Use Cases for Abnormal Behaviour Detection in Smart Homes

While people have many ideas about how a smart home should react to particular behaviours from their inhabitant, there seems to have been relatively little attempt to organise this systematically. In this paper, we attempt to rectify this in consideration of context awareness and novelty detection for a smart home that monitors its inhabitant for illness and unexpected behaviour. We do this through the concept of the Use Case, which is used in software engineering to specify the behaviour of a system. We describe a set of scenarios and the possible outputs that the smart home could give and introduce the SHMUC Repository of Smart Home Use Cases. Based on this, we can consider how probabilistic and logic-based reasoning systems would produce different capabilities

Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection

This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from dense sensor data collected from 30 participants. The ADLs considered are related to preparing and drinking (i) tea, and (ii) coffee. Abnormal behaviour identified in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. The approach presented considers the temporal and sequential aspects of the actions that are part of each ADL and that vary between participants. The average and standard deviation for the duration and number of steps of each activity are calculated to define the average time and steps and a range within which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) is used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity in terms of time and steps. Analysis shows that CDF can provide precise and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute or consist of many steps. Finally, this approach could be used to train machine learning algorithms for abnormal behaviour detection.status: publishe

Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions

The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.Comment: This work has been accepted by IEEE Transactions on Emerging Topics in Computational Intelligenc

Locomotion Traces Data Mining for Supporting Frail People with Cognitive Impairment

The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of older people. Thus, this thesis at first focused on providing a systematic literature review of locomotion data mining systems for supporting Neuro-Degenerative Diseases (NDD) diagnosis, identifying locomotion anomaly indicators and movement patterns for discovering low-level locomotion indicators, sensor data acquisition, and processing methods, as well as NDD detection algorithms considering their pros and cons. Then, we investigated the use of sensor data and Deep Learning (DL) to recognize abnormal movement patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduced novel visual feature extraction methods for locomotion data. Our solutions rely on locomotion traces segmentation, image-based extraction of salient features from locomotion segments, and vision-based DL. Furthermore, we proposed a data augmentation strategy to increase the volume of collected data and generalize the solution to different smart-homes with different layouts. We carried out extensive experiments with a large real-world dataset acquired in a smart-home test-bed from older people, including people with cognitive diseases. Experimental comparisons show that our system outperforms state-of-the-art methods

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors' knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table

Detecting abnormal events on binary sensors in smart home environments

With a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies.PostprintPeer reviewe

Activity-driven detection of cognitive impairment using deep learning.

While life expectancy is on the rise all over the world, more people face health related problems such as cognitive decline. Cognitive impairment is a collective name for progressive brain syndromes which affect memory, cognition, behaviour and emotion. People suffering from cognitive impairment may lose their abilities to perform daily life activities and they get dependent on their caregivers. Although some medications can slow the progress of the disease, currently there is no way to stop its development. Sufferers may require special needs which increase the cost of care. Thus, detecting the indicators of cognitive decline before it gets worse would be very crucial. Current assessment methods mostly rely on queries from questionnaires or in-person examinations, which depend on recall of events that may poorly represent a person’s typical state. The aim in this thesis is to adapt deep learning techniques for analysing daily activities of elderly people and detecting abnormalities in the activity patterns. Recent studies suggest that indicators of cognitive decline can be observed in daily life activity patterns. The spatio-temporal and hierarchical relationship of activities and their intrinsic structures are important in the context of cognitive decline analysis. Existing studies treat each activity as an atomic unit and fail to capture the relationship among sub-activities. Also, existing studies rely on fixed length features to model activities, ignoring the granular level information coming from raw sensor activations. Moreover, there exists no daily activity dataset representing the behaviour of dementia sufferers because producing such datasets requires time and adequate experimental environment. Given these challenges, the present thesis addresses the following research questions: How can we cope with the scarcity of dataset reflecting on cognitive status of elderly people? How can activities be modelled taking into account their spatio-temporal neighbourhood and hierarchical information? How can we represent raw data to encode the granular level details? These research questions are addressed in the following way. Firstly, two methods are proposed to cope with the scarcity of data: (i) synthetic data generation and (ii) transfer learning adoption. Secondly, the activity recognition problem is emulated (i) as a sequence labelling problem to model spatio-temporal patterns. (ii) as a hierarchical learning problem to model sub-activities. (iii) as a graph labelling problem to encode granular level details. Thirdly, raw sensor measurements stemming from sequential data are used to model sensor activation relationships. The proposed methods are also compared against the state-of-art methods. The preliminary results obtained indicate that pro- posed data simulation and transfer learning approaches are useful to cope with the scarcity of data reflecting cognitive status of elderly people. Moreover, experiments show that the proposed deep learning methods are promising to detect abnormalities in the context of cognitive decline. Proposed methods are not only promising to detect abnormal behaviour at a fine-grained level, but some of them can also model activities hierarchically by taking sub-activities into account and then can detect abnormal behaviour occurring at granular levels