Occupational Stress Monitoring Using Biomarkers and Smartwatches: A Systematic Review.

This article presents a systematic review of the literature concerning scientific publications on wrist wearables that can help to identify stress levels. The study is part of a research project aimed at modeling a stress surveillance system and providing coping recommendations. The investigation followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In total, 38 articles were selected for full reading, and 10 articles were selected owing to their alignment with the study proposal. The types of technologies used in the research stand out amongst our main results after analyzing the articles. It is noteworthy that stress assessments are still based on standardized questionnaires, completed by the participants. The main biomarkers collected by the devices used in the selected works included: heart rate variation, cortisol analysis, skin conductance, body temperature, and blood volume at the wrist. This study concludes that developing a wrist wearable for stress identification using physiological and chemical sensors is challenging but possible and applicable.

Development of a microcontrolled bioinstrumentation system for active control of leg prostheses.

This article describes the design of a microcontrolled bioinstrumentation system for active control of leg prostheses, using 4-channel electromyographic signal (EMG) detection and a single-channel electrogoniometer. The system is part of a control and instrumentation architecture in which a master processor controls the tasks of slave microcontrollers, through a RS-485 interface. Several signal processing methods are integrated in the system, for feature extraction (Recursive Least Squares), feature projection (Self Organizing Maps), and pattern classification (Levenberg-Marquardt Neural Network). The acquisition of EMG signals and additional mechanical information could help improving the precision in the control of leg prostheses.

Left ventricle segmentation in echocardiography using a radial-search-based image processing algorithm.

A new left ventricle segmentation method in two-dimensional echocardiography images is proposed. Image processing techniques combined with radial search and temporal information are used to extract the left ventricle boundary. Borders from sequential images are extracted using the proposed method, and a curve illustrating the area variation within a cardiac cycle is presented. Performance evaluation is performed by comparing the borders obtained from the presented method to those manually prescribed by a medical specialist. The new sequential radial search algorithm improved the border extraction from long-axis ultrasound images, specially the ones where the mitral valve was open. Segmentation errors due to low contrast were corrected.

Energy efficient simulator for patient monitoring in Body Sensor Networks.

The medical tracking of patients carried out from a distance by means of wireless technology, incorporating various sensors simultaneously connected to the body of an individual, can promote health in suitable time. This article proposes a simulator for the monitoring of human health. This prototype simulates the workings of a BSN (Body Sensor Network) functioning as a sensor (electrophysiological signal) that is to be monitored, which utilizes a strategy based on the concept of data fusion. This tool incorporates a policy of economizing energy which has as its main objective increasing the operating time of the system.

A new model for programming software in body sensor networks.

A Body Sensor Network (BSN) must be designed to work autonomously. On the other hand, BSNs need mechanisms that allow changes in their behavior in order to become a clinically useful tool. The purpose of this paper is to present a new programming model that will be useful for programming BSN sensor nodes. This model is based on an intelligent intermediate-level compiler. The main purpose of the proposed compiler is to increase the efficiency in system use, and to increase the lifetime of the application, considering its requirements, hardware possibilities and specialist knowledge. With this model, it is possible to maintain the autonomous operation capability of the BSN and still offer tools that allow users with little grasp on programming techniques to program these systems.

Application-oriented programming model for sensor networks embedded in the human body.

This work presents a new programming model for sensor networks embedded in the human body which is based on the concept of multi-programming application-oriented software. This model was conceived with a top-down approach of four layers and its main goal is to allow the healthcare professionals to program and to reconfigure the network locally or by the Internet. In order to evaluate this hypothesis, a benchmarking was executed in order to allow the assessment of the mean time spent in the programming of a multi-functional sensor node used for the measurement and transmission of the electrocardiogram.