Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study

Abstract

Background Smartphone-based blood pressure (BP) monitoring using photoplethysmography (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. Objective This study aimed to develop a mobile personal health care system for noninvasive, pervasive, and continuous estimation of BP level and variability, which is user friendly for elderly people. 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 app using multilayer perceptron machine learning techniques from raw signals. We performed a development and usability study with three older adults (mean age 61.3 years, SD 1.5 years; 66% women) to test the usability and accuracy of the smartphone-based BP monitor. Results The employed artificial neural network model had good average accuracy (>90%) and very strong correlation (>0.90) (P<.001) for predicting the reference BP values of our validation sample (n=150). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. However, according to the Association for the Advancement of Medical Instrumentation and British Hypertension Society standards, only diastolic blood pressure prediction met the clinically accepted accuracy thresholds. Conclusions With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of health care, particularly in rural zones, areas lacking physicians, and areas with solitary elderly populations.

Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management

“…As the mask directly contacts the facial skin, a photoplethysmographic (PPG) sensor can be adopted to conduct pulse oximetry and measure heart rate variability, oxygen saturation, and blood pressure. These metrics are widely researched for indirect measures of physical and mental health [ 49 - 51 ], physical stress [ 31 , 49 ], and hypertension or hypotension [ 51 - 53 ], respectively. Electrooculography (EOG) [ 54 , 55 ], electrodermal activity (EDA) [ 56 , 57 ], and electromyography (EMG) [ 50 , 52 , 53 ] can also be adopted to measure various biophysical signals that arise from facial skin.…”

“…These metrics are widely researched for indirect measures of physical and mental health [ 49 - 51 ], physical stress [ 31 , 49 ], and hypertension or hypotension [ 51 - 53 ], respectively. Electrooculography (EOG) [ 54 , 55 ], electrodermal activity (EDA) [ 56 , 57 ], and electromyography (EMG) [ 50 , 52 , 53 ] can also be adopted to measure various biophysical signals that arise from facial skin. For example, eye-blinking EOG measures have been linked to attention [ 58 ] and may infer the user’s mental state.…”

Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management (Preprint)

“…As the mask directly contacts the facial skin, a photoplethysmographic (PPG) sensor can be adopted to conduct pulse oximetry and measure heart rate variability, oxygen saturation, and blood pressure. These metrics are widely researched for indirect measures of physical and mental health [49][50][51], physical stress [31,49], and hypertension or hypotension [51][52][53], respectively. Electrooculography (EOG) [54,55], electrodermal activity (EDA) [56,57], and electromyography (EMG) [50,52,53] can also be adopted to measure various biophysical signals that arise from facial skin.…”

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

“…Their proposed pressure sensor has the potential for low production costs and integration into wearable devices, yet this technology needs time to mature until it reaches a pervasive computing status. Mena et al developed a mobile system for non-invasive, continuous BP monitoring [ 14 ]. Their system actively collected and transmitted PPG readings from a wrist-mounted sensor to a smartphone, where BP estimation is computed with machine-learning algorithms.…”