Mobile Personal Healthcare System for Non-Invasive, Pervasive and Continuous Blood Pressure Monitoring: A Feasibility Study

Background: Smartphone-based blood pressure (BP) monitor using photoplethysmogram (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control ofhypertension (HT). Objective: This study aimed to develop a mobile personal healthcare system for non-invasive, pervasive, and continuous estimation of BP level and variability to be user-friendly to elderly. Methods: The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless and wearable PPG-only sensor, and a native purposely-designed smartphone application using multilayer perceptron machine learning techniques from raw signals. We performed a pilot study with three elder adults (mean age 61.3 ± 1.5 years; 66% women) to test usability and accuracy of the smartphone-based BP monitor. Results: The employed artificial neural network (ANN) model performed with high accuracy in terms of predicting the reference BP values of our validation sample (n=150). On average, our approach predicted BP measures with accuracy \u3e90% and correlations \u3e0.90 (P \u3c .0001). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. Conclusions: With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of healthcare, particularly in rural zones, areas lacking physicians, and solitary elderly populations

Measuring 2D:4D finger length ratios with Smartphone Cameras

Finger length ratios have received much attention among researchers as the 2D:4D ratio has been linked to several physical and mental characteristics. This study explores the feasibility of using a Smartphone as an instrument for measuring finger length ratios. The approach taken in this study is to use the Smartphone camera to take freehand photos of the hand which is subsequently subjected to image analysis. Measurement procedures include hand near and far from the body, palms up or down, or hands in mid air versus hands resting on a flat surface. Experimental evaluations show that the most accurate measurements are achieved by resting the hand on a surface with the palm facing up. These results are comparable to those achieved with conventional procedures with an error of 1%

Investigation of Five Algorithms for Selection of the Optimal Region of Interest in Smartphone Photoplethysmography

Smartphone photoplethysmography is a newly developed technique that can detect several physiological parameters from the photoplethysmographic signal obtained by the built-in camera of a smartphone. It is simple, low-cost, and easy-to-use, with a great potential to be used in remote medicine and home healthcare service. However, the determination of the optimal region of interest (ROI), which is an important issue for extracting photoplethysmographic signals from the camera video, has not been well studied. We herein proposed five algorithms for ROI selection: variance (VAR), spectral energy ratio (SER), template matching (TM), temporal difference (TD), and gradient (GRAD). Their performances were evaluated by a 50-subject experiment comparing the heart rates measured from the electrocardiogram and those from the smartphone using the five algorithms. The results revealed that the TM and the TD algorithms outperformed the other three as they had less standard error of estimate (<1.5 bpm) and smaller limits of agreement (<3 bpm). The TD algorithm was slightly better than the TM algorithm and more suitable for smartphone applications. These results may be helpful to improve the accuracy of the physiological parameters measurement and to make the smartphone photoplethysmography technique more practical

Open Source Quantitative Stress Prediction Leveraging Wearable Sensing and Machine Learning Methods

The ability to monitor physiological parameters in an individual is paramount for the evaluation of physical health and the detection of many ailments. Wearable technologies are being introduced on a widening scale to address the absence of low-cost and non-invasive health monitoring as compared to medical grade equipment and technologies. By leveraging wearable technologies to supplement or replace traditional gold-standard measurement techniques, the research community can develop a deeper multifaceted understanding of the relationship between specific physiological parameters and particular health conditions. One particular research area in which wearable technologies are beginning to see application is the quantification of physical and mental stress levels in individuals through brainwave and physiological feature monitoring. At present, these methods are time consuming, invasive, expensive, or some combination of the three. This thesis chronicles the development and application of a novel open source wearable sensing platform to the field of stress and fatigue estimation and quantization. More specifically, the garment in its current configuration monitors heart rate, blood oxygen saturation, skin temperature, respiration rate, and skin conductivity parameters to explore the relationship between these parameters and various self-reported stress measures. Utilizing machine-learning methods, subject-specific models were generated in an n=1 study which predicts the self-perceived stress level of the subject with an accuracy of between 92% and 100%. The garment was developed with a modular interface and open source code base to allow and encourage reconfiguration and customization of the sensor array for other research applications. The dataset generated in this effort spans the early stages of the COVID-19 pandemic as the subject experienced increasing levels of isolation and tracks physiological parameters across two months via daily measurements