An Overview of Wearable Photoplethysmographic Sensors and Various Algorithms for Tracking of Heart Rates

Obi, Amarachukwu Ikechukwu

doi:10.3390/engproc2021010077

Cited by 5 publications

(4 citation statements)

References 42 publications

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“…Measuring and estimating HR from PPG signals during intense physical activity is challenging because it requires high accuracy. The PPG technique measured HR because it makes the device small and easy to use [25]. The PPG principle is that the skin surface is illuminated by light from a light emitting diode (LED), infrared or red LED.…”

Section: Pulse Sensormentioning

confidence: 99%

Real-time stress detection and monitoring system using IoT-based physiological signals

et al. 2023

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Currently, medical experts use psychophysiological questionnaires to evaluate human stress levels during counseling or interviews. Typically, biochemical samples use urine, saliva, and blood samples to identify the effects of stress on the human body. This research explains that stress detection can be done by analyzing psychological signals and the importance of monitoring stress levels. The authors develop research on stress detection based on psychological signals. The system then processes the recorded data; the android application displays the calculation results. The database can also be accessed as a spreadsheet via a web application. The design of real-time stress detection and monitoring using internet of things (IoT) can work well.

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Section: Pulse Sensormentioning

confidence: 99%

Real-time stress detection and monitoring system using IoT-based physiological signals

et al. 2023

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“…A HR signal is composed from consecutive beat-to-beat intervals of the human heart [43,44]. As such, the HR is the most widely measured physiological signal [45,46]. The beat-to-beat intervals are usually extracted from either an ECG or photoplethysmogram (PPG) signals [47].…”

Section: Heart Rate (Hr)mentioning

confidence: 99%

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

et al. 2022

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Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment.

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“…The amount of light received by the photodetector is strongly affected by tissue vascular characteristics and is inversely proportional to blood volume, which varies according to the cardiac cycle, creating the AC-like waveform of the PPG signal [ 1 ]. PPG also contains a quasi-DC component, influenced by respiration, sympathetic nervous system activity, and thermoregulation [ 1 , 4 , 9 , 10 ]. Both components can be further analyzed after appropriate filtering in order to extract health-related indices [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

Vraka,

Zangróniz,

Quesada

et al. 2023

Sensors

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Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted signal often remains unexploited. This work introduces a novel method able to reconstruct noisy PPGs and facilitate uninterrupted health monitoring. The algorithm starts with spectral-based MA detection, followed by signal reconstruction by using the morphological and heart-rate variability information from the clean segments adjacent to noise. The algorithm was tested on (a) 30 noisy PPGs of a maximum 20 s noise duration and (b) 28 originally clean PPGs, after noise addition (2–120 s) (1) with and (2) without cancellation of the corresponding clean segment. Sampling frequency was 250 Hz after resampling. Noise detection was evaluated by means of accuracy, sensitivity, and specificity. For the evaluation of signal reconstruction, the heart-rate (HR) was compared via Pearson correlation (PC) and absolute error (a) between ECGs and reconstructed PPGs and (b) between original and reconstructed PPGs. Bland-Altman (BA) analysis for the differences in HR estimation on original and reconstructed segments of (b) was also performed. Noise detection accuracy was 90.91% for (a) and 99.38–100% for (b). For the PPG reconstruction, HR showed 99.31% correlation in (a) and >90% for all noise lengths in (b). Mean absolute error was 1.59 bpm for (a) and 1.26–1.82 bpm for (b). BA analysis indicated that, in most cases, 90% or more of the recordings fall within the confidence interval, regardless of the noise length. Optimal performance is achieved even for signals of noise up to 2 min, allowing for the utilization and further analysis of recordings that would otherwise be discarded. Thereby, the algorithm can be implemented in monitoring devices, assisting in uninterrupted health-tracking.

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An Overview of Wearable Photoplethysmographic Sensors and Various Algorithms for Tracking of Heart Rates

Cited by 5 publications

References 42 publications

Real-time stress detection and monitoring system using IoT-based physiological signals

Real-time stress detection and monitoring system using IoT-based physiological signals

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

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