Privacy Mining from IoT-based Smart Homes

Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoT-based applications is smart homes for healthcare, especially for elders. IoT-based smart homes enable elders' health to be properly monitored and taken care of. However, elders' privacy might be disclosed from smart homes due to non-fully protected network communication or other reasons. To demonstrate how serious this issue is, we introduce in this paper a Privacy Mining Approach (PMA) to mine privacy from smart homes by conducting a series of deductions and analyses on sensor datasets generated by smart homes. The experimental results demonstrate that PMA is able to deduce a global sensor topology for a smart home and disclose elders' privacy in terms of their house layouts.Comment: This paper, which has 11 pages and 7 figures, has been accepted BWCCA 2018 on 13th August 201

Mining top-k regular episodes from sensor streams

International audienceThe monitoring of human activities plays an important role in health-care applications and for the data mining community. Existing approaches work on activities recognition occurring in sensor data streams. However, regular behaviors have not been studied. Thus, we here introduce a new approach to discover top-k most regular episodes from sensors streams, TKRES. The top-k approach allows us to control the size of the output, thus preventing overwhelming result analysis for the supervisor. TKRES is based on the use of a simple top-k list and a k-tree structure for maintaining the top-k episodes and their occurrence information. We also investigate and report the performances of TKRES on two real-life smart home datasets

Acceptance of ambient assisted living (AAL) technologies among older Australians : a review of barriers in user experience

One of the great challenges facing Australian society is that of an ageing population. Amongst the issues involved in this drastic demographic change, the most significant aspect is the demand for older Australians to live independently at home. The development of Ambient Assisted Living (AAL) technologies aims to address this issue. The advancement of AAL applications have been done to support the users with their daily-life activities and health concerns by providing increased mobility, security, safety in emergencies, health-monitoring, improved lifestyle, and fall-detection through the use of sensors. However, the optimum uptake of these technologies among the end-users (the elderly Australians) still remains a big concern. Thus, there is an elevated need to understand the needs and preferences of the seniors in order to improve the acceptance of AAL applications. The aim of this study is to investigate the barriers and perceptions in the use of AAL applications amongst older Australians. Focus groups and quantitative surveys have been conducted to provide a detailed analysis of these impediments. The results show that there are different factors that restrict the use of these technologies along with the fact that elderly people have certain preferences when using them. An understanding of these factors has been gained and suggestions have been made to increase the acceptance of AAL devices. This work gives useful insights towards the design of AAL solutions according to user needs

Inteligencia computacional en sistemas de teleasistencia en domicilios

156 p.Este trabajo presenta un sistema inteligente de predicción del comportamiento de cara a prevenir situaciones de riesgo en el hogar, y que está orientado principalmente orientado a personas mayores. El sistema presenta una modelo híbrido basado en Reglas Heurísticas introducidas por expertos en el dominio, y un Módulo de Detección Automática de Patrones, conformado por un sistema combinado de clasificadores basados en técnicas de Inteligencia Artificial. El sistema ha sido probado en entornos reales y productivos en diversos domicilios. Para lograrlo, el sistema utiliza por un lado la información de sensores simples en el hogar, por otro el conocimiento de sus actividades físicas recopiladas por aplicaciones móviles y por último la información de salud personalizada basada en informes clínicos codificados en el sistema. Actualmente, se está implantando en condiciones reales, con una precisión superior al 81%

Inteligencia computacional en sistemas de teleasistencia en domicilios

156 p.Este trabajo presenta un sistema inteligente de predicción del comportamiento de cara a prevenir situaciones de riesgo en el hogar, y que está orientado principalmente orientado a personas mayores. El sistema presenta una modelo híbrido basado en Reglas Heurísticas introducidas por expertos en el dominio, y un Módulo de Detección Automática de Patrones, conformado por un sistema combinado de clasificadores basados en técnicas de Inteligencia Artificial. El sistema ha sido probado en entornos reales y productivos en diversos domicilios. Para lograrlo, el sistema utiliza por un lado la información de sensores simples en el hogar, por otro el conocimiento de sus actividades físicas recopiladas por aplicaciones móviles y por último la información de salud personalizada basada en informes clínicos codificados en el sistema. Actualmente, se está implantando en condiciones reales, con una precisión superior al 81%