A hybrid approach to recognising activities of daily living from object use in the home environment

Accurate recognition of Activities of Daily Living (ADL) plays an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object use for routine activities. Modelling activities from such information can lead to incorrect recognition of particular routine activities resulting in possible failure to detect abnormal activity trends. In cases where such prior knowledge are not available, such techniques become virtually unemployable. A significant step in the recognition of activities is the accurate discovery of the object usage for specific routine activities. This paper presents a hybrid framework for automatic consumption of sensor data and associating object usage to routine activities using Latent Dirichlet Allocation (LDA) topic modelling. This process enables the recognition of simple activities of daily living from object usage and interactions in the home environment. The evaluation of the proposed framework on the Kasteren and Ordonez datasets show that it yields better results compared to existing techniques

Recognition of activities of daily living from topic model

Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

Activities of daily life recognition using process representation modelling to support intention analysis

Purpose – This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge. Design/methodology/approach – This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients. Findings – A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches. Originality/value – The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features

Real-Time Sensor Observation Segmentation For Complex Activity Recognition Within Smart Environments

The file attached to this record is the author's final peer reviewed versionActivity Recognition (AR) is at the heart of any types of assistive living systems. One of the key challenges faced in AR is segmentation of the sensor events when inhabitant performs simple or composite activities of daily living (ADLs). In addition, each inhabitant may follow a particular ritual or a tradition in performing different ADLs and their patterns may change overtime. Many recent studies apply methods to segment and recognise generic ADLs performed in a composite manner. However, little has been explored in semantically distinguishing individual sensor events and directly passing it to the relevant ongoing/new atomic activities. This paper proposes to use the ontological model to capture generic knowledge of ADLs and methods which also takes inhabitant-specific preferences into considerations when segmenting sensor events. The system implementation was developed, deployed and evaluated against 84 use case scenarios. The result suggests that all sensor events were adequately segmented with 98% accuracy and the average classification time of 3971ms and 62183ms for single and composite ADL scenarios were recorded, respectively

A hierarchal framework for recognising activities of daily life

PhDIn today’s working world the elderly who are dependent can sometimes be neglected by society. Statistically, after toddlers it is the elderly who are observed to have higher accident rates while performing everyday activities. Alzheimer’s disease is one of the major impairments that elderly people suffer from, and leads to the elderly person not being able to live an independent life due to forgetfulness. One way to support elderly people who aspire to live an independent life and remain safe in their home is to find out what activities the elderly person is carrying out at a given time and provide appropriate assistance or institute safeguards. The aim of this research is to create improved methods to identify tasks related to activities of daily life and determine a person’s current intentions and so reason about that person’s future intentions. A novel hierarchal framework has been developed, which recognises sensor events and maps them to significant activities and intentions. As privacy is becoming a growing concern, the monitoring of an individual’s behaviour can be seen as intrusive. Hence, the monitoring is based around using simple non intrusive sensors and tags on everyday objects that are used to perform daily activities around the home. Specifically there is no use of any cameras or visual surveillance equipment, though the techniques developed are still relevant in such a situation. Models for task recognition and plan recognition have been developed and tested on scenarios where the plans can be interwoven. Potential targets are people in the first stages of Alzheimer’s disease and in the structuring of the library of kernel plan sequences, typical routines used to sustain meaningful activity have been used. Evaluations have been carried out using volunteers conducting activities of daily life in an experimental home environment. The results generated from the sensors have been interpreted and analysis of developed algorithms has been made. The outcomes and findings of these experiments demonstrate that the developed hierarchal framework is capable of carrying activity recognition as well as being able to carry out intention analysis, e.g. predicting what activity they are most likely to carry out next

A semantics-based approach to sensor data segmentation in real-time Activity Recognition

Department of Information Engineering, Dalian University, China The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Activity Recognition (AR) is key in context-aware assistive living systems. One challenge in AR is the segmentation of observed sensor events when interleaved or concurrent activities of daily living (ADLs) are performed. Several studies have proposed methods of separating and organising sensor observations and recognise generic ADLs performed in a simple or composite manner. However, little has been explored in semantically distinguishing individual sensor events directly and passing it to the relevant ongoing/new atomic activities. This paper proposes Semiotic theory inspired ontological model, capturing generic knowledge and inhabitant-specific preferences for conducting ADLs to support the segmentation process. A multithreaded decision algorithm and system prototype were developed and evaluated against 30 use case scenarios where each event was simulated at 10sec interval on a machine with i7 2.60GHz CPU, 2 cores and 8GB RAM. The result suggests that all sensor events were adequately segmented with 100% accuracy for single ADL scenarios and minor improvement of 97.8% accuracy for composite ADL scenario. However, the performance has suffered to segment each event with the average classification time of 3971ms and 62183ms for single and composite ADL scenarios, respectively

Include 2011 : The role of inclusive design in making social innovation happen.

Include is the biennial conference held at the RCA and hosted by the Helen Hamlyn Centre for Design. The event is directed by Jo-Anne Bichard and attracts an international delegation