A Behaviour Monitoring System (BMS) for Ambient Assisted Living

Unusual changes in the regular daily mobility routine of an elderly person at home can be an indicator or early symptom of developing health problems. Sensor technology can be utilised to complement the traditional healthcare systems to gain a more detailed view of the daily mobility of a person at home when performing everyday tasks. We hypothesise that data collected from low-cost sensors such as presence and occupancy sensors can be analysed to provide insights on the daily mobility habits of the elderly living alone at home and to detect routine changes. We validate this hypothesis by designing a system that automatically learns the daily room-to-room transitions and permanence habits in each room at each time of the day and generates alarm notifications when deviations are detected. We present an algorithm to process the sensors' data streams and compute sensor-driven features that describe the daily mobility routine of the elderly as part of the developed Behaviour Monitoring System (BMS). We are able to achieve low detection delay with confirmation time that is high enough to convey the detection of a set of common abnormal situations. We illustrate and evaluate BMS with synthetic data, generated by a developed data generator that was designed to mimic different user's mobility profiles at home, and also with a real-life dataset collected from prior research work. Results indicate BMS detects several mobility changes that can be symptoms of common health problems. The proposed system is a useful approach for learning the mobility habits at the home environment, with the potential to detect behaviour changes that occur due to health problems, and therefore, motivating progress toward behaviour monitoring and elder's care.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT—Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

A smart home environment to support safety and risk monitoring for the elderly living independently

The elderly prefer to live independently despite vulnerability to age-related challenges. Constant monitoring is required in cases where the elderly are living alone. The home environment can be a dangerous environment for the elderly living independently due to adverse events that can occur at any time. The potential risks for the elderly living independently can be categorised as injury in the home, home environmental risks and inactivity due to unconsciousness. The main research objective was to develop a Smart Home Environment (SHE) that can support risk and safety monitoring for the elderly living independently. An unobtrusive and low cost SHE solution that uses a Raspberry Pi 3 model B, a Microsoft Kinect Sensor and an Aeotec 4-in-1 Multisensor was implemented. The Aeotec Multisensor was used to measure temperature, motion, lighting, and humidity in the home. Data from the multisensor was collected using OpenHAB as the Smart Home Operating System. The information was processed using the Raspberry Pi 3 and push notifications were sent when risk situations were detected. An experimental evaluation was conducted to determine the accuracy with which the prototype SHE detected abnormal events. Evaluation scripts were each evaluated five times. The results show that the prototype has an average accuracy, sensitivity and specificity of 94%, 96.92% and 88.93% respectively. The sensitivity shows that the chance of the prototype missing a risk situation is 3.08%, and the specificity shows that the chance of incorrectly classifying a non-risk situation is 11.07%. The prototype does not require any interaction on the part of the elderly. Relatives and caregivers can remotely monitor the elderly person living independently via the mobile application or a web portal. The total cost of the equipment used was below R3000

Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation

The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders. The strain this places on services means providing 24-hour monitoring is not sustainable. No solution exists to non-intrusively monitor the wellbeing of patients with dementia, resulting in delayed intervention. Using machine learning and signal processing, domestic energy supplies can be disaggregated to detect appliance usage. This enables Activities of Daily Living (ADLs) to be assessed. The aim is to facilitate early intervention and enable patients to stay in their homes for longer. A Support Vector Machine (SVM) and Random Decision Forest classifier are modelled using data from three test homes. The trained models are then used to monitor two patients with dementia during a six-month clinical trial undertaken in partnership with Mersey Care NHS Foundation Trust. In the case of load disaggregation, the SVM achieved (AUC=0.86074, Sen=0.756 and Spec=0.92838). While the Decision Forest achieved (AUC=0.9429, Sen=0.9634 and Spec=0.9634). ADLs are also analysed to identify the behavioural patterns of the occupant while detecting alterations in routine. The approach is sensitive in identifying behavioural routines and detecting anomalies in patient behaviour

Behavioural modelling for ambient assisted living

Tese de doutoramento - MAP-i (University of Minho, Aveiro, and Porto)A mudança incomum na rotina diária ao nível da mobilidade de um idoso em sua casa, pode ser um sinal ou sintoma precoce para a possibilidade de vir a desenvolver um problema de saúde. O recurso a diferentes sensores pode ser um meio para complementar os sistemas de cuidados de saúde tradicionais, de forma a obter uma visão mais detalhada da mobilidade diária do individuo em sua casa, enquanto realiza as suas tarefas diárias. Acreditamos, que os dados recolhidos a partir de sensores de baixo custo, como sensores de presença e ocupação, podem ser utilizados para fornecer evidências sobre os hábitos diários de mobilidade dos idosos que vivem sozinhos em casa e detetar desta forma mudanças nas suas rotinas. Neste trabalho, validamos esta hipótese, desenvolvendo um sistema que aprende automaticamente as transições diárias entre divisões da habitação e hábitos de estadia em cada uma dessas divisões em cada momento do dia e consequentemente gera alarmes sempre que os desvios são detetados. Apresentamos neste trabalho um algoritmo que processa os fluxos de dados dos diferentes sensores e identifica características que descrevem a rotina diária de mobilidade de um idoso que vive sozinho em casa. Para isso foi definido um conjunto de dimensões baseadas nos dados extraídos dos sensores, como parte do nosso Behaviour Monitoring System (BMS). Fomos capazes de detetar com um atraso mínimo os comportamentos incomuns e ao mesmo tempo, durações de confirmação da deteção elevadas, de tal modo suficientes para um conjunto comum de situações anormais. Apresentamos e avaliamos o BMS com dados sintetizados, produzidos por um gerador de dados desenvolvido para este efeito e projetado para simular diferentes perfis de mobilidade de indivíduos em casa, e também com dados reais obtidos de trabalhos de investigação anteriores. Os resultados indicam que o BMS deteta várias mudanças de mobilidade que podem ser sintomas para problemas de saúde comuns. O sistema proposto é uma abordagem útil para a aprendizagem dos hábitos de mobilidade em ambientes domésticos, com potencial para detetar alterações comportamentais que ocorrem devido a problemas de saúde, e assim encorajar a monitorização dos comportamentos e dos cuidados de saúde dos idosos.Unusual changes in the regular daily mobility routine of an elderly at home can be an indicator or early symptoms for developing a health problem. Sensor technology can be utilised to complement the traditional healthcare systems to gain a more detailed view of the daily mobility of a person at home when performing everyday tasks. We hypothesise that data collected from low-cost sensors such as presence and occupancy sensors can be analysed to provide insights on the daily mobility habits of the elderly living alone at home and to detect routine changes. We validate this hypothesis by designing a system that automatically learns the daily room-to-room transitions and stays habits in each room at each time of the day and generates alarm notifications when deviations are detected. We present an algorithm to process the sensor data streams and compute features that describe the daily mobility routine of an elderly living alone at home. This was done by defining a set of sensor-driven dimensions extracted from the sensor data as part of our Behaviour Monitoring System (BMS). We are able to achieve low detection delay with confirmation time that is high enough to convey the detection of a set of common abnormal situations. We illustrate and evaluate BMS with synthetic data, generated by a developed data generator that was designed to mimic different users’ mobility profiles at home, and also with real-life dataset collected from prior research work. Results indicate BMS detects several mobility changes that can be symptoms of common health problems. The proposed system is a useful approach for learning the mobility habits at home environments, with the potential to detect behaviour changes that occur due to health problems, and therefore, motivating progress toward behaviour monitoring and elder’s care

Early diagnosis of disorders based on behavioural shifts and biomedical signals

There are many disorders that directly affect people’s behaviour. The people that are suffering from such a disorder are not aware of their situation, and too often the disorders are identified by relatives or co-workers because they notice behavioural shifts. However, when these changes become noticeable, it is often too late and irreversible damages have already been produced. Early detection is the key to prevent severe health-related damages and healthcare costs, as well as to improve people’s quality of life. Nowadays, in full swing of ubiquitous computing paradigm, users’ behaviour patterns can be unobtrusively monitored by means of interactions with many electronic devices. The application of this technology for the problem at hand would lead to the development of systems that are able to monitor disorders’ onset and progress in an ubiquitous and unobtrusive way, thus enabling their early detection. Some attempts for the detection of specific disorders based on these technologies have been proposed, but a global methodology that could be useful for the early detection of a wide range of disorders is still missing. This thesis aims to fill that gap by presenting as main contribution a global screening methodology for the early detection of disorders based on unobtrusive monitoring of physiological and behavioural data. The proposed methodology is the result of a cross-case analysis between two individual validation scenarios: stress in the workplace and Alzheimer’s Disease (AD) at home, from which conclusions that contribute to each of the two research fields have been drawn. The analysis of similarities and differences between the two case studies has led to a complete and generalized definition of the steps to be taken for the detection of a new disorder based on ubiquitous computing.Jendearen portaeran eragin zuzena duten gaixotasun ugari daude. Hala ere, askotan, gaixotasuna pairatzen duten pertsonak ez dira euren egoerataz ohartzen, eta familiarteko edo lankideek identifikatu ohi dute berau jokabide aldaketetaz ohartzean. Portaera aldaketa hauek nabarmentzean, ordea, beranduegi izan ohi da eta atzerazeinak diren kalteak eraginda egon ohi dira. Osasun kalte larriak eta gehiegizko kostuak ekiditeko eta gaixoen bizi kalitatea hobetzeko gakoa, gaixotasuna garaiz detektatzea da. Gaur egun, etengabe zabaltzen ari den Nonahiko Konputazioaren paradigmari esker, erabiltzaileen portaera ereduak era diskretu batean monitorizatu daitezke, gailu teknologikoekin izandako interakzioari esker. Eskuartean dugun arazoari konponbidea emateko teknologi hau erabiltzeak gaixotasunen sorrera eta aurrerapena nonahi eta era diskretu batean monitorizatzeko gai diren sistemak garatzea ekarriko luke, hauek garaiz hautematea ahalbidetuz. Gaixotasun konkretu batzuentzat soluzioak proposatu izan dira teknologi honetan oinarrituz, baina metodologia orokor bat, gaixotasun sorta zabal baten detekzio goiztiarrerako erabilgarria izango dena, oraindik ez da aurkeztu. Tesi honek hutsune hori betetzea du helburu, mota honetako gaixotasunak garaiz hautemateko, era diskretu batean atzitutako datu fisiologiko eta konportamentalen erabileran oinarritzen den behaketa sistema orokor bat proposatuz. Proposatutako metodologia bi balidazio egoera desberdinen arteko analisi gurutzatu baten emaitza da: estresa lantokian eta Alzheimerra etxean, balidazio egoera bakoitzari dagozkion ekarpenak ere ondorioztatu ahal izan direlarik. Bi kasuen arteko antzekotasun eta desberdintasunen analisiak, gaixotasun berri bat nonahiko konputazioan oinarrituta detektatzeko jarraitu beharreko pausoak bere osotasunean eta era orokor batean definitzea ahalbidetu du

A smart home environment to support safety and risk monitoring for the elderly living independently

The elderly prefer to live independently despite vulnerability to age-related challenges. Constant monitoring is required in cases where the elderly are living alone. The home environment can be a dangerous environment for the elderly living independently due to adverse events that can occur at any time. The potential risks for the elderly living independently can be categorised as injury in the home, home environmental risks and inactivity due to unconsciousness. The main research objective was to develop a Smart Home Environment (SHE) that can support risk and safety monitoring for the elderly living independently. An unobtrusive and low cost SHE solution that uses a Raspberry Pi 3 model B, a Microsoft Kinect Sensor and an Aeotec 4-in-1 Multisensor was implemented. The Aeotec Multisensor was used to measure temperature, motion, lighting, and humidity in the home. Data from the multisensor was collected using OpenHAB as the Smart Home Operating System. The information was processed using the Raspberry Pi 3 and push notifications were sent when risk situations were detected. An experimental evaluation was conducted to determine the accuracy with which the prototype SHE detected abnormal events. Evaluation scripts were each evaluated five times. The results show that the prototype has an average accuracy, sensitivity and specificity of 94%, 96.92% and 88.93% respectively. The sensitivity shows that the chance of the prototype missing a risk situation is 3.08%, and the specificity shows that the chance of incorrectly classifying a non-risk situation is 11.07%. The prototype does not require any interaction on the part of the elderly. Relatives and caregivers can remotely monitor the elderly person living independently via the mobile application or a web portal. The total cost of the equipment used was below R3000