There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent developments in wearable pulse rate sensors with green LEDs. The application of wearable pulse rate monitors is discussed.

Wearable Photoplethysmographic Sensors—Past and Present

Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time (PTT) represents a well-known potential approach for ubiquitous BP monitoring. The goal of this review is to facilitate the achievement of reliable ubiquitous BP monitoring via PTT. We explain the conventional BP measurement methods and their limitations; present models to summarize the theory of the PTT-BP relationship; outline the approach while pinpointing the key challenges; overview the previous work toward putting the theory to practice; make suggestions for best practice and future research; and discuss realistic expectations for the approach.

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Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice.

Heart rate monitoring using wrist-type photoplethysmographic signals during subjects’ intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects’ hand movements. So far few works have studied this problem. In this study, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/h showed that the average absolute error of heart rate estimation was 2.34 beat per minute, and the Pearson correlation between the estimates and the ground truth of heart rate was 0.992. This framework is of great values to wearable devices such as smartwatches which use PPG signals to monitor heart rate for fitness.

TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise

Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes. PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation. Recently, there has been much interest from numerous researchers around the globe to extract further valuable information from the PPG signal in addition to heart rate estimation and pulse oxymetry readings. PPG signal's second derivative wave contains important health-related information. Thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness. Moreover, investigating the second derivative wave of PPG signal can also assist in early detection and diagnosis of various cardiovascular illnesses that may possibly appear later in life. For early recognition and analysis of such illnesses, continuous and real-time monitoring is an important approach that has been enabled by the latest technological advances in sensor technology and wireless communications. The aim of this article is to briefly consider some of the current developments and challenges of wearable PPG-based monitoring technologies and then to discuss some of the potential applications of this technology in clinical settings.

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A review on wearable photoplethysmography sensors and their potential future applications in health care.

The performance of pulse oximeters is highly influenced by motion artifacts (MAs) in photoplethysmographic (PPG) signals. In this paper, we propose a simple and efficient approach based on adaptive step-size least mean squares (AS-LMS) adaptive filter for reducing MA in corrupted PPG signals. The presented method is an extension to our prior work on efficient use of adaptive filters for reduction of MA in PPG signals. The novelty of the method lies in the fact that a synthetic noise reference signal for an adaptive filtering process, representing MA noise, is generated internally from the MA-corrupted PPG signal itself instead of using any additional hardware such as accelerometer or source-detector pair for acquiring noise reference signal. Thus, the generated noise reference signal is then applied to the AS-LMS adaptive filter for artifact removal. While experimental results proved the efficacy of the proposed scheme, the merit of the method is clearly demonstrated using convergence and correlation analysis, thus making it best suitable for present-day pulse oximeters utilizing PPG sensor head with a single pair of source and detector, which does not have any extra hardware meant for capturing noise reference signal. In addition to arterial oxygen saturation estimation, the artifact reduction method facilitated the waveform contour analysis on artifact-reduced PPG, and the conventional parameters were evaluated for assessing the arterial stiffness.

A Novel Approach for Motion Artifact Reduction in PPG Signals Based on AS-LMS Adaptive Filter

Rapid urbanization has given us many benefits in terms of giving us a better standard of life, but it sure has brought a lot of problems with it. Solid waste management is a major issue that has been in the forefront of the issues caused by urbanization. It has brought a lot of domains together namely social, environmental, and climate together. Technology has been used occasionally but has not made significant advances in this domain. This domain is relatively new in terms of deep learning. This is due to some of the issues like lack of proper dataset, effective architecture to classify multiple objects and so on. The goal is to build multi-class dataset in this domain and perform detection and classification using both single stage and two stage object detection networks. The single stage network that is to be implemented is YOLOv5 and the two stage network that is to be implemented is Faster Region based CNN using Resnet50. The single object dataset used is TrashNet dataset and the multi-object dataset used is Waste-mart self-built dataset. The result obtained shows mAP around 0.84 for the two-stage network and mAP around 0.98 with IoU threshold placed at 0.5 for both the systems. 


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M. R. Ram

Papers

A Novel Approach for Motion Artifact Reduction in PPG Signals Based on AS-LMS Adaptive Filter

Multi Object Detection and Classification in Solid Waste Management using Region Proposal Network and YOLO model