Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

As a new therapeutic technique based on digital technology, the commercialization and clinical application of digital therapeutics (DTx) are increasing, and the demand for expansion to new clinical fields is remarkably high. However, the use of DTx as a general medical component is still ambiguous, and this ambiguity may be owing to a lack of consensus on a definition, in addition to insufficiencies in research and development, clinical trials, standardization of regulatory frameworks, and technological maturity. In this study, we conduct an in-depth investigation and analysis of definitions, clinical trials, commercial products, and the regulatory status related to DTx using published literature, ClinicalTrials.gov, and web pages of regulatory and private organizations in several countries. Subsequently, we suggest the necessity and considerations for international agreements on the definition and characteristics of DTx, focusing on the commercialization characteristics. In addition, we discuss the status and considerations of clinical research, key technology factors, and the direction of regulatory developments. In conclusion, for the successful settlement of DTx, real-world evidence-based validation should be strengthened by establishing a cooperative system between researchers, manufacturers, and governments, and there should be effective technologies and regulatory systems for overcoming engagement barriers of DTx.

This research presents the ultrasonic proximity sensor approach to respiration measurement. The ultrasonic proximity sensor measures respiration signatures and rates in real-time and for long-term monitoring, which is necessary for mobility from the end-user perspective. The study used a 240 kHz ultrasonic sensor to measure the time of flight of a sound wave between the transmitted signal and received signal during respiration in the abdominal wall-motion. Respiration rates measured with the ultrasonic proximity sensor were then compared with those measured with a thermocouple sensor on ten male subjects. Data from the measurement of respiration rates at 100 cm is provided. We have used this data from the method comparison study to confirm agreement with the reference signal to determine that the current version of respiratory rate detection system using ultrasonic can successfully measure respiration rates. The proposed respiratory measurement method could be used to monitor an unconscious person without the need to apply electrodes or other sensors in the correct position and to wire the subject to the system. Monitoring respiration using ultrasonic sensor offers a promising possibility of noncontact measurement of respiration rates. In particular, this technology offers a potentially inexpensive means to extend applications to consumer home-healthcare and mobile-healthcare products. Further advances in the sensor design, system design and signal processing can increase the range and quality of the measurement, broadening the potential application areas of this technology.

Pulse rate variability (PRV) analysis appears as the first alternative to heart rate variability analysis for wearable devices; however, there is a constraint on computational load and energy consumption for the limited system resources available to the devices. Considering that adjustment of the sampling frequency is one of the strategies for reducing computational load and power consumption, this study aimed to investigate the influence of sampling frequency (f s) on PRV analysis and to find the minimum sampling frequency while maintaining reliability. We generated 5000, 2500, 1000, 500, 250, 100, 50, 25, 20, 15, 10, 5 Hz down-sampled photoplethysmograms from 10 kHz-sampled PPGs and derived time- and frequency-domain variables of the PRV. These included AVNN, SDNN, SDSD, RMSSD, NN50, pNN50, total power, VLF, LF, HF, LF/HF, nLF and nHF for each down-sampled signal. Derived variables were compared with heart rate variability of the 10 kHz-sampled electrocardiograms, and then statistically investigated using one-way ANOVA test and Bland-Altman analysis. As a result, significant differences (P < 0.05) were found for SDNN, SDSD, RMSSD, NN50, pNN50, TP, HF, LF/HF, nLF and nHF, but not for AVNN, VLF and LF. Based on the post hoc tests, it was found that the NN50 and pNN50, SDSD and RMSSD, LF/HF and nHF, SDNN, TP and nLF analysis had significant differences at f s ⩽ 20 Hz, f s ⩽ 15 Hz, f s ⩽10 Hz; f s = 5 Hz, respectively. In other words, a significant difference was not seen for any variable if the f s was greater than 25 Hz. Consequently, our pilot study suggests that analysis of variability in the time and frequency domain from pulse rate obtained through PPG may be potentially as reliable as that derived from the analysis of the electrocardiogram, provided that f s ⩾25 Hz sampling frequency is used.