An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.

Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use

Unen mittaaminen voimasensoreilla

This thesis presents methods for comfortable sleep measurement at home. Existing medical sleep measurement systems are costly, disturb sleep quality, and are only suited for short-term measurement. As sleeping problems are affecting about 30% of the population, new approaches for everyday sleep measurement are needed. We present sleep measurement methods that are based on measuring the body with practically unnoticeable force sensors installed in the bed. The sensors pick up forces caused by heartbeats, respiration, and movements, so those physiological parameters can be measured. Based on the parameters, the quality and quantity of sleep is analyzed and presented to the user. In the first part of the thesis, we propose new signal processing algorithms for measuring heart rate and respiration during sleep. The proposed heart rate detection method enables measurement of heart rate variability from a ballistocardiogram signal, which represents the mechanical activity of the heart. A heartbeat model is adaptively inferred from the signal using a clustering algorithm, and the model is utilized in detecting heartbeat intervals in the signal. We also propose a novel method for extracting respiration rate variation from a force sensor signal. The method solves a problem present with some respiration sensors, where erroneous cyclicity arises in the signal and may cause incorrect measurement. The correct respiration cycles are found by filtering the input signal with multiple filters and selecting correct results with heuristics. The accuracy of heart rate measurement has been validated with a clinical study of 60 people and the respiration rate method has been tested with a one-person case study. In the second part of the thesis, we describe an e-health system for sleep measurement in the home environment. The system measures sleep automatically, by uploading measured force sensor signals to a web service. The sleep information is presented to the user in a web interface. Such easy-to-use sleep measurement may help individuals to tackle sleeping problems. The user can track important aspects of sleep such as sleep quantity and nocturnal heart rate and learn how different lifestyle choices affect sleep.Unen mittaaminen voimasensoreilla Noin joka kolmannella on ongelmia unen kanssa. Nukahtamisvaikeus, heräily, huono unen laatu sekä erilaiset unenaikaiset hengitysongelmat ovat yleisiä. Helppo ja mukava unen seuranta voisi auttaa unenlaadun parantamisessa. Nykyiset mittausmenetelmät ovat kuitenkin epämukavia ja suunniteltu lähinnä lääketieteellisten diagnoosien tekemiseen. Ne eivät siis sovellu unen mittaamiseen itsenäisesti kotona. Tämä väitöskirja esittelee uuden mittausmenetelmän, joka mahdollistaa unen määrän sekä laadun mittaamisen helposti omassa sängyssä. Lakanan alle laitetaan pehmeästä kalvosta tehty anturi, joka mittaa nukkujan liikkeitä, sydämen sykettä sekä hengitystä. Anturi tunnistaa näiden mittausten perusteella useita uneen liittyviä asioita, kuten unenmäärä, kuorsaaminen sekä yön aikana mitattu leposyke. Uni-informaatio näytetään laitteen käyttäjälle verkkopalvelun tai mobiililaitteen avulla. Väitöskirjassa esitellyn unenmittausmenetelmän etu on, että syke- ja hengitystieto saadaan mitattua siitä huolimatta että anturi ei ole suoraan kosketuksissa nukkujan kehon kanssa. Kehitetyt signaalinkäsittelymenetelmät pystyvät erottamaan signaalista sykkeen ja hengityksen, sillä erilaisten mittaushäiriöiden ilmaantuminen signaaliin on otettu huomioon. Uutta unimittausmenetelmää on ehditty jo soveltaa käytännössä. Kehitetty tuote toimii siten, että mittaus lähetetään sensorilta langattomasti mobiililaitteelle, jossa unitiedot näytetään käyttäjälle. Mobiilisovellus antaa ohjeita unen parantamiseksi mittausten sekä käyttäjän profiilin perusteella

Automated and unobtrusive measurement of physical activity in an interactive playground

© 2019 Promoting physical activity is one of the main goals of interactive playgrounds. To validate whether this goal is met, we need to measure the amount of physical player activity. Traditional methods of measuring activity, such as observations or annotations of game sessions, require time and personnel. Others, such as heart rate monitors and accelerometers, need to be worn by the player. In this paper, we investigate whether physical activity can be measured unobtrusively by tracking players using depth cameras and applying computer vision algorithms. In a user study with 32 players, we measure the players’ speed while playing a game of tag, and demonstrate that our measures correlate well with exertion measured using heart rate sensors. This makes the method an attractive alternative to either manual coding or the use of worn devices. We also compare our approach to other exertion measurement methods. Finally, we demonstrate and discuss its potential for automated, unobtrusive measurements and real-time game adaptation

Smart helmet: wearable multichannel ECG & EEG

Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet. The electrode positions are at the lower jaw, mastoids, and forehead, while for validation purposes a respiration belt around the thorax and a reference ECG from the chest serve as ground truth to assess the performance. The within-helmet EEG is verified by exposing the subjects to periodic visual and auditory stimuli and screening the recordings for the steady-state evoked potentials in response to these stimuli. Cycling and walking are chosen as real-world activities to illustrate how to deal with the so-induced irregular motion artifacts, which contaminate the recordings. We also propose a multivariate R-peak detection algorithm suitable for such noisy environments. Recordings in real-world scenarios support a proof of concept of the feasibility of recording vital signs and EEG from the proposed smart helmet

Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

From the Inside Out: A Literature Review on Possibilities of Mobile Emotion Measurement and Recognition

Information systems are becoming increasingly intelligent and emotion artificial intelligence is an important component for the future. Therefore, the measurement and recognition of emotions is necessary and crucial. This paper presents a state of the art in the research field of mobile emotion measurement and recognition. The aim of this structured literature analysis using the PRISMA statement is to collect and classify the relevant literature and to provide an overview of the current status of mobile emotion recording and its future trends. A total of 59 articles were identified in the relevant literature databases, which can be divided into four main categories of emotion measurement. There was an increase of publications over the years in all four categories, but with a particularly strong increase in the areas of optical and vital-data-based recording. Over time, both the speed as well as the accuracy of the measurement has improved considerably in all four categories

Smart workplaces: a system proposal for stress management

Over the past last decades of contemporary society, workplaces have become the primary source of many health issues, leading to mental problems such as stress, depression, and anxiety. Among the others, environmental aspects have shown to be the causes of stress, illness, and lack of productivity. With the arrival of new technologies, especially in the smart workplaces field, most studies have focused on investigating the building energy efficiency models and human thermal comfort. However, little has been applied to occupants’ stress recognition and well-being overall. Due to this fact, this present study aims to propose a stress management solution for an interactive design system that allows the adapting of comfortable environmental conditions according to the user preferences by measuring in real-time the environmental and biological characteristics, thereby helping to prevent stress, as well as to enable users to cope stress when being stressed. The secondary objective will focus on evaluating one part of the system: the mobile application. The proposed system uses several usability methods to identify users’ needs, behavior, and expectations from the user-centered design approach. Applied methods, such as User Research, Card Sorting, and Expert Review, allowed us to evaluate the design system according to Heuristics Analysis, resulting in improved usability of interfaces and experience. The study presents the research results, the design interface, and usability tests. According to the User Research results, temperature and noise are the most common environmental stressors among the users causing stress and uncomfortable conditions to work in, and the preference for physical activities over the digital solutions for coping with stress. Additionally, the System Usability Scale (SUS) results identified that the system’s usability was measured as “excellent” and “acceptable” with a final score of 88 points out of the 100. It is expected that these conclusions can contribute to future investigations in the smart workplaces study field and their interaction with the people placed there.Nas últimas décadas da sociedade contemporânea, o local de trabalho tem se tornado principal fonte de muitos problemas de saúde mental, como o stress, depressão e ansiedade. Os aspetos ambientais têm se revelado como as causas de stress, doenças, falta de produtividade, entre outros. Atualmente, com a chegada de novas tecnologias, principalmente na área de locais de trabalho inteligentes, a maioria dos estudos tem se concentrado na investigação de modelos de eficiência energética de edifícios e conforto térmico humano. No entanto, pouco foi aplicado ao reconhecimento do stress dos ocupantes e ao bem-estar geral das pessoas. Diante disso, o objetivo principal é propor um sistema de design de gestão do stress para um sistema de design interativo que permita adaptar as condições ambientais de acordo com as preferências de utilizador, medindo em tempo real as características ambientais e biológicas, auxiliando assim na prevenção de stress, bem como ajuda os utilizadores a lidar com o stress quando estão sob o mesmo. O segundo objetivo é desenhar e avaliar uma parte do projeto — o protótipo da aplicação móvel através da realização de testes de usabilidade. O sistema proposto resulta da abordagem de design centrado no utilizador, utilizando diversos métodos de usabilidade para identificar as necessidades, comportamentos e as expectativas dos utilizadores. Métodos aplicados, como Pesquisa de Usuário, Card Sorting e Revisão de Especialistas, permitiram avaliar o sistema de design de acordo com a análise heurística, resultando numa melhoria na usabilidade das interfaces e experiência. O estudo apresenta os resultados da pesquisa, a interface do design e os testes de usabilidade. De acordo com os resultados de User Research, a temperatura e o ruído são os stressores ambientais mais comuns entre os utilizadores, causando stresse e condições menos favoráveis para trabalhar, igualmente existe uma preferência por atividades físicas sobre as soluções digitais na gestão do stresse. Adicionalmente, os resultados de System Usability Scale (SUS) identificaram a usabilidade do sistema de design como “excelente” e “aceitável” com pontuação final de 88 pontos em 100. É esperado que essas conclusões possam contribuir para futuras investigações no campo de estudo dos smart workplaces e sua interação com os utilizadores

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking