Smart Home Systems

The monitoring of elderly people in their home by location sensors provides the recording of their walk trajectories within their own flat giving some insights into their daily routine. These space/time data are then used to establish an individual specific profile concerning the time serie which corresponds to the successive locations of the person. The detection of large deviations from the profile may be used to trigger alarms at the level of the family (to make the family member more careful) or at the level of the Hospital at Home service (for an emergency). The present article proposes two different mathematical approaches for location modelling. The first calculations point out the simplicity of the Markovian model. This feature makes it more cost-effective in terms of calculation and response time and thus more interesting for implementation. Further works are necessary to explore the Pólya's urns model deeper. If the choice is available, however, priority will be given to the other. The Pólya's urns model will only be used if the Markovian one is no longer available. For the validation of each model, a procedure based on the calculation of the mean of remaining duration in task is proposed. At this point, it is important to mention that this parameter differs from the mean of the time passed the task considered (it should be by a factor of 0.5). For deciding between the two methods proposed, further works should be performed using the statistics equal to the empirical mean E of a task remaining duration and their confidence interval. The n-grams model used to predict the next location is also in favour of a first order Markov chain approach. Other models need to be improved more deeply. Indeed, first results offer promising results with n=2 and a degradation of performances with n increasing up to 10. For the confirmation of this trend, experiment should be applied for n=60, watching for the whole last minute in order to predict the 60th second. For the time being, the best model offers up to 98.03% of good prediction location, considering only the last second of location i.e. using the first order Markov chain model, but distinguishing days of week. Indeed, taking day of the week into account offers better performance (97.36% by working directly on the whole corpus). Performance seems to differ for each day of week. This factor of variability should be taken into account when designing a system using a location model. To go further, future experiments may be done for other refinements among which providing the possibility of distinguishing the time in the day to take into account the circadian rhythm (Virone et al. 2002, Cerny et al. 2009). Each day of the month could affect activity: some days, as 1st of the month for example, are particular. The comparison between each month may show seasonal effects, and so on (Fouquet et al. 2009 b). It could then be interesting to develop a new model with a continuum approach considering estimations (interpolation) between data observed. Our procedure may be extended later to more complex task (named composite task in Das et al. 2008) recognition by means of a multi-sensors network. The multidimensional data collection obtained with such a system will be fused and mined using the data processing techniques above introduced. Concerning the detection of progressive stereotyped behaviour for the early diagnosis of neuro-degenerative diseases, actimetric data may be completed by physiological ones to quantify the state of anxiety of the subject particularly at nightfall or a possible shift in his/her activity circadian rhythm which are behaviour commonly observed in the first stages of these pathologies (Monk 2005, Hofman and Swaab 2006) There are several smart home devices with their control units available in practice. The appliances are connected to the concerned bus and the switching is monitored with their software. For instance X10 devices (website 2009) which are used in communication protocols are employed in home automation applications. This needs the x10 transceivers to be installed at desired nodes. This project does not rely on any commercial smart home devices and the control is effected by simple means through relays which are commonly known to everybody. In case of arising any fault performing the fault diagnosis is quite easy. When smart home devices of different makes are used there is a quite possibility of mismatch to occur when control actions are effected from common software. When internet is involved in effecting control actions to devices of various makes the security threats are more vulnerable. In the proposed relay based control the security threats could be easily handled. Based on the information provided here, one can build the system incorporating the facilities required for his flat in the building. We don't normally apply the controlled switching for all electrical appliances in the flat. When the resident is away he would like only a very few devices to be operated as to serve certain needs arising at any moment. Therefore, we considered only selected devices to be operated under controlled action besides the manual switching facilities available for the same. Keeping the requirements of modern home automation system accommodating most desirable features the home automation system has been designed and reported. The selected appliances to control by remotely through phone and internet and also by local means are taken as example only. Although, it satisfies most requirements of consumers, if in case the user wants to control few additional devices it can easily accommodated. Timers associated with the monitor program installed in the PC can be programmed to switch on cyclically the selected appliances for security concerns. For instance, the periodical illumination of lamps by a timer would simulate the condition of resident staying in the flat. The solar panels and windmills concede a part of electrical energy requirements of the flat. Since the panel and windmill are kept on the roof their clear exposure to sun and wind would extract maximum energy and this is aided by direction alignment act. The extraction of energy from these resources becomes important nowadays due to the shortage of main resources such as fossil fuels. The security measures are efficient with the authorized visitor entry, control of appliances and intruder detection. The presentation is made in a simple way that one can easily follow and develop and the technology is open for anyone to accommodate more features by adjusting the hardware and software This chapter presented some of the state of the art technologies and associated applications in the field of smart homes. It gave an overview of the major wireless communication technologies that form a fundamental part of the infrastructure of modern smart homes. Some of those technologies are integrated within sensing and networking devices such as Zigbee, Bluetooth, RFID, and WiFi. Other wireless technologies, such as the GSM, are more of a wider format that can form large network and yet can integrate with the other ones dedicated for short range. The paper also briefly discussed some of the modern sensors that can be used in smart homes. Many of them are of the embedded ubiquitous type that is equipped with wireless communication capabilities and can connect to other devices. The application areas discussed include appliances monitoring and control, safety and security, telehealth care, energy saving, environmental control, and information access Preface......Page 5 P. LALANDA, J. BOURCIER, J. BARDIN & S. CHOLLET......Page 11 Mahmoud A. Al-Qutayri and Jeedella S. Jeedella......Page 27 K. Balasubramanian and A. Cellatoglu......Page 53 Shang-Yuan CHEN and Shu-Fen CHANG......Page 73 Ana Fernández Vilas, Rebeca P. Díaz Redondo, José J. Pazos Arias, Manuel Ramos Cabrer, Alberto Gil Solla and Jorge García Duque......Page 95 Blanson Henkemans, Olivier A., Alpay, Laurence, L., Dumay Adrie, C.M.......Page 115 Yannick Fouquet, Céline Franco, Jacques Demongeot, Christophe Villemazet and Nicolas Vuillerme......Page 131 Machiko R. Tomita, Ph.D., Linda S. Russ, Ph.D., OTR/L, Ramalingam Sridhar, Ph.D., Bruce J. Naughton, MD.......Page 149 Beizhong Chen, Ibrahim Kamel and Ivan Marsic......Page 169 Jumphon Lertlakkhanakul and Jinwon Choi......Page 187