For improving e-health services, we propose a context-aware framework to monitor the activities of daily living of dependent persons. We define a strategy for generating long-term realistic scenarios and a framework containing an adaptive monitoring algorithm based on three approaches for optimizing resource usage. The used approaches provide a deep knowledge about the person's context by considering: the person's profile, the activities and the relationships between activities. We evaluate the performances of our framework and show its adaptability and significant reduction in network, energy and processing usage over a traditional monitoring implementation.
 Recently, how to support and care elderly people is serious problem. Modern approach is trying to support to keep health (body and mental) seniors living alone, but it is hard to keep a health by oneself. Our aim is monitoring a user's life for providing suitable services to keep his her health. To providing suitable service, life log is very useful information. However, traditional monitoring system is only focusing elementary motion, walking, sleeping, and so on. We deal with recognizing advanced motions by using transportation data from an area to other areas in a house. These advanced motion logs support not only elderly but also care stuff or family who plans supports. In daily life, our motion is very variety. Therefore we focused on Basic Activities of Daily Living (ADL) which categories normally doing in daily life such as feeding, bathing, homemaking, and so on. In this research, we pick up 11 motion from ADL for recognition. Recent developments and technological advancements in wireless communication, MicroElectroMechanical Systems (MEMS) technology and integrated circuits has enabled low-power, intelligent, miniaturized, invasive non-invasive micro and nano-technology sensor nodes strategically placed in or around the human body to be used in various applications, such as personal health monitoring. This exciting new area of research is called Wireless Body Area Networks (WBANs) and leverages the emerging IEEE 802.15.6 and IEEE 802.15.4j standards, specifically standardized for medical WBANs. The aim of WBANs is to simplify and improve speed, accuracy, and reliability of communication of sensors actuators within, on, and in the immediate proximity of a human body. The vast scope of challenges associated with WBANs has led to numerous publications. In this paper, we survey the current state-of-art of WBANs based on the latest standards and publications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in WBANs. By 2050, about one third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international activities of daily living (ADL) or the French Autonomie Gerontologie Groupes Iso-Ressources (AGGIR) scales, by automatically classifying the different ADL performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, infrared presence sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors are then used to classify each temporal frame into one of the ADL that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing undressing). This is done using support vector machines. We performed a 1-h experimentation with 13 young and healthy subjects to determine the models of the different activities, and then we tested the classification algorithm (cross validation) with real data. In this work, we propose a framework for automatic dependency evaluation that can notify the changes of the elderly dependency, hence providing persons with required services in time. In order to validate our approach, we target the French dependent population. Especially, we focus our proposal on the AGGIR dependency model used in the French medical field. Our objective is to provide a flexible architecture and an extensible model linked to opened data that can refer to a wide variety of services (hardware sensors and software; simple or composed services). Because the broad application prospects of Internet of Things (IoT) and the progress of short-range wireless technology, the Internet and the intelligent terminals have an explosive growth, IoT is expected to become the next trillion-dollar information industry. In wired wireless technologies, the machine to machine (M2M) communications have higher request to benefit people, include e-health, family energy management and entertainment. This paper represents technical indexes, application range, radio coexistence mechanism and suitable network standards of key radio technologies. In the application layer, this paper argues the cloud computing technology as the center to help process a large amount of data of the sensing layer, for providing efficient services to clients and effectively reducing the cost of smart home with the help of data processing. Base on the analysis of traditional long-range medical healthcare system, the human body healthcare system used in the family is introduced in this paper. According to the requirements of family health monitor system, the paper compares several kinds of wireless communication technology which uses in the family, The system is presented to combine the short-range wireless communication technology Zigbee and the long-range wireless communication technology GPRS to monitor human health. This system can not only meet the main requirement of real-time monitoring, but also avoid the costly nursing expenses. The ever growing percentage of elderly people within modern societies poses welfare systems under relevant stress. In fact, partial and progressive loss of motor, sensorial, and or cognitive skills renders elders unable to live autonomously, eventually leading to their hospitalization. This results in both relevant emotional and economic costs. Ubiquitous computing technologies can offer interesting opportunities for in-house safety and autonomy. However, existing systems partially address in-house safety requirements and typically focus on only elder monitoring and emergency detection. The paper presents ANGELAH, a middleware-level solution integrating both "elder monitoring and emergency detection" solutions and networking solutions. ANGELAH has two main features: i) it enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and ii) provides a solid framework for creating and managing rescue teams composed of individuals willing to promptly assist elders in case of emergency situations. A prototype of ANGELAH, designed for a case study for helping elders with vision impairments, is developed and interesting results are obtained from both computer simulations and a real-network testbed.