A Multisensor System Embedded in a Computer Mouse for Occupational Stress Detection.

Occupational stress is a major challenge in modern societies, related with many health and economic implications. Its automatic detection in an office environment can be a key factor toward effective management, especially in the post-COVID era of changing working norms. The aim of this study is the design, development and validation of a multisensor system embedded in a computer mouse for the detection of office work stress. An experiment is described where photoplethysmography (PPG) and galvanic skin response (GSR) signals of 32 subjects were obtained during the execution of stress-inducing tasks that sought to simulate the stressors present in a computer-based office environment. Kalman and moving average filters were used to process the signals and appropriately formulated algorithms were applied to extract the features of pulse rate and skin conductance. The results found that the stressful periods of the experiment significantly increased the participants' reported stress levels while negatively affecting their cognitive performance. Statistical analysis showed that, in most cases, there was a highly significant statistical difference in the physiological parameters measured during the different periods of the experiment, without and with the presence of stressors. These results indicate that the proposed device can be part of an unobtrusive system for monitoring and detecting the stress levels of office workers.

A smart computer mouse with biometric sensors for unobtrusive office work-related stress monitoring.

Health disorders related to the prolonged exposure to stress are very common among office workers. The need for an automated and unobtrusive method of detecting and monitoring occupational stress is imperative and intensifies in the current conditions, where the pandemic COVID-19 causes changes in the working norms globally. In this study, we present a smart computer mouse with biometric sensors integrated in such a way that its structure and functionality remain unaffected. Photoplethysmography (PPG) signal is collected from user's thumb by a PPG sensor placed on the side wall of the mouse, while galvanic skin response (GSR) is measured from the palm through two electrodes placed on the top surface of the mouse. Biosignals are processed by a microcontroller and can be transferred wirelessly over Wi-Fi connection. Both the sensors and the microcontroller have been placed inside the mouse, enabling its plug and play use, without any additional equipment. The proposed module has been developed as part of a system that infers about the stress levels of office workers, based on their interactions with the computer and its peripheral devices.

Serious games for Parkinson's Disease management as implemented in PROPHETIC platform.

In this paper, we describe the serious games, integrated into PROPHETIC which is an innovating personal healthcare service for a holistic remote management of Parkinson's disease (PD) patients. The main objective of the three developed serious games is to allow health professionals to remotely monitor and appraise the overall physical status of their patients. The significant benefits for the patients, making use of this platform, is the improvement of their engagement, empowerment and, consequently, the provision of education about their condition and its management. The design of the serious games was based on the clinical needs derived from the literature and their primary target is to assess and record specific physical capabilities of the patient. All the games scores and the recorded parameters are gathered and also presented to the clinicians, offering them a precise overview of the patient's motor status and the possibility to modify the therapeutic plan, if required.

A Smartphone Application Designed to Engage the Elderly in Home-Based Rehabilitation.

As life expectancy increases, it is imperative that the elderly take advantage of the benefits of technology to remain active and independent. Mobile health applications are widely used nowadays as they promote a healthy lifestyle and self-management of diseases, opening new horizons in the interactive health service delivery. However, adapting these applications to the needs and requirements of the elderly is still a challenge. This article presents a smartphone application that is part of a multifactorial intervention to support older people with balance disorders. The application aims to enable users to self-evaluate their activity and progress, to communicate with each other and, through strategically selected motivational features, to engage with the system with undiminished interest for a long period of time. Mock-up interfaces were evaluated in semi-structured focus groups and interviews that were performed across three European countries. Further evaluation in the form of four pilot studies with 160 participants will be performed and qualitative and quantitative measures will be used to process the feedback about the use of the application.

MODELHealth: Facilitating Machine Learning on Big Health Data Networks.

MODELHealth is a platform that aims to facilitate the implementation of Machine Learning (ML) techniques on medical data in order to upgrade the delivery of healthcare services. MODELHealth platform is a "holistic" approach to the implementation of processes for the development and utilization of ML algorithms in many forms, including Neural Networks, and can be used to assist clinical work and administrative decision-making. It covers the entire lifecycle of these processes, from pumping, homogenization, anonymization, and enrichment of the initial data, to the final disposal of efficient algorithms through Application Program Interfaces for consumption by any authorized Information System.