysmography is an optical measurement principle which is present in most modern wearable devices such as fitness trackers and smartwatches. As the analysis of physiological signals requires reliable but energy-efficient algorithms, suitable datasets are essential for their development, evaluation, and benchmark. A broad variety of clinical datasets is available with recordings from medical pulse oximeters which traditionally apply transmission mode photoplethysmography at the fingertip or earlobe. However, only few publicly available datasets utilize recent reflective mode sensors which are typically worn at the wrist and whose signals show different characteristics. Moreover, the recordings are often advertised as raw, but then turn out to be preprocessed and filtered while the applied parameters are not stated. In this way, the heart rate and its variability can be extracted, but interesting secondary information from the non-stationary signal is often lost. Consequently, the test of novel signal processing approaches for wearable devices usually implies the gathering of own or the use of inappropriate data. In this paper, we present a multi-varied method to analyze the suitability and applicability of presumably raw photoplethysmography signals. We present an analytical tool which applies 7 decision metrics to characterize 10 publicly available datasets with a focus on less or ideally unfiltered, raw signals. Besides the review, we finally provide a guideline for future datasets, to suit to and to be applicable in digital signal processing, to support the development and evaluation of algorithms for resource-limited wearable devices. CCS CONCEPTS • Human-centered computing → Ubiquitous and mobile devices; • Hardware → Digital signal processing; • Applied computing → Health informatics.

Novel Multi-Parametric Sensor System for Comprehensive Multi-Wavelength Photoplethysmography Characterization

“…The proposed system offers unprocessed raw data, crucial for research purposes, as previous studies have emphasized [ 45 ]. Raw data are essential for testing new signal processing approaches, as many recordings available in databases and advertised as raw signals have in fact been pre-processed and filtered, resulting in the loss of valuable secondary information.…”