Non-Contact HR Monitoring via Smartphone and Webcam During Different Respiratory Maneuvers and Body Movements

Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care

BACKGROUND

Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized.

OBJECTIVE

This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis.

METHODS

We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences.

RESULTS

The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies.

CONCLUSIONS

While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care.

Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement

In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.

Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review

In recent years, noncontact measurements of vital signs using cameras received a great amount of interest. However, some questions are unanswered: (i) Which vital sign is monitored using what type of camera? (ii) What is the performance and which factors affect it? (iii) Which health issues are addressed by camera-based techniques? Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, we conduct a systematic review of continuous camera-based vital sign monitoring using Scopus, PubMed, and the Association for Computing Machinery (ACM) databases. We consider articles that were published between January 2018 and April 2021 in the English language. We include five vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), body skin temperature (BST), and oxygen saturation (SpO2). In total, we retrieve 905 articles and screened them regarding title, abstract, and full text. One hundred and four articles remained: 60, 20, 6, 2, and 1 of the articles focus on HR, RR, BP, BST, and SpO2, respectively, and 15 on multiple vital signs. HR and RR can be measured using red, green, and blue (RGB) and near-infrared (NIR) as well as far-infrared (FIR) cameras. So far, BP and SpO2 are monitored with RGB cameras only, whereas BST is derived from FIR cameras only. Under ideal conditions, the root mean squared error is around 2.60 bpm, 2.22 cpm, 6.91 mm Hg, 4.88 mm Hg, and 0.86 °C for HR, RR, systolic BP, diastolic BP, and BST, respectively. The estimated error for SpO2 is less than 1%, but it increases with movements of the subject and the camera-subject distance. Camera-based remote monitoring mainly explores intensive care, post-anaesthesia care, and sleep monitoring, but also explores special diseases such as heart failure. The monitored targets are newborn and pediatric patients, geriatric patients, athletes (e.g., exercising, cycling), and vehicle drivers. Camera-based techniques monitor HR, RR, and BST in static conditions within acceptable ranges for certain applications. The research gaps are large and heterogeneous populations, real-time scenarios, moving subjects, and accuracy of BP and SpO2 monitoring.

Multi-ROI Spectral Approach for the Continuous Remote Cardio-Respiratory Monitoring from Mobile Device Built-In Cameras

Heart rate (HR) and respiratory rate (f_R) can be estimated by processing videos framing the upper body and face regions without any physical contact with the subject. This paper proposed a technique for continuously monitoring HR and f_R via a multi-ROI approach based on the spectral analysis of RGB video frames recorded with a mobile device (i.e., a smartphone's camera). The respiratory signal was estimated by the motion of the chest, whereas the cardiac signal was retrieved from the pulsatile activity at the level of right and left cheeks and forehead. Videos were recorded from 18 healthy volunteers in four sessions with different user-camera distances (i.e., 0.5 m and 1.0 m) and illumination conditions (i.e., natural and artificial light). For HR estimation, three approaches were investigated based on single or multi-ROI approaches. A commercially available multiparametric device was used to record reference respiratory signals and electrocardiogram (ECG). The results demonstrated that the multi-ROI approach outperforms the single-ROI approach providing temporal trends of both the vital parameters comparable to those provided by the reference, with a mean absolute error (MAE) consistently below 1 breaths·min^-1 for f_R in all the scenarios, and a MAE between 0.7 bpm and 6 bpm for HR estimation, whose values increase at higher distances.

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

The measurement of physiological parameters is fundamental to assess the health status of an individual. The contactless monitoring of vital signs may provide benefits in various fields of application, from healthcare and clinical setting to occupational and sports scenarios. Recent research has been focused on the potentiality of camera-based systems working in the visible range (380-750 nm) for estimating vital signs by capturing subtle color changes or motions caused by physiological activities but invisible to human eyes. These quantities are typically extracted from videos framing some exposed body areas (e.g., face, torso, and hands) with adequate post-processing algorithms. In this review, we provided an overview of the physiological and technical aspects behind the estimation of vital signs like respiratory rate, heart rate, blood oxygen saturation, and blood pressure from digital images as well as the potential fields of application of these technologies. Per each vital sign, we provided the rationale for the measurement, a classification of the different techniques implemented for post-processing the original videos, and the main results obtained during various applications or in validation studies. The available evidence supports the premise of digital cameras as an unobtrusive and easy-to-use technology for physiological signs monitoring. Further research is needed to promote the advancements of the technology, allowing its application in a wide range of population and everyday life, fostering a biometrical holistic of the human body (BHOHB) approach.

Contactless Heart Rate Variability (HRV) Estimation Using a Smartphone During Respiratory Maneuvers and Body Movement

Heart rate variability (HRV) has been extensively investigated as a noninvasive marker to evaluate the functionality of the autonomic nervous system (ANS). Many studies have provided photoplethysmography (PPG) as a surrogate for electrocardiogram (ECG) signal HRV measurements. Remote PPG (rPPG) has been also investigated for pulse rate variability (PRV) estimation but in controlled conditions. We remotely extracted PRV using a smartphone camera for subjects in static and lateral motion while their respiratory rate was set to three breathing rates in an indoor illumination environment. PRV was compared with ECG-based HRV as a gold standard. We tested our algorithms on five healthy subjects. The results showed high correlation for rPPG-based HRV by presenting means of standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive heartbeat interval differences (RMSSD) correlation coefficient greater than 0.95 in rest and greater than 0.87 in motion. The error of mean low frequency over high frequency (LF/HF) ratio estimated from PRV was 0.13 in rest and 0.25 in lateral motion. Moreover, a statistically significant correlation was obtained between HRV and PRV power spectra and temporal signals for all performed tasks. The obtained results contributed to confirm that remote imaging measurement of cardiac parameters is a promising, convenient, and low-cost alternative to specialized biomedical sensors in a diversity of relevant experimental maneuver.