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

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

HalleyAssist: A Personalised Internet of Things Technology to Assist the Elderly in Daily Living

Ambient Assisted Living (AAL) research has received extensive attention in recent years. AAL applications combine aspects of Internet of Things (IoT), smart platform design and machine learning to produce an intelligent system. In this paper we describe a personalised IoT-based AAL system that enables an independent and safe life for elderly people within their own home via real-time monitoring and intervention. The system, HalleyAssist underpinned by smart home automation functions includes a novel approach for monitoring the wellbeing and detecting abnormal changes in behavioral patterns of an elderly person. The significance of the approach is in the use of machine learning models to automatically learn normal behavioral pattern for the person from IoT sensor data and using the models derived to detect significant changes in behavioral pattern should they occur. The architecture and developed proof of concept of the proposed system is presented along with discussion of how privacy and security concerns are addressed. We also report on outcomes of real-world in-home trials of an early version of the system where it was installed in four older people\u27s home for a period of six weeks. The response from the older people to the deployed system was very positive. Finally, the paper presents a discussion and an analysis of the results using the data collected during the in-home trials

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 person’s 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

Central monitoring system for ambient assisted living

Smart homes for aged care enable the elderly to stay in their own homes longer. By means of various types of ambient and wearable sensors information is gathered on people living in smart homes for aged care. This information is then processed to determine the activities of daily living (ADL) and provide vital information to carers. Many examples of smart homes for aged care can be found in literature, however, little or no evidence can be found with respect to interoperability of various sensors and devices along with associated functions. One key element with respect to interoperability is the central monitoring system in a smart home. This thesis analyses and presents key functions and requirements of a central monitoring system. The outcomes of this thesis may benefit developers of smart homes for aged care

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

User-centric anomaly detection in activities of daily living

The current system for providing care to older adults is not sustainable due to its excessive cost. It places an unbearable financial burden on the government and families and pressure on the workforce due to the demand for human carers. Studies have also shown that older adults prefer to be looked after in their homes rather than in a care facility. An automated system of monitoring can provide much-needed support at a lower cost and give peace of mind to relatives. The focus of the research reported in this thesis is to investigate the concept of abnormality detection in activities of daily living. More precisely, this work is aimed at proposing a dynamic approach for anomaly detection capable of adapting to changes in human behaviour. Abnormalities in daily activities can be an early indication of health decline. Therefore, early detection can inform the families of the need for intervention. Anomalies are often detected by modelling the existing activity data representing the usual behavioural routine of an individual to serve as a baseline model. Subsequent activities deviating from the baseline are then classified as outliers or anomalies. However, existing approaches suffer from a high rate of false prediction due to the static nature and the inability of the approaches to adapt to the changing human behaviour. The contributions of the research are reported in four main categories. First, a novel ensemble approach termed "Consensus Novelty Detection Ensemble" is proposed. The outlying activities are predicted by computing their normality score using the internal and external consensus vote and the estimated weights of the models in the ensemble. Activities with a score exceeding a threshold estimated using a statistical method based on data distribution are predicted as outliers and vice versa. Secondly, a similarity measure approach for identifying the likely sources of the ADL anomalies is proposed. While the models can detect anomalous activities, they are unable to identify the source (cause) of the anomaly. Identifying the anomaly source allows for the development of an adaptive system. The approach is based on a pairwise distance measurement of the features extracted from the activity data. Two approaches for performing the similarity measures are presented, namely, One vs One Similarity Measure (OOSM) and One vs All Similarity Measure (OASM). Features of the data with a higher dissimilarity rate are predicted as the source. To make the proposed model adaptive to the changes in human behaviour, a novel adaptive approach is proposed based on the concept of forgetting factors. This allows the model to forget (discard) outdated activity data and adapt to the current behavioural patterns by incorporating newly verified data. The data verification can be performed by incorporating human feedback into the system. Two forgetting factor approaches are proposed namely; Forgetting Factor based on Data Ageing (FFDD) and Forgetting Factor based on Data Dissimilarity (FFDA). The data ageing forgetting factor discard old behavioural routine based on the age of the activity data, while in the data dissimilarity approach, this is achieved by measuring the similarity of the activity data. Lastly, the means of utilising an assistive robot as a communication intermediary is explored for incorporating human feedback into the learning process using hand gestures as a communication modality. Experimental data used for the gesture recognition model is collected using a wearable sensor and a 2D camera. The feasibility of utilising the robotic platform as an exercise coach to encourage physical activity and promote a healthy lifestyle is explored. To this end, an exercise training solution is developed for the robotic platform to coach, motivate and assess the older adults in the recommended physical activities

A consensus novelty detection ensemble approach for anomaly detection in activities of daily living

A new approach to creating an ensemble of novelty detection algorithms is proposed in this paper. The novelty detection process identifies new or unknown data by detecting if a test data differs significantly from the data available during training. It is applicable for anomaly detection in a situation where there is sufficiently large training data representing the normal class and little or no training data for the anomalous (abnormal) class. Abnormality in Activities of Daily Living (ADL) is identified as any significant deviation from an individual’s usual behavioural routine. Novelty detection is relevant to ADL anomaly detection since abnormalities in ADL are rare and data representing the anomalous cases are not readily available. The proposed Consensus Novelty Detection Ensemble approach is based on the concept of internal and external consensus. The internal consensus is an internal voting scheme within each model in the ensemble while the external consensus is an external voting scheme among the ensemble models. The weight of each model is estimated based on its performance and a score, called “Normality Score”. Computed score is used in classifying the data as abnormal (anomalous) based on certain threshold crossing, normal otherwise. Experimental evaluation is conducted to detect abnormalities in ADL data obtained from CASAS repository as well as experimental dataset collected for this research. The obtained results show that the proposed approach is able to identify anomalous instances. The proposed approach offers more flexibility compared with the existing approaches by allowing the Normality Score threshold to be adjusted without retraining the models

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