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

Semantic knowledge base in support of activity recognition in smart home environments

Activity recognition plays a major role in smart home technologies in providing services to users. One of the approaches to identify ac-tivity is through the use of knowledge-driven reasoning. This paper presents a framework of semantic activity recognition, which is used to support smart home systems to identify users’ activities based on the existing context. The framework consists of two main compo-nents: a semantic knowledge base and an activity recognition module. The knowledge base is represented using ontology and it is used to provide a semantic understanding of the environment in order to classify users’ patterns of activities. Experimental results show that the proposed approach can support the classification process and accurately infer users’ activities with the accuracy of 90.9%

Semantic knowledge base in support of activity recognition in smart home environments

Activity recognition plays a major role in smart home technologies in providing services to users. One of the approaches to identify ac-tivity is through the use of knowledge-driven reasoning. This paper presents a framework of semantic activity recognition, which is used to support smart home systems to identify users’ activities based on the existing context. The framework consists of two main compo-nents: a semantic knowledge base and an activity recognition module. The knowledge base is represented using ontology and it is used to provide a semantic understanding of the environment in order to classify users’ patterns of activities. Experimental results show that the proposed approach can support the classification process and accurately infer users’ activities with the accuracy of 90.9%

A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model

Assessment of ambient assisted living systems for patients with mild cognitive impairment

According to the World Health Organization, about 50 million people worldwide suffer from dementia. Ten million new cases added every year. Mild Cognitive Impairment (MCI) affects more than 15% of the population aged 65. Technological solutions, such as smart home technology with ubiquitous computing devices, 24/7 telemedical observation and support can alleviate the growing problem and lower pressure on the healthcare system. This approach is also preferable for homecare patients in distant and rural areas. MCI patients are mostly home-based. Ambient Assisted Living (AAL) systems provide tools for automatic registration of vital signs and other medically and socially important information. AAL system for MCI patients is a logical answer to the problem. At the same time, many of the proposed AAL systems are proprietary, technically complicated and have a high price tag for implementation and service. Also, some proposed technical solutions not entirely reflect the opinion of healthcare stakeholders. The current study was proposed as a way to bridge the possible differences in the positions. An online anonymous questionnaire for healthcare professionals was created to prove or disprove the number of interconnected hypotheses about the necessity and feasibility of AAL system for MCI patients. The main focus was made on the hypotheses: "There is necessity of AAL systems for the healthcare" and "AAL systems are capable of providing assistance for patients with Mild Cognitive Impairment". The questionnaire was presented to more than three hundred potential respondents. Around a hundred and twenty agreed to fill it, and sixty completed the whole questionnaire. Results were analyzed to produce some directions guideline for future technical applications of AAL systems for MCI patients and future research. Descriptive statistics show support for the implementation of general AAL and variants for MCI patients. Comparative analysis of ordinal data for specific groups of respondents is done with help of non-parametric tests. Mann–Whitney–Wilcoxon test and Kruskal-Wallis test are applied. Table questions results are analyzed with chisquare for frequency tables. Group analysis demonstrated relative positive uniformity in of responses in the support of AAL of MCI patients.Segundo a Organização Mundial da Saúde, cerca de 50 milhões de pessoas em todo o mundo sofrem de demência. Dez milhões de novos casos adicionados a cada ano. O comprometimento cognitivo leve (MCI) afeta mais de 15% da população com 65 anos. Soluções tecnológicas, como tecnologia de casa inteligente com dispositivos de computação onipresentes, observação e suporte telemédico 24 horas por dia, 7 dias por semana, podem aliviar o problema crescente e diminuir a pressão sobre o sistema de saúde. Essa abordagem também é preferível para pacientes de cuidados domiciliares em áreas distantes e rurais. Os pacientes com CCL são, em sua maioria, domiciliares. Os sistemas Ambient Assisted Living (AAL) fornecem ferramentas para registro automático de sinais vitais e outras informações médicas e socialmente importantes. O sistema AAL para pacientes com MCI é uma resposta lógica para o problema. Ao mesmo tempo, muitos dos sistemas AAL propostos são proprietários, tecnicamente complicados e têm um alto preço para implementação e serviço. Além disso, algumas soluções técnicas propostas não refletem inteiramente a opinião das partes interessadas na área da saúde. O presente estudo foi proposto como forma de colmatar as possíveis diferenças nas posições. Um questionário anônimo online para profissionais de saúde foi criado para comprovar ou refutar o número de hipóteses interligadas sobre a necessidade e viabilidade do sistema AAL para pacientes com CCL. O foco principal foi feito nas hipóteses: "Há necessidade de sistemas de AAL para a saúde" e "Os sistemas de AAL são capazes de prestar assistência a pacientes com Comprometimento Cognitivo Leve". O questionário foi apresentado a mais de trezentos respondentes potenciais. Cerca de cento e vinte concordaram em preenchê-lo e sessenta preencheram todo o questionário. Os resultados foram analisados para produzir algumas diretrizes para futuras aplicações técnicas de sistemas AAL para pacientes com MCI e pesquisas futuras. Estatísticas descritivas mostram suporte para a implementação de AAL geral e variantes para pacientes com CCL. A análise comparativa de dados ordinais para grupos específicos de respondentes é feita com a ajuda de testes não paramétricos. Aplicam-se os testes de Mann-Whitney-Wilcoxon e Kruskal-Wallis. Os resultados das questões da tabela são analisados com qui-quadrado para tabelas de frequência. A análise do grupo demonstrou relativa uniformidade positiva nas respostas no suporte de AAL de pacientes com CCL.Selon l'Organisation mondiale de la santé, environ 50 millions de personnes dans le monde souffrent de démence. Dix millions de nouveaux cas ajoutés chaque année. Les troubles cognitifs légers (MCI) touchent plus de 15 % de la population âgée de 65 ans. Les solutions technologiques, telles que la technologie de la maison intelligente avec des appareils informatiques omniprésents, l'observation et le soutien télémédicaux 24 heures sur 24, 7 jours sur 7, peuvent atténuer le problème croissant et réduire la pression sur le système de santé. Cette approche est également préférable pour les patients en soins à domicile dans les régions éloignées et rurales. Les patients MCI sont pour la plupart à domicile. Les systèmes Ambient Assisted Living (AAL) fournissent des outils pour l'enregistrement automatique des signes vitaux et d'autres informations importantes sur le plan médical et social. Le système AAL pour les patients MCI est une réponse logique au problème. Dans le même temps, bon nombre des systèmes AAL proposés sont propriétaires, techniquement compliqués et ont un prix élevé pour la mise en oeuvre et le service. De plus, certaines solutions techniques proposées ne reflètent pas entièrement l'opinion des acteurs de santé. L'étude actuelle a été proposée comme un moyen de combler les différences possible dans les positions. Un questionnaire anonyme en ligne destiné aux professionnels de la santé a été créé pour prouver ou réfuter le nombre d'hypothèses interconnectées sur la nécessité et la faisabilité du système AAL pour les patients MCI. L'accent a été mis principalement sur les hypothèses: "Il existe une nécessité de systèmes AAL pour les soins de santé" et "Les systèmes AAL sont capables de fournir une assistance aux patients atteints de troubles cognitifs légers". Le questionnaire a été présenté à plus de trois cents répondants potentiels. Environ cent vingt ont accepté de le remplir, et soixante ont rempli tout le questionnaire. Les résultats ont été analysés pour produire des lignes directrices pour les futures applications techniques des systèmes AAL pour les patients MCI et l'avenir de la recherche. Les statistiques descriptives montrent un soutien à la mise en oeuvre de l'AAL général et des variantes pour les patients MCI. L'analyse comparative des données ordinales pour des groupes spécifiques de répondants est effectuée à l'aide de tests non paramétriques. Le test de Mann-Whitney-Wilcoxon et le test de Kruskal-Wallis sont appliqués. Les résultats des questions de tableau sont analysés avec le chi carré pour les tableaux de fréquence. L'analyse de groupe a démontré une uniformité positive relative dans les réponses à l'appui de l'AAL des patients MCI