Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data to clinicians with a potential for earlier diagnostic and guidance of treatment. The technology revolution in the miniaturization of electronic devices is enabling to design more reliable and adaptable wearables, contributing for a world-wide change in the health monitoring approach. In this paper we review important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications. A focus on vital signs acquired by WHDs is made: first a discussion about the most important vital signs for health assessment using WHDs is presented and then for each vital sign a description is made concerning its origin and effect on heath, monitoring needs, acquisition methods and WHDs and recent scientific developments on the area (electrocardiogram, heart rate, blood pressure, respiration rate, blood oxygen saturation, blood glucose, skin perspiration, capnography, body temperature, motion evaluation, cardiac implantable devices and ambient parameters). A general WHDs system architecture is presented based on the state-of-the-art. After a global review of WHDs, we zoom in into cardiovascular WHDs, analysing commercial devices and their applicability versus quality, extending this subject to smart t-shirts for medical purposes. Furthermore we present a resumed evolution of these devices based on the prototypes developed along the years. Finally we discuss likely market trends and future challenges for the emerging WHDs area.

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Wearable Health Devices-Vital Sign Monitoring, Systems and Technologies.

There is provided a method of detecting a fall by a user, the method comprising detecting whether a user has potentially experienced a fall event from measurements of the movements of the user; on detecting a potential fall event, determining the activity level of the user and a measure of a autonomic nervous system, ANS, response for the user associated with the potential fall event; comparing the determined activity level and the measure of the ANS response to a user profile relating activity level and ANS response for the user; and determining whether the potential fall event is a fall based on the result of the comparison.

Method for detecting falls and a fall detector.

Background: Self-quantification (SQ) is a way of working in which, by using tracking tools, people aim to collect, manage, and reflect on personal health data to gain a better understanding of their own body, health behavior, and interaction with the world around them. However, health SQ lacks a formal framework for describing the self-quantifiers’ activities and their contextual components or constructs to pursue these health related goals. Establishing such framework is important because it is the first step to operationalize health SQ fully. This may in turn help to achieve the aims of health professionals and researchers who seek to make or study changes in the self-quantifiers’ health systematically. Objective: The aim of this study was to review studies on health SQ in order to answer the following questions: What are the general features of the work and the particular activities that self-quantifiers perform to achieve their health objectives? What constructs of health SQ have been identified in the scientific literature? How have these studies described such constructs? How would it be possible to model these constructs theoretically to characterize the work of health SQ? Methods: A systematic review of peer-reviewed literature was conducted. A total of 26 empirical studies were included. The content of these studies was thematically analyzed using Activity Theory as an organizing framework. Results: The literature provided varying descriptions of health SQ as data-driven and objective-oriented work mediated by SQ tools. From the literature, we identified two types of SQ work: work on data (ie, data management activities) and work with data (ie, health management activities). Using Activity Theory, these activities could be characterized into 6 constructs: users, tracking tools, health objectives, division of work, community or group setting, and SQ plan and rules. We could not find a reference to any single study that accounted for all these activities and constructs of health SQ activity. Conclusions: A Health Self-Quantification Activity Framework is presented, which shows SQ tool use in context, in relation to the goals, plans, and competence of the user. This makes it easier to analyze issues affecting SQ activity, and thereby makes it more feasible to address them. This review makes two significant contributions to research in this field: it explores health SQ work and its constructs thoroughly and it adapts Activity Theory to describe health SQ activity systematically. [J Med Internet Res 2016;18(5):e131]

Activity Theory as a Theoretical Framework for Health Self-Quantification: A Systematic Review of Empirical Studies

Hypertension is a common and chronic disease, caused by high blood pressure. Since hypertension often has no warning signs or symptoms, many cases remain undiagnosed. Untreated or sub-optimally controlled hypertension may lead to cardiovascular, cerebrovascular and renal morbidity and mortality, along with dysfunction of the autonomic nervous system. Therefore, it could be quite valuable to predict or provide early warnings about hypertension. Heart rate variability (HRV) analysis has emerged as the most valuable non-invasive test to assess autonomic nervous system function, and has great potential for detecting hypertension. However, HRV indicators may be subtle and present at random, resulting in two challenges: how to support continuous monitoring for hours at a time while being unobtrusive, and how to efficiently analyze the collected data to minimize data collection and user burden. In this paper, we present a machine learning-based approach for detecting hypertension, using a waist belt continuous sensing system that is worn overnight. Using 24 hypertension patients and 24 healthy controls, we demonstrate that our approach can differentiate hypertension patients from healthy controls with 93.33% accuracy. This represents a promising approach for performing hypertension classification in the field, and also we would improve its performance based on a large number of hypertensive subjects monitored by the proposed pervasive sensors.

Automated recognition of hypertension through overnight continuous HRV monitoring

The use of spatiotemporal video processing to extract biosignals is an emerging technique. This paper aims to build upon current work through robust experimentation and analysis. A blood flow simulation model was captured by thermal and optical cameras, while hot water was pumped through the system. Additionally, five subjects were recruited to perform two experimental trials: a facial perfusion trial and an arm blood occlusion trial, for which subjects sat quietly, while video data were captured using thermal and optical cameras. Each video was subjected to region of interest selection and adaptive Eulerian video magnification (EVM); the iterative application of EVM, first with a wide temporal bandpass filter and low amplification factor and again with a narrower, targeted temporal bandpass filter and higher amplification factor. The results from the simulation experiments indicated that thermal video in conjunction with adaptive EVM processing can reveal variations in temperature indicative of pulse rate in a controlled system of known variables. This process helped to better characterize Eulerian signal enhancement versus Eulerian noise enhancement. The results from the facial perfusion experiments suggest that the adaptive EVM processing of thermal video results in signals representative of facial perfusion rate. The results from the blood occlusion experiments revealed an occlusion temperature pattern, but not a perfusion rate. This paper therefore further demonstrated the potential of thermal video in conjunction with adaptive EVM methods to extract a signal representative of facial perfusion rate, and illustrated the need for more research on thermal video and adaptive EVM.

Adaptive Eulerian Video Processing of Thermal Video: An Experimental Analysis

Sensor miniaturization and advances in low power communication protocols have helped enable continuous monitoring of physiological signals. Commercial products exist today which are wearable, in a form of a chest belt or a wrist strap, and can continuously stream or log bio-signals such as heart rate, as well as motion signals. Such devices could be of importance not only to a health-conscious consumer, but also in the healthcare system, for monitoring of patients whose prescribed treatment includes some form of physical activity. This paper proposes a new framework for monitoring compliance and impact of an exercise regimen. While previous related work typically uses an accelerometer based approach, our method gives a more complete picture of impact of exercise on the subject by adding bio-signals such as heart rate, breathing rate and skin conductivity and correlating them with motion signals. Such framework can be used to assess progress over time for different types of exercise regimens.

Measuring heart rate, breathing rate and skin conductance during exercise

Analysis of Heart Rate Variability (HRV) is an active area of research in the engineering and the medical communities. Current studies use medical-grade ECG signals, from a limited number of available databases. On the other hand, the trend for physiological measurements is towards less obtrusive, wearable devices. It is therefore of interest to apply HRV analysis to signals from such wearable sensors, whose outputs could exhibit varying levels of noise caused by motion artifacts. The main contribution of this paper is the quantification of the impact of imperfect HRV measurements due to motion artifacts on classifiers which use standard time domain and spectral HRV features. The analysis could potentially lead to development of more robust features and classifiers for use in wearable and non-controlled environments.

Impact of motion artifacts on Heart Rate Variability measurements and classification performance

Measuring vital signs such as heart rate using a camera has the potential to enable better health monitoring for subjects at risk and as such enhance their quality of life. Applications could include driver monitoring via in-dash camera, critical function operator monitoring at work, or remote health monitoring via a webcam. For such a system to be feasible however, it needs to be work well in realistic scenarios where the subject does not sit completely still in front of a camera. Motion artifacts, if not taken into account when designing the system, yield inaccurate results and potentially create false alarms. In this paper, we start with a popular algorithm for extracting heart rate from video based on spatial and temporal filtering, quantify how key parameters used in the algorithm affect its performance in situations when the subject is not sitting still, analyze in detail the performance of the filtering approach in videos with motion, identify issues, and propose approaches to overcome those limitations. The paper shows that the use of wider filters and more levels in the Gaussian pyramid lead to a better performance when the subject is moving, but that the motion artifacts dominate the extracted signal.

Impact of motion artifacts on video-based non-intrusive heart rate measurement

Proliferation of wearable devices and the “quantified self” behavior creates massive amounts of data acquired from physiological sensors. Unfortunately this vast amount of data is to date largely untapped for clinical purposes because of low data reliability caused by issues such as contact noise and motion artifacts. One important physiological signal is ECG and one of its important features is the heart rate, obtained from the QRS complex of the ECG signal or beat detection algorithms. This feature is important in characterizing Heart Rate Variability (HRV) and detecting various pathologies such as arrhythmia, chronic heart failure, or sleep apnea. Many such algorithms simply discard data segments which are deemed “unreliable” due to errors in QRS detection. This paper analyzes the impact of changing the noise level and noise duration on the percentage of data segments that are discarded. The paper proposes an approach to improve the usability of ECG data corrupted by noise by analyzing the impact of noise on features of interest and adapting relevant system parameters accordingly. The proposed approach can be used with any classifier operating on short-term HRV features.

Jelena Nikolic-Popovic

Papers

Measuring heart rate, breathing rate and skin conductance during exercise

Impact of motion artifacts on Heart Rate Variability measurements and classification performance

Impact of motion artifacts on video-based non-intrusive heart rate measurement

Towards increased usability of noisy ECG signals in HRV-based classifiers