PDMS/PVA composite ferroelectret for improved energy harvesting performance

This paper address the PDMS ferroelectret discharge issue for improved long- term energy harvesting performance. The PDMS/PVA ferroelectret is fabricated using a 3D-printed plastic mould technology and a functional PVA composite layer is introduced. The PDMS/PVA composite ferroelectret achieved 80% piezoelectric coefficient d33 remaining, compared with 40% without the proposed layer over 72 hours. Further, the retained percentage of output voltage is about 73% over 72 hours

Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

Contains fulltext : 187230.pdf (preprint version ) (Open Access

SAGA: Smart gateway for adaptive environments

The development of adaptive environments has the main objective of providing well-being to an individual, improving the environmental conditions of indoor environments and facilitating/automating any activity. In order to implement such systems, the use of devices capable of intercommunication and acquisition of environment-related parameters around the user is essential. Using wireless sensor networks, it is possible to monitor the various quality indices of indoor environments that can be used to develop strategies to improve quality of life of the users in personalized way. In this dissertation, a system based on a wireless sensor network that analyses and improves the environmental quality of indoor spaces, as well as evaluating the health status of an individual is presented. The system acquires and acts upon air quality and illumination quality-related parameters, as well as physiological data of a user, using sensor nodes and actuators distributed throughout the environment. Several wireless communication protocols have been implemented to enable intercommunication between the several elements present in the sensor network, such as actuators, sensor nodes and a coordinating / gateway node. Several warning mechanisms have been configured to alert the user to the presence of factors that may endanger their health, namely the presence of pollutants and thermal conditions that may trigger respiratory distress. In order to provide real-time system control including additional warning mechanisms, data analysis, a dedicated web application has been developed for this system. The user can control the environment according with his own needs and preferences through profiles configuration. The whole process of system development, hardware, software, experimental tests and contributions are included in this dissertation.A criação de ambientes adaptativos tem o principal objetivo de providenciar o bem-estar a um indivíduo, melhorar as condições do ambiente em seu redor e de facilitar/automatizar qualquer atividade. De forma a implementar tais sistemas, a utilização de dispositivos com capacidade de intercomunicação e de recolha de parâmetros relacionados com o ambiente em redor do utilizador é essencial. Com a utilização de redes de sensores sem fios, é possível monitorizar os diversos índices de qualidade de um ambiente interior e dessa forma melhorar a qualidade de vida. Nesta dissertação será apresentado um sistema baseado numa rede de sensores sem fios que permite analisar e melhorar a qualidade ambiental de espaços interiores e avaliar o estado de saúde de um indivíduo. O sistema adquire e atua sobre parâmetros relacionados com a qualidade do ar e qualidade de iluminação, assim como dados fisiológicos de um utilizador, através da utilização de nós de sensores e atuadores distribuídos pelo ambiente. Foram implementados diversos protocolos de comunicação sem fios para possibilitar a intercomunicação com outros elementos da rede, nomeadamente o nó coordenador/gateway. Foram configurados diversos mecanismos de alerta de forma a avisar o utilizador para a presença de fatores que possam colocar em risco a sua saúde, nomeadamente a presença de poluentes e condições térmicas que possam desencadear desconforto respiratório. De forma a proporcionar uma análise de dados em tempo real, controlo do sistema e dispor de mecanismos de alerta adicionais, foi desenvolvida uma aplicação Web dedicada a este sistema. Através desta, o utilizador poderá tornar o ambiente adaptável às suas características e de acordo com as suas preferências, através da configuração de perfis. Todo o processo de desenvolvimento do sistema, hardware, software, testes experimentais e contribuições serão incluídos nesta dissertação

Simultaneous pulse rate estimation for two individuals that share a sensor-laden bed

Master of ScienceDepartment of Electrical and Computer EngineeringSteven WarrenSleep monitoring has received increased attention in recent years given an improved understanding of the impact of sleep quality on overall well-being. A Kansas State University team has developed a sensor-based bed that can unobtrusively track sleep quality for an individual by analyzing their ballistocardiograms (BCGs) while they lay on the bed, foregoing the need to visit a sleep clinic to quantify their sleep quality. A BCG is a signal that represents cardiac forces that have spread from the heart to the rest of the body – forces that result in part from the injection of blood into the vascular system. The sensor bed software can extract BCG-based health parameters such as heart rate and respiration rate from data acquired continuously throughout the night. Such a toolset creates a new challenge, namely that many people sleep on a shared bed. In such cases, a given sensor bed would acquire mixed BCGs that contain information for both people. This thesis documents efforts to create an algorithm to extract individual health parameters from mixed parent BCGs obtained from bed sensors that reside on a shared bed. The first component of the two-part algorithm performs ‘blind source separation:’ a technique originally designed for mixed audio applications that attempts to optimally separate two individual BCGs contained in an original mixed signal. The second component of the algorithm utilizes a frequency-domain, peak-scoring method to identify the most likely fundamental BCG harmonic for each separated signal – a harmonic that corresponds to the pulse rate for that individual. The peak-scoring approach allows the algorithm to overcome challenges associated with different time-domain BCG waveform shapes, the presence of signal artifact, and the loss of BCG characteristic features that occurs during the separation stage. These challenges can be problematic for time-domain pulse rate algorithms, but the repetitive waveform patterns can be exploited in the frequency-spectrum. The peak-scoring algorithm was verified by comparing pulse rates determined from single-subject BCGs (obtained in various sleeping positions) against pulse rates determined from simultaneously collected electrocardiograms. The separation and peak-scoring components were combined together, and this overall technique was applied to over 20 sets of paired BCG data, with variations in sensor placement, sensor type and mattress type. Early results indicate the ability of the algorithm to determine pulse rates from mixed BCGs with acceptable levels of success but with areas for improvement

Towards Computer-Assisted Regulation of Emotions

Tunteet ovat keskeinen ja erottamaton osa ihmisen toimintaa, ajattelua ja yksilöiden välistä vuorovaikutusta. Tunteet luovat perustan mielekkäälle, toimivalle ja tehokkaalle toiminnalle. Joskus tunteiden sävy tai voimakkuus voi kuitenkin olla epäedullinen henkilön tavoitteiden ja hyvinvoinnin kannalta. Tällöin taidokas tunteiden säätely voi auttaa saavuttamaan terveen ja menestyksellisen elämän. Väitöstyön tavoitteena oli muodostaa perusta tulevaisuuden tietokoneille, jotka auttavat säätelemään tunteita. Tietokoneiden tunneälyä on toistaiseksi kehitetty kahdella alueella: ihmisen tunnereaktioiden mittaamisessa ja tietokoneen tuottamissa tunneilmaisuissa. Viimeisimmät teknologiat antavat tietokoneille jo mahdollisuuden tunnistaa ja jäljitellä ihmisen tunneilmaisuja hyvinkin tarkasti. Väitöstyössä toimistotuoliin asennetuilla paineantureilla kyettiin huomaamattomasti havaitsemaan muutoksia kehon liikkeissä: osallistujat nojautuivat kohti heille esitettyjä tietokonehahmoja. Tietokonehahmojen esittämät kasvonilmeet ja kehollinen etäisyys vaikuttivat merkittävästi osallistujien tunne- ja tarkkaavaisuuskokemuksiin sekä sydämen, ihon hikirauhasten ja kasvon lihasten toimintaan. Tulokset osoittavat että keinotekoiset tunneilmaisut voivat olla tehokkaita henkilön kokemusten ja kehon toiminnan säätelyssä. Väitöstyössä laadittiin lopulta vuorovaikutteinen asetelma, jossa tunneilmaisujen automaattinen tarkkailu liitettiin tietokoneen tuottamien sosiaalisten ilmaisujen ohjaamiseen. Osallistujat pystyivät säätelemään välittömiä fysiologisia reaktioitaan ja tunnekokemuksiaan esittämällä tahdonalaisia kasvonilmeitä (mm. ikään kuin hymyilemällä) heitä lähestyvälle tietokonehahmolle. Väitöstyön tuloksia voidaan hyödyntää laajasti, muun muassa uudenlaisten, ihmisen luonnollisia vuorovaikutustapoja paremmin tukevien tietokoneiden suunnittelussa.Emotions are intimately connected with our lives. They are essential in motivating behaviour, for reasoning effectively, and in facilitating interactions with other people. Consequently, the ability to regulate the tone and intensity of emotions is important for leading a life of success and well-being. Intelligent computer perception of human emotions and effective expression of virtual emotions provide a basis for assisting emotion regulation with technology. State-of-the-art technologies already allow computers to recognize and imitate human social and emotional cues accurately and in great detail. For example, in the present work a regular looking office chair was used to covertly measure human body movement responses to artifical expressions of proximity and facial cues. In general, such artificial cues from visual agents were found to significantly affect heart, sweat gland, and facial muscle activities, as well as subjective experiences of emotion and attention. The perceptual and expressive capabilities were combined in a setup where a person regulated her or his more spontaneous reactions by either smiling or frowning voluntarily to a virtual humanlike character. These results highlight the potential of future emotion-sensitive technologies for creating supportive and even healthy interactions between humans and computers

A Multi-Resident Number Estimation Method for Smart Homes

Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%

Quality of heart rate variability features obtained from ballistocardiograms

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringDavid E. ThompsonHeartbeat intervals (HBIs) vary over time, and that variance can be quantified as heart rate variability (HRV). HRV has several health-related applications including long-term health monitoring and sleep quality assessment. The focus of this research is obtaining HRV from ballistocardiograms (BCGs), force signals caused by micro-movements of the human body in response to blood ejections. This method of HRV estimation is attractive because it does not require direct attachment of any sensor to the body. However, the HBIs and corresponding HRV measured with BCGs are different than those obtained via electrocardiograms (ECGs), signals obtained by attaching electrodes to the body to detect electrical heart activity. Because ECG-based HRV is typically considered ground truth, differences in BCG-based versus ECG-based parameters are referred to as HBI and HRV errors. This research investigates the effects of HBI error on HRV feature quality. While a few studies have used BCG-based HBIs to estimate HRV features for sleep staging, the effects of HBI error on the quality of the resulting HRV features seem to have been overlooked. As a result, an acceptable HBI error range has not been defined. One contribution of this work is the development of such an acceptable error range. This dissertation work (i) develops a hardware and software system necessary to record BCGs and to perform BCG peak detection to obtain HBIs with the least possible error, (ii) determines an allowable range for HBI error by studying the effects of this error on HRV quality in the context of HRV-based sleep staging, and (iii) compares the determined acceptable HBI error range to the HBI error of our final system. The inherent error in BCG-based HBI determination due to physiological and platform effects is also taken into account in this comparison. A minimum HBI error of 20 ms was obtained from the system developed in (i), and the allowable error range was determined to be 30 ms based on the investigations conducted in (ii). The combined physiological and platform effects led to an error of 8.8 ms on average. Based on the comparisons conducted in (iii), the developed system is suitable for long-term sleep quality assessment. In addition, the effects of the HBI errors introduced by this system on the resulting HRV features are negligible in the sleep staging context