689 research outputs found
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Identification and prediction of abnormal behaviour activities of daily living in intelligent environments
The aim of this research is to investigate efficient mining of useful information from a sensor network forming an Ambient Intelligence (AmI) environment. In this thesis, we investigate methods for supporting independent living of the elderly (and specifically patients who are suffering from dementia) by means of equipping their home with a simple sensor network to monitor their behaviour and identify their Activities of Daily Living (ADL). Dementia is considered to be one of the most important causes of disability in the elderly. Mostpatients would prefer to use non-intrusive technology to help them tomaintain their independence. Such monitoring and prediction would allow the caregiver to see any trend in the behaviour of the elderly person and to be informed of any abnormal behaviour
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Trend analysis for human activities recognition
Smart environments equipped with appropriate sensory devices are used to measure people's activities. These activities represent Activities of Daily Living (ADL) or Activities of Daily Working (ADW). Measuring progressive changes in activities is a subject of research interest. A number of medical conditions and their treatments are associated with progressive changes such as reduced movement over time.
The aim of this research is to determine means of inspecting trends in the ADL/ADW to identify progressive changes and predict behavioural abnormalities. The ADL/ADW pattern will change over time and this is a consequence of the individual's condition. Identifying evolving behavioural patterns will help to predict the trend in the ADL/ADW behavioural pattern before any abnormalities are identifed. The data provided for this investigation are from real environments home and office). Additionally, a simulator is developed to generate simulated data for ADLs.
To answer the research question identifed in this research, the initial investigation was conducted and a novel Human Behaviour Momentum Indicator (HBMI) is proposed. The HBMI is introduced to identify changes based on activities recorded from a single sensor. To show the effectiveness of the proposed approach, results are compared with Relative Strength Index (RSI). The results show that trends in ADL or ADW can be detected and the direction of the activity's trend is predicted.
To represent a holistic report based on a multiple sensors/activities representing progressive changes in the participant's behaviour, a novel Human Behaviour Indicator (HBI) is also proposed. The proposed HBI indicator is constructed as a composite indicator, which will compute progressive changes in behaviour based on the events that are performed during the entire day. The percentage of changes between events is used to compare events and measure the progressive changes. The proposed technique identifies the user's daily behaviour and distinguishes between normal and abnormal behavioural patterns of the ADLs or ADWs. Analysis of the data indicates that the HBI could clearly differentiate between the normal and the abnormal behaviour and give a warning status with a confidence level.
Identifying trends in ADLs or ADWs using trend analysis techniques are investigated to interpret the behavioural changes in a suitable format to be understood by the carers or supervisors
Development of a simulation tool for measurements and analysis of simulated and real data to identify ADLs and behavioral trends through statistics techniques and ML algorithms
openCon una popolazione di anziani in crescita, il numero di soggetti a rischio di patologia Ăš in rapido aumento. Molti gruppi di ricerca stanno studiando soluzioni pervasive per monitorare continuamente e discretamente i soggetti fragili nelle loro case, riducendo i costi sanitari e supportando la diagnosi medica. Comportamenti anomali durante l'esecuzione di attivitĂ di vita quotidiana (ADL) o variazioni sulle tendenze comportamentali sono di grande importanza.With a growing population of elderly people, the number of subjects at risk of pathology is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Anomalous behaviors while performing activities of daily living (ADLs) or variations on behavioral trends are of great importance. To measure ADLs a significant number of parameters need to be considering affecting the measurement such as sensors and environment characteristics or sensors disposition. To face the impossibility to study in the real context the best configuration of sensors able to minimize costs and maximize accuracy, simulation tools are being developed as powerful means. This thesis presents several contributions on this topic. In the following research work, a study of a measurement chain aimed to measure ADLs and represented by PIRs sensors and ML algorithm is conducted and a simulation tool in form of Web Application has been developed to generate datasets and to simulate how the measurement chain reacts varying the configuration of the sensors. Starting from eWare project results, the simulation tool has been thought to provide support for technicians, developers and installers being able to speed up analysis and monitoring times, to allow rapid identification of changes in behavioral trends, to guarantee system performance monitoring and to study the best configuration of the sensors network for a given environment. The UNIVPM Home Care Web App offers the chance to create ad hoc datasets related to ADLs and to conduct analysis thanks to statistical algorithms applied on data. To measure ADLs, machine learning algorithms have been implemented in the tool. Five different tasks have been identified. To test the validity of the developed instrument six case studies divided into two categories have been considered. To the first category belong those studies related to: 1) discover the best configuration of the sensors keeping environmental characteristics and user behavior as constants; 2) define the most performant ML algorithms. The second category aims to proof the stability of the algorithm implemented and its collapse condition by varying user habits. Noise perturbation on data has been applied to all case studies. Results show the validity of the generated datasets. By maximizing the sensors network is it possible to minimize the ML error to 0.8%. Due to cost is a key factor in this scenario, the fourth case studied considered has shown that minimizing the configuration of the sensors it is possible to reduce drastically the cost with a more than reasonable value for the ML error around 11.8%. Results in ADLs measurement can be considered more than satisfactory.INGEGNERIA INDUSTRIALEopenPirozzi, Michel
Integrated design of transport infrastructure and public spaces considering human behavior: A review of state-of-the-art methods and tools
In order to achieve holistic urban plans incorporating transport infrastructure, public space and the behavior of people in these spaces, integration of urban design and computer modeling is a promising way to provide both qualitative and quantitative support to decision-makers. This paper describes a systematic literature review following a four-part framework. Firstly, to understand the relationship of elements of transport, spaces, and humans, we review policy and urban design strategies for promoting positive interactions. Secondly, we present an overview of the integration methods and strategies used in urban design and policy discourses. Afterward, metrics and approaches for evaluating the effectiveness of integrated plan alternatives are reviewed. Finally, this paper gives a review of state-of-the-art tools with a focus on seven computer simulation paradigms. This article explores mechanisms underlying the complex system of transport, spaces, and humans from a multidisciplinary perspective to provide an integrated toolkit for designers, planners, modelers and decision-makers with the current methods and their challenges
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The human behaviour indicator: a measure of behavioural evolution
Activities of daily living (ADL) or activities of daily working (ADW) may be affected by changes in a personâs health or well-being. Measuring progressive changes in one activity or multiple activities is representative of behavioural variations. By inspecting the trends in multiple activities, it is possible to identify and predict human behavioural changes. We refer to the trends in people's behaviour as behavioural evolution. In this paper, we propose a novel indicator to measure the progressive changes representing a participant's behavioural evolution. The proposed indicator presents activities as a holistic measure, which first combine multi-activities and then measure the progressive changes in the combined activities for each single day.
Real data sets were collected from a wireless sensor network and used to examine our proposed technique. As part of this process, we were able to quantify progressive changes for individual and aggregated activities. Our experimental results demonstrated that: (1) the proposed approach can identify and distinguish normal and abnormal behaviours; (2) large data sets gathered from sensors in an intelligent environment represented in various time series can be visualised in a simple and more understandable format; (3) identifying trends in ADLs or ADWs is a relevant means of sharing information with carers or supervisors
EstimaciĂłn del impacto ambiental y social de los nuevos servicios de movilidad
El transporte es fuente de numerosas externalidades negativas, como los accidentes de trĂĄfico, la congestiĂłn en las zonas urbanas y la falta de calidad del aire. El transporte tambiĂ©n es un sector que contribuye sustancialmente a la crisis climĂĄtica con mĂĄs del 16% de las emisiones globales de gases de efecto invernadero como resultado de las actividades de transporte. Muchos creen que la introducciĂłn de nuevos servicios de movilidad podrĂa ayudar a reducir esas externalidades. Sin embargo, con cada introducciĂłn de un nuevo servicio de movilidad podemos observar factores que podrĂan contribuir negativamente a la sostenibilidad del sistema de transporte: una cadena de cambios de comportamiento causados por la introducciĂłn de posibilidades completamente nuevas. El objetivo de esta tesis es investigar cĂłmo los nuevos servicios de movilidad, habilitados por la electrificaciĂłn, la conectividad y la automatizaciĂłn, podrĂan impactar en las externalidades causadas por el transporte. En particular, el objetivo es desarrollar y validar un marco de modelado capaz de capturar la complejidad del sistema de transporte y aplicarlo para evaluar el impacto potencial de los vehĂculos automatizados.Transport is a source of numerous negative externalities, such as road accidents, congestion in urban areas and lacking air quality. Transport is also a sector substantially contributing to climate crisis with more than 16% of global greenhouse gas emissions being a result of transport activities. Many believe that the introduction of new mobility services could help reduce those externalities. However, with each introduction of a new mobility service we can observe factors that could negatively contribute to the sustainability of the transport system â a chain of behavioural changes caused by introduction of entirely new possibilities. The aim of this thesis is to investigate how the new mobility services, enabled by electrification, connectivity and automation, could impact the externalities caused by transport. In particular the objective is to develop and validate a modelling framework able to capture the complexity of the transport system and to apply it to assess the potential impact of automated vehicles.This work was realised with the collaboration of the European Commission Joint Research Centre under the Collaborative Doctoral Partnership Agreement N035297. Moreover, this research has been partially funded by the Spanish Ministry of Science and Innovation through the
project: AUTONOMOUS â InnovAtive Urban and Transport planning tOols for the implementation of New mObility systeMs based On aUtonomouS drivingâ, 2020-2023, ERDF
(EU) (PID2019-110355RB-I00)
Developing service supply chains by using agent based simulation
The Master thesis present a novel approach to model a service supply chain with agent based simulation. Also, the case study of thesis is related to healthcare services and research problem includes facility location of healthcare centers in Vaasa region by considering the demand, resource units and service quality. Geographical information system is utilized for locating population, agent based simulation for patients and their illness status probability, and discrete event simulation for healthcare services modelling. Health centers are located on predefined sites based on managersâ preference, then each patient based on the distance to health centers, move to the nearest point for receiving the healthcare services. For evaluating cost and services condition, various key performance indicators have defined in the modelling such as Number of patient in queue, patients waiting time, resource utilization, and number of patients ratio yielded by different of inflow and outflow. Healthcare managers would be able to experiment different scenarios based on changing number of resource units or location of healthcare centers, and subsequently evaluate the results without necessity of implementation in real life.fi=OpinnĂ€ytetyö kokotekstinĂ€ PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=LĂ€rdomsprov tillgĂ€ngligt som fulltext i PDF-format
Modélisation du comportement humain réactif et délibératif avec une approche multi-agent pour la gestion énergétique dans le bùtiment
Energy consumption in buildings is affected by various factors including its physical characteristics, the appliances inside, and the outdoor environment, etc. However, inhabitantsâ behaviour that determines the global energy consumption must not be forgotten. In most of the previous works and simulation tools, human behaviour is modelled as occupancy profiles. In this thesis the focus is more on detailed behaviour representation, particularly the cognitive, reactive, and deliberative mechanisms. The inhabitantsâ dynamic behaviour is modelled and co-simulated together with the physical aspects of a building and an energy management system. The analysis of different household appliances has revealed that energy consumption patterns are highly associated with inhabitantsâ behaviours. Data analysis of inhabitantsâ actions and appliancesâ consumptions is used to derive a model of inhabitantsâ behaviour that impacts the energy consumption. This model represents the cognitive mechanisms that provide causes that motivate the actions, including the communication with other inhabitants. An approach based on multi-agent systems is developed along with a methodology for parameter tuning in the proposed behaviour model. These tools are used to co-simulate, not only the physical characteristics of the building, the reactive behaviour that is sensitive to physical data, and deliberative behaviour of the inhabitants, but also the building energy management system. The energy management system allows the direct adjustment of the building parameters or simply giving advice to the inhabitants. The impact of different types of inhabitantsâ behaviours, with and without the inclusion of an energy management system is analyzed. This work opens new perspectives not only in the building simulation and in the validation of energy management systems but also in the representation of buildings in the smart grid where signals can be sent to end users advising them to modulate their consumption.La consommation Ă©nergĂ©tique dans le secteur bĂątiment dĂ©pend de diverses facteurs parmi lesquels ses caractĂ©ristiques physique, ses Ă©quipements, lâenvironnement extĂ©rieur, etc⊠mais il ne faut pas oublier le comportement des habitants qui est dĂ©terminant pour la consommation Ă©nergĂ©tique globale. Or, la plupart des travaux et outils reprĂ©sentent les occupants par des profils dâoccupation. Cette thĂšse sâintĂ©resse Ă la reprĂ©sentation plus dĂ©taillĂ©e du comportement des occupants, en particulier les mĂ©canismes cognitifs, rĂ©actifs et dĂ©libĂ©ratifs. Le comportement dynamique des occupants est modĂ©lisĂ© et co-simulĂ© avec les aspects physiques et des Ă©ventuels systĂšmes de gestion Ă©nergĂ©tique. Lâanalyse de la consommation de diffĂ©rents Ă©quipements Ă©lectromĂ©nagers met en Ă©vidence que le consommation Ă©nergĂ©tique est trĂšs dĂ©pendante des comportements des occupants. Lâanalyse des consommations et des actions des habitants permet dâĂ©laborer un modĂšle du comportement des occupants impactant la consommation Ă©nergĂ©tique. Le modĂšle reprĂ©sente des mĂ©canismes cognitifs, qui reprĂ©sente les causes qui motivent les actions, incluant des Ă©change avec dâautres acteurs humains. Une approche Ă base dâagents logiciels a Ă©tĂ© dĂ©veloppĂ©e. Outre les aspects techniques, une mĂ©thodologie de rĂ©glage des paramĂštres des modĂšles de comportement est proposĂ©e. Ces outils sont utilisĂ©s pour rĂ©aliser une co-simulation reprĂ©sentant la physique du bĂątiment, le comportement rĂ©actif, câest-Ă -dire sensible aux donnĂ©es physiques, et dĂ©libĂ©ratif des habitants mais aussi un systĂšme de gestion Ă©nergĂ©tique qui peut ajuster directement la configuration du logement ou simplement conseiller ces occupants. Lâimpact de diffĂ©rents types de comportements, avec et sans gestionnaire Ă©nergĂ©tique est analysĂ©. Ces travaux ouvrent de nouvelles perspectives dans la simulation bĂątiment, dans la validation de gestionnaires Ă©nergĂ©tiques mais aussi dans la reprĂ©sentation des bĂątiments dans les rĂ©seaux dâĂ©nergie dits intelligents, dans lesquels des signaux peuvent ĂȘtre envoyĂ©s aux utilisateurs finaux pour les inviter Ă moduler leur consommation
Determining behavioural-based risk to SLODs of urban public open spaces: Key performance indicators definition and application on established built environment typological scenarios
A behavioural-based approach can be used to assess how usersâ reactions to surrounding environmental conditions can alter the urban Built Environment (BE) risk to Slow Onset Disasters (SLODs). Public Open Spaces (POSs) in the BE are relevant scenarios, due to micro-climate-related stress, usersâ vulnerabilities (e.g., age, health frailty) and exposure time. Simulation methods can support behavioural-based risk-assessment, but results are generally site-specific. Performing analysis on BE Typologies (BETs) can improve robustness, since BETs represent archetypes from real-world scenarios. This work adopts a behavioural-based approach to evaluate time-dependant usersâ risks of POSs in different BETs due to SLODs-related stress (i.e., heat, air pollution). UTCI and AQI values are mapped within each BET. Usersâ distributions are then calculated depending on thermal acceptability correlations. Key Performance Indicators are developed associating usersâ distribution to SLODs effects on health (i.e., sweat rate, water loss; health affection rate probability). The approach is applied to Italian BETs, under one relevant climate, rating their heat and air pollution risks. Results suggest critical conditions for toddlers. In detail, about 2-hour high heat exposure could result in dehydration, while 1-hour exposure to low NO2 concentration could result in +1% mortality probability. This approach could potentially support decision-makers on BE risk-assessment
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
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