Alcogait Gamification

Alcohol abuse causes 1 in 10 deaths among adults in the United States aged 20-64 years [11]. An effort to motivate health-related behavioral changes in e-health industry could be seen before, but it was never done in mobile (m-health) context. Technologically, current applications in smartphone domain, emphasize on a manual way of measuring intoxication levels for users such as logging BAC values, taking cognitive tests; but none of them passively infer user’s intoxication level [1]. ‘Alcogait’ is a smartphone app that infers a smartphone user’s intoxication level from their gait by classifying motion data gathered from the smartphone’s accelerometer and gyroscope by Aiello et al [1]. This study is part of a Master’s thesis to build an intervention system around Alcogait’s functionality and explore the effects of gamification and avatar (for feedback) using Alcogait’s inferred intoxication level. Creation of user engagement is examined, in order to continue future study using gamification along with Alcogait’s functionality. The Alcogait system is not intended to either encourage or discourage abstinence. Its goal is to incentivize responsible transportation choices made by a person or their peers after that person is detected to be intoxicated in order to potentially mitigate DUI situations

Physiological-based Driver Monitoring Systems: A Scoping Review

A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PD

A review on flexible electrochemical biosensors to monitor alcohol in sweat

The continued focus on improving the quality of human life has encouraged the development of increasingly efficient, durable, and cost-effective products in healthcare. Over the last decade, there has been substantial development in the field of technical and interactive textiles that combine expertise in electronics, biology, chemistry, and physics. Most recently, the creation of textile biosensors capable of quantifying biometric data in biological fluids is being studied, to detect a specific disease or the physical condition of an individual. The ultimate goal is to provide access to medical diagnosis anytime and anywhere. Presently, alcohol is considered the most commonly used addictive substance worldwide, being one of the main causes of death in road accidents. Thus, it is important to think of solutions capable of minimizing this public health problem. Alcohol biosensors constitute an excellent tool to aid at improving road safety. Hence, this review explores concepts about alcohol biomarkers, the composition of human sweat and the correlation between alcohol and blood. Different components and requirements of a biosensor are reviewed, along with the electrochemical techniques to evaluate its performance, in addition to construction techniques of textile-based biosensors. Special attention is given to the determination of biomarkers that must be low cost and fast, so the use of biomimetic materials to recognize and detect the target analyte is turning into an attractive option to improve electrochemical behavior.Authors acknowledge the Portuguese Foundation for Science and Technology (FCT), FEDER funds by means of Portugal 2020 Competitive Factors Operational Program (POCI) and the Portuguese Government (OE) for funding the project PluriProtech—“Desenvolvimentos de soluções multicamada para proteção ativa contra ameaças NBQR”, ref. POCI-01-0247-FEDER-047012. Authors also acknowledge strategic funding of UID/CTM/00264/2020 of 2C2T and by the “plurianual” 2020–2023 Project UIDB/00264/2020

An IoT based Virtual Coaching System (VSC) for Assisting Activities of Daily Life

Nowadays aging of the population is becoming one of the main concerns of theworld. It is estimated that the number of people aged over 65 will increase from 461million to 2 billion in 2050. This substantial increment in the elderly population willhave significant consequences in the social and health care system. Therefore, in thecontext of Ambient Intelligence (AmI), the Ambient Assisted Living (AAL) has beenemerging as a new research area to address problems related to the aging of the population. AAL technologies based on embedded devices have demonstrated to be effectivein alleviating the social- and health-care issues related to the continuous growing of theaverage age of the population. Many smart applications, devices and systems have beendeveloped to monitor the health status of elderly, substitute them in the accomplishment of activities of the daily life (especially in presence of some impairment or disability),alert their caregivers in case of necessity and help them in recognizing risky situations.Such assistive technologies basically rely on the communication and interaction be-tween body sensors, smart environments and smart devices. However, in such contextless effort has been spent in designing smart solutions for empowering and supportingthe self-efficacy of people with neurodegenerative diseases and elderly in general. Thisthesis fills in the gap by presenting a low-cost, non intrusive, and ubiquitous VirtualCoaching System (VCS) to support people in the acquisition of new behaviors (e.g.,taking pills, drinking water, finding the right key, avoiding motor blocks) necessary tocope with needs derived from a change in their health status and a degradation of theircognitive capabilities as they age. VCS is based on the concept of extended mind intro-duced by Clark and Chalmers in 1998. They proposed the idea that objects within theenvironment function as a part of the mind. In my revisiting of the concept of extendedmind, the VCS is composed of a set of smart objects that exploit the Internet of Things(IoT) technology and machine learning-based algorithms, in order to identify the needsof the users and react accordingly. In particular, the system exploits smart tags to trans-form objects commonly used by people (e.g., pillbox, bottle of water, keys) into smartobjects, it monitors their usage according to their needs, and it incrementally guidesthem in the acquisition of new behaviors related to their needs. To implement VCS, thisthesis explores different research directions and challenges. First of all, it addresses thedefinition of a ubiquitous, non-invasive and low-cost indoor monitoring architecture byexploiting the IoT paradigm. Secondly, it deals with the necessity of developing solu-tions for implementing coaching actions and consequently monitoring human activitiesby analyzing the interaction between people and smart objects. Finally, it focuses on the design of low-cost localization systems for indoor environment, since knowing theposition of a person provides VCS with essential information to acquire information onperformed activities and to prevent risky situations. In the end, the outcomes of theseresearch directions have been integrated into a healthcare application scenario to imple-ment a wearable system that prevents freezing of gait in people affected by Parkinson\u2019sDisease

Microneedle based electrochemical (bio)sensing: towards decentralized and continuous health status monitoring

Microneedle (MN) based electrochemical (bio)sensing has become a growing field within the discipline of analytical chemistry as a result of its unique capacity for continuous, decentralized health status monitoring. There are two significant advantages to this exclusive feature: i) the ability to directly analyze interstitial fluid (ISF), a body fluid with a similar enough composition to plasma (and blood) to be considered a plentiful source of information related to biologically relevant molecules and biomarkers; and ii) the capacity to overcome some of the major limitations of blood analysis including painful extraction, high interferant concentrations, and incompatibility with diagnosis of infants (and especially newborns). Recent publications have demonstrated important advancements in electrochemical MN sensor technology, among which are included new MN fabrication methods and various modification strategies, providing different architectures and allowing for the integration of electronics. This versatility highlights the undeniable need for interdisciplinary efforts towards tangible progress in the field. In a context evidently dominated by glucose sensing, which is slowly being expanded towards other analytes, the following crucial questions arise: to what extent are electrochemical MN (bio)sensors a reliable analytical tool for continuous ISF monitoring? Which is the best calibration protocol to be followed for in vivo assays? Which strategies can be employed to protect the sensing element during skin penetration? Is there an appropriate validation methodology to assess the accuracy of electrochemical MN (bio)sensors? How significant is the distinction between successful achievements in the laboratory and the real commercial feasibility of products? This paper aims to reflect on those previous questions while reviewing the progress of electrochemical MN (bio)sensors in the last decade with a focus on the analytical aspects. Overall, we describe the current state of electrochemical MN (bio)sensors, the benefits and challenges associated to ISF monitoring, as well as key features (and bottlenecks) regarding its implementation for in vivo assays

Dispositivo de Deteção do Bruxismo do Sono

This thesis aims to explore and, ultimately, develop a system capable of monitoring physiological signals to detect bruxism events. Bruxism is a disorder characterized by the habit of pressing and grinding the teeth. These events can either occur during the day (Awake Bruxism) or during the night (Sleep Bruxism). Studies suggest that 20% of the adult population suffer from Awake Bruxism, and 8-16% from Sleep Bruxism. The consequences of this disorder are several, ranging from tooth wear, dental fractures, or abfraction, resulting in headaches, or facial myalgia. This dissertation focuses on the Sleep Bruxism type since it’s harder to detect and treat. First, a study about the evolution of technology in healthcare is carried out, fundamentally about how it was introduced and how did it get to the point it is now. The topic of wearable devices is also explored, in the sense that it’s where the market is going and how these devices can transform healthcare. Then, the study converges on the devices developed especially for bruxism, namely which devices, and what type of techniques are used. Subsequently, the general concept for the system is elaborated, exploring several options both in terms of devices and physiological data to be parameterized. However, some restrictions exist for the construction of the system. For the construction of an intraoral system, the device has to be of small dimensions and with low energy consumption. With these constraints, the system has implemented an Inertial Measurement Unit to estimate the orientation of the patient’s sleeping position, and force sensors to measure the force exerted between the teeth. For compactness, a Systemon-Chip is used, since it includes an ARM Cortex M4 processor, several peripherals, and an RF transceiver in one package. The system is not only responsible for the data acquisition, but also the data transmission. This is accomplished by using Bluetooth Low Energy, which is one of the most common protocols for low-power devices. Customized service is developed for this purpose, consisting of three different characteristics: the force characteristic, the accelerometer characteristic, and the gyroscope characteristic. The reason is for maximizing efficiency. The last step was to develop the prototype, testing its functionalities and try to project next iterations of the prototype

Wearables in medicine

Wearables as medical technologies are becoming an integral part of personal analytics, measuring physical status, recording physiological parameters, or informing schedule for medication. These continuously evolving technology platforms do not only promise to help people pursue a healthier life style, but also provide continuous medical data for actively tracking metabolic status, diagnosis, and treatment. Advances in the miniaturization of flexible electronics, electrochemical biosensors, microfluidics, and artificial intelligence algorithms have led to wearable devices that can generate real-time medical data within the Internet of things. These flexible devices can be configured to make conformal contact with epidermal, ocular, intracochlear, and dental interfaces to collect biochemical or electrophysiological signals. This article discusses consumer trends in wearable electronics, commercial and emerging devices, and fabrication methods. It also reviews real-time monitoring of vital signs using biosensors, stimuli-responsive materials for drug delivery, and closed-loop theranostic systems. It covers future challenges in augmented, virtual, and mixed reality, communication modes, energy management, displays, conformity, and data safety. The development of patient-oriented wearable technologies and their incorporation in randomized clinical trials will facilitate the design of safe and effective approaches

A review of technologies for heart attack monitoring systems

Every year, approximately 1.35 million people die in car accidents. One of the causes of traffic accidents is a heart attack while driving. Common heart attack warning signs are pain or discomfort in the chest or one or both arms or shoulders, light-headedness, faintness, cold sweat, and shortness of breath. When having a heart attack, a car driver has strong pain in the centre or left side of the chest. Current technology for heart attack detection is based on sensory signal properties such as the electrocardiogram (ECG), heart rate and oxygen saturation (SpO2). This paper is intended to give the readers an overview of technologies for heart attack monitoring system that has been used at the hospital, at the home and in the vehicle. The result shows that ECG, heart rate and SpO2 properties are mostly used by numerous researchers for heart attack monitoring systems at hospitals. Meanwhile, many researchers developed a system by using heart rate, ECG, SpO2 and images as properties for heart attack monitoring systems at home. Existing technologies for heart attack monitoring systems in the vehicle used heart rate and ECG as properties in a system. However, there are no review papers yet on heart attack monitoring systems using image processing in vehicles. We believe that researchers and practitioners will embrace this technology by addressing image processing in the heart attack monitoring system in vehicles

Survey and synthesis of state of the art in driver monitoring

Road vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists of characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions—called here “(sub)states”—of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced