Smart Sensory Furniture based on WSN for Ambient Assisted Living
Abstract:
Ubiquitous computing has been defined as ‘machines that fit the human environment instead of forcing humans to enter theirs’. An example of this type of approach is “Smart Sensory Furniture” (SSF) project. SSF is an AAL (Ambient Assisted Living) system that allows inferring a potential dangerous action of an elderly person living alone at home. This inference is obtained by a specific sensory layer with sensor nodes fixed into furniture and a reasoning layer embedded in a PC that learns from the users’ behavioral patterns and advices when the system detects unusual patterns. This paper aims to explain the SSF sensory layer which is a distributed signal processing system in a network of sensing objects massively distributed, physically-coupled, wirelessly networked, and energy limited. A complete set of experimental test has been carried out. The results show the level of accuracy for each type of sensors and potential use. Finally, the power consumption was experimentally measured and the results show the low maintenance requirements of this solution. The complete system design is described and discussed including the node mesh details, as well as the type of sensors and actuators and other aspects such as integration issues and solutions.
Existing System:
Elderly population would like to maintain their independence, and live at their own homes for as long as possible. According to the European Commission Report [11], the percentage of elderly people living alone will rise in future years in the entire world, and the number of older people receiving care in institutions would almost triple. Those receiving formal care at home would more than double, and those receiving informal or no care would almost double by 2060. Either at homes or in any kind of caregiving institution furniture is always present and used. That is the main reason why our proposed system relies on furniture as a key integration element to achieve a really pervasive presence of devices in typical older people’s environments. By embedding sensors, ubiquitous computation, and wireless communication into everyday objects, future computing applications will be able to anticipate human needs. However, it is currently difficult to develop this type of ubiquitous computing in these everyday objects, because of the lack of devices that integrate both the required hardware and software. In that regard, we propose a type of small computers with wireless communication, sensors and actuators as the basis for low power smart objects.
Proposed System:
Our system has been designed considering the presence of a local AmI (Ambient Intelligent) station used to process event patterns `in situ’ and take decisions. This home station is provided with a Java-based intelligent software which is able to take decisions about different events. In short, it has Java application for monitoring the elderly [12] and IEEE-802.15.4 wireless connectivity provided by a USB base station for our prototype. The application can monitor the events to determine if an unusual situation has occurred. If normal behaviour is detected by the latter devices, then the event might just be recorded as an incident of interest, or the user might be prompted to ask if they are all right. On the other hand, if a non common behaviour is detected, then the AmI station will immediately query the user and send an emergency signal if there is no response within a certain (short) period of time. This layer stack form a global software architecture. The middle level software layer, model of user behavior, obtains the actual state of the attendee, detecting if the resident is in an emergency situation which must be solved. The deep reasoning layer is being developed to solve inconsistencies reached in the middle layer. The Open Context Platform layer captures raw data from sensorized furniture through sensor driver adapters, and transforms the data in an ontology-based format to represent context information.
Conclusion:
AmI systems require the use of sensors that seamlessly monitor the users and their environment in order to anticipate their needs and provide the necessary support and assistance in a non-invasive way. Sensing accuracy is essential to develop reliable systems, whereas transparency and invisibility greatly contribute to the sense of comfort and lack of intrusiveness. With the SSF project, we have taken the first steps toward the creation of smart environment platforms that deliver both, sensing accuracy and system transparency through the seamless integration of sensors in everyday objects like pieces of furniture. In this paper we have shown that sensors in upholstered furniture can measure certain variables beyond the reach of remote sensing, or at least they can provide better accuracy, since they can get in direct contact with or very close to the user. of activity/movement and moisture. At the same time, any kind of furniture can improve the information about the user context, and it is a great place to `hide’ the system hardware including sensors and communication nodes. The complete system design has been described including the node mesh details, as well as the type of sensors and actuators that have been used in SSF.
References:
[1] Santos, O. C., Uria-Rivas, R., Rodriguez-Sanchez, M. C., & Boticario, J. G. (2016). An open sensing and acting platform for context-aware affective support in ambient intelligent educational settings. IEEE Sensors Journal, 16(10), 3865-3874.
[2] Cook, D. J., Augusto, J. C., & Jakkula, V. R., (2009). Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing 5 (4), 277 – 298. Available: http://www.sciencedirect.com/science/article/pii/S157411920900025X
[3] Weiser, M., (1993). Ubiquitous computing. Computer 26, 71-72.
[4] Hansmann, U., Merk, L., Nicklous, M. S., & Stober, T. (2003). Pervasive Computing : The Mobile World. Springer. Available: http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/3540002189
[5] Greenfield, A., (2006). Everyware: The Dawning Age of Ubiquitous Computing, 1st Edition. New Riders Publishing. Available:http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0321384016
[6] Ghayvat, H., Liu, J., Mukhopadhyay, S. C., & Gui, X. (2015). Wellness Sensor Networks: A Proposal and Implementation for Smart Home for Assisted Living. IEEE Sensors Journal, 15(12), 7341-7348.
[7] Suryadevara, N. K., & Mukhopadhyay, S. C. (2012). Wireless sensor network based home monitoring system for wellness determination of elderly. IEEE Sensors Journal, 12(6), 1965-1972.Available:https://www.researchgate.net/profile/Sc_Mukhopadhyay/publication/260581551_Wireless_Sensor_Network_Based_Home_Monitoring_System_for_Wellness_Determination_of_Elderly/links/555a695908aeaaff3bfabe68.pdf
[8] Cortes, U., Urdiales, C., & Annicchiarico, R., (2007). Intelligent healthcare managing: An assistive technology approach. Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (Eds.), Computational and Ambient Intelligence. Vol. 4507 of Lecture Notes in Computer Science. Springer Berlin / Heidelberg, pp. 1045{1051, 10.1007/978-3-540-73007-1 126. Available: http://dx.doi.org/10.1007/978-3-540-73007-1_126
[9] Brewer, K., Ciolek, C., & Delaune, M. F. (2007). Falls in community dwelling older adults: Introduction to the problem. APTA Continuing Education Series, 38-46.
[10] Fernandez-Luque, F. J., Zapata, J., Ruiz, R., & Iborra, E., (2009). A wireless sensor network for assisted living at home of elderly people. Bioinspired Applications in Articial and Natural Computation. Vol. 5602 of Lecture Notes in Computer Science. pp. 65-74.