Leveraging knowledge from physiological data: on-body heat stress risk prediction with sensor networks

On the development of low power wearable devices for assessment of physiological vital parameters: a systematic review

Aim

Smart wearable devices for continuous monitoring of health conditions have bbecome very important in the healthcare sector to acquire and assess the different physiological parameters. This paper reviews the nature of physiological signals, desired vital parameters, role of smart wearable devices, choices of wearable devices and design considerations for wearable devices for early detection of health conditions.

Subject and methods

This article provides designers with information to identify and develop smart wearable devices based on the data extracted from a literature survey on previously published research articles in the field of wearable devices for monitoring vital parameters.

Results

The key information available in this article indicates that quality signal acquisition, processing and longtime monitoring of vital parameters requires smart wearable devices. The development of smart wearable devices with the listed design criteria supports the developer to design a low power wearable device for continuous monitoring of patient health conditions.

Conclusion

The wide range of information gathered from the review indicates that there is a huge demand for smart wearable devices for monitoring health conditions at home. It further supports tracking heath status in the long term via monitoring the vital parameters with the support of wireless communication principles.

Towards real-time thermal stress prediction systems for workers

Exposure to extreme temperatures in workplaces implies serious physical hazards to workers. In addition, a poorly acclimatized worker can have reduced performance and alertness. It may therefore be more vulnerable to the risk of accidents and injuries. Due to the incompatibility of standards and regulations with some work environments and a lack of thermal exchange in many personal protective equipment, heat stress remains among the most common physical risks in many industrial sectors. Furthermore, conventional methods of measuring physiological parameters in order to calculate personal thermophysiological constraints are not practical to use during work tasks. However, the emergence of wearable technologies can contribute to real-time measurement of body temperature and the biometric signals needed to assess thermophysiological constraints while actively working. Thus, the present study was carried out in order to scrutinize the current knowledge of these types of technologies by analyzing the available systems and the advances made in previous studies, as well as to discuss the efforts required to develop devices for the prevention of the occurrence of heat stress in real time.

Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects

The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.

Impact of passive heat stress on persons with spinal cord injury: Implications for Olympic spectators

Environmental heat stress can negatively impact health, work capacity, and athletic performance and potentially to lead to life-threatening consequences if not mitigated. With the upcoming Toyko Olympic games to be held during anticipated warm ambient temperatures (up to 29°C), and with spectators potentially spending long durations of time outdoors, certain populations of persons with impaired thermoregulatory capacity will be at higher risk of heat-related illness from passive heat stress. Persons with spinal cord injury (SCI) are one of these groups as a result of a decentralized sympathetic nervous system, which leaves them with impairment in convective and evaporative cooling via vasodilation and sweating, respectively. This review summarizes (1) thermoregulatory physiological responses of persons with SCI under passive heat stress: the effect of level and completeness of injury; (2) the impact of passive heat stress on quality of life (QOL), outdoor participation, behavioral thermoregulation, and cognition; (3) recommendations and education for clinicians providing health care for persons with SCI; and (4) suggestions of future directions for exploring the gaps in the literature on passive heat stress in persons with SCI. This article aims to equip consumers with SCI and health-care professionals with the most up-to-date knowledge on passive heat stress responses in persons with SCI, so that their attendance at the Olympic games can be done with maximal safety and enjoyment.

Wearable Health Devices-Vital Sign Monitoring, Systems and Technologies

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.

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection

The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k-nearest neighbor (k-NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.

Informatics for Precision Medicine and Healthcare

The past decade has witnessed great advances in biomedical informatics. Biomedical informatics is an emerging field of healthcare that aims to translate the laboratory observation into clinical practice. Smart healthcare has also developed rapidly with ubiquitous sensor and communication technologies. It is able to capture the online patient-centric phenotypic variables, thus providing a rich information base for translational biomedical informatics. Biomedical informatics and smart healthcare represent two interrelated disciplines. On one hand, biomedical informatics translates the bench discoveries into bedside, and, on the other hand, it is reciprocally informed by clinical data generated from smart healthcare. In this chapter, we will introduce the major strategies and challenges in the application of biomedical informatics technology in precision medicine and healthcare. We highlight how the informatics technology will promote the precision medicine and therefore promise the improvement of healthcare.

Evaluation of physiological strain in hot work areas using thermal imagery

BACKGROUND

Monitoring core body temperature to identify heat strain in workers engaged in hot work in heat stress environments is intrusive and expensive. Nonintrusive, inexpensive methods are needed to calculate individual Physiological Strain Index (PSI).

OBJECTIVE

Thermal imaging and heart rate monitoring were used in this study to calculate Physiological Strain Index (PSI) from thermal imaging temperatures of human subjects wearing thermal protective garments during recovery from hot work.

METHODS

Ten male subjects were evaluated for physiological strain while participating in hot work. Thermal images of the head and neck were captured with a high-resolution thermal imaging camera concomitant with measures of gastrointestinal and skin temperature. Lin's concordance correlation coefficient (rho_c), Pearson's coefficient (r) and bias correction factor (C-b) were calculated to compare thermal imaging based temperatures to gastrointestinal temperatures. Calculations of PSI based thermal imaging recorded temperatures were compared to gastrointestinal based PSI.

RESULTS

Participants reached a peak PSI of 5.2, indicating moderate heat strain. Sagittal measurements showed low correlation (rho_c=0.133), moderate precision (r=0.496) and low accuracy (C_b=0.269) with gastrointestinal temperature. Bland-Altman plots of imaging measurements showed increasing agreement as gastrointestinal temperature rose; however, the Limits of Agreement (LoA) fell outside the ±0.25C range of clinical significance. Bland-Altman plots of PSI calculated from imaging measurements showed increasing agreement as gastrointestinal temperature rose; however, the LoA fell outside the ±0.5 range of clinical significance.

CONCLUSION

Results of this study confirmed previous research showing thermal imagery is not highly correlated to body core temperature during recovery from moderate heat strain in mild ambient conditions. Measurements display a trend toward increasing correlation at higher body core temperatures. Accuracy was not sufficient at mild to moderate heat strain to allow calculation of individual physiological stress.

Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.