This paper presents a telemetry system used in a combined trilateration method for the precise indoor localization of the elderly who need health monitoring. The system is based on the association of two wireless technologies: ultrasonic and 802.15.4. The use of the 802.15.4 RF signal gives the reference starting time of the ultrasonic emission (time difference of arrival method). A time of flight measurement of the ultrasonic pulses provides the distances between the mobile node and three anchor points. These distance measurements are then used to locate the mobile node using the trilateration method with an accuracy of a few centimetres. The originality of our work lies in embedding the mobile node in clothes. The system is embedded in clothes in two ways: on a shoe in order to form a “smart” shoe and in a hat in order to form a “smart” hat. Both accessories allow movements, gait speed and distance covered to be monitored for health applications. Experiments in a test room are presented to show the effectiveness of our system.
References
[1]
Chan, M.; Estève, D.; Escriba, C.; Campo, E. A review of smart homes—Present state and future challenges. Comput. Meth. Prog. Biomed. 2008, 91, 55–81.
[2]
Gentry, T. Smart homes for people with neurological disability: State of the art. NeuroRehabilitation 2009, 25, 209–217.
[3]
Fleury, A.; Vacher, M.; Noury, N. SVM-based multimodal classification of activities of daily living in health smart homes: Sensors, algorithms, and first experimental results. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 274–283.
[4]
Cerny, M. Movement Activity Monitoring of Elderly People. Proceedings of the Second International Conference on Computer Engineering and Applications, (ICCEA' 10), Bali Island, Indonesia, 19–21 March 2010; pp. 454–455.
[5]
Krumm, J.; Harris, S.; Meyers, B.; Brumitt, B.; Hale, M.; Shafer, S. Multi-Camera Multi-Person Tracking for EasyLiving. Proceedings of the Third IEEE International Workshop on Visual Surveillance, Dublin, Ireland, 1 July 2000; pp. 3–10.
[6]
Riedel, D.E.; Venkatesh, S.; Liu, W. Spatial Activity Recognition in a Smart Home Environment Using a Chemotactic Model. Proceedings of the Second Intelligent Sensors, Sensor Networks and Information Processing Conference, Melbourne, Australia, 5–8 December 2005; pp. 301–306.
[7]
Barnes, N.M.; Edwards, N.H.; Rose, D.A.D.; Garner, P. Lifestyle monitoring-technology for supported independence. Comp. Contr. Eng. J. 1998, 9, 169–174.
[8]
Virone, G.; Noury, N.; Demongeot, J. A system for automatic measurement of circadian activity deviations in telemedicine. IEEE Trans. Biomed. Eng. 2002, 49, 1463–1469.
[9]
Chan, M.; Campo, E.; Estève, D. PROSAFE, A Multisensory Remote Monitoring System for the Elderly or the Handicapped. Proceedings of the International Conference on Smart Homes and Health Telematics, (ICOST' 03), Montreal, PQ, Canada, 20–22 June 2011; pp. 89–95.
[10]
Fulk, G.D.; Lopez-Meyer, P.; Sazonov, E.S. Characterizing Walking Activity in People with Stroke. Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS' 11), Boston, MA, USA, 30 August–3 September 2011; pp. 5211–5214.
[11]
Martin Schepers, H.; van Asseldonk, E.H.; Baten, C.T.; Veltink, P.H. Ambulatory estimation of foot placement during walking using inertial sensors. J. Biomech. 2010, 43, 3138–3143.
[12]
Helal, S.; Mann, W.; El-Zabadani, H.; King, J.; Kaddoura, Y.; Jansen, E. The gator tech smart house: A programmable pervasive space. Computer 2005, 38, 50–60.
[13]
Kidd, C.D.; Orr, R.; Abowd, G.D.; Atkeson, C.G.; Essa, I.A.; MacIntyre, B.; Mynatt, E.; Starner, T.E.; Newstetter, W. The Aware Home: A Living Laboratory for Ubiquitous Computing Research. Proceedings of the Second International Workshop on Cooperative Buildings, (CoBuild' 99), Integrating Information, Organization, and Architecture, Pittsburgh, PA, USA, 1–2 October 1999; pp. 191–198.
[14]
Isoda, Y.; Kurakake, S.; Nakano, H. Ubiquitous Sensors Based Human Behavior Modeling and Recognition Using a Spatio-Temporal Representation of User States. Proceedings of the 18th International Conference on Advanced Information Networking and Applications, (AINA' 04), Fukuoka, Japan, 29–31 March 2004; pp. 512–517.
[15]
Charlon, Y.; Bourennane, W.; Bettahar, F.; Campo, E. Activity monitoring system for elderly in a context of smart home. IRBM 2013, 34, 60–63.
[16]
Fourty, N.; Charlon, Y.; Campo, E. Embedded wireless system for pedestrian localization in indoor environments. J. Sens. Transd. 2012, 14–2, 211–227.
[17]
Fried, L.P.; Tangen, C.M.; Walston, J.; Newman, A.B.; Hirsch, C.; Gottdiener, J.; Seeman, T.; Tracy, R.; Kop, W.J.; Burke, G.; et al. Frailty in older adults: Evidence for a phenotype. J. Gerontol. A Biol. Sci. Med. Sci. 2001, 56, 146–157.
[18]
Deak, G.; Curran, K.; Condell, J. A survey of active and passive indoor localisation systems. Comput. Commun. 2012, 35–16, 1939–1954.
[19]
Filliata, D.; Meyerb, J. Map-based navigation in mobile robots: A review of localization strategies. Cogni. Syst. Res. 2003, 4, 243–282.
[20]
Sanchez, A.; Elvira, S.; de Castro, A.; Glez-de-Rivera, G.; Ribalda, R.; Garrido, J. Low Cost Indoor Ultrasonic Positioning Implemented in FPGA. Proceedings of the 35th Annual Conference of IEEE Industrial Electronics, Porto, Portugal, 3–5 November 2009; pp. 2709–2714.
[21]
Khoury, H.M.; Kamat, V.R. Evaluation of position tracking technologies for user localization in indoor construction environments. Automat. Const. 2009, 18–4, 444–457.
[22]
Kuo, W.H.; Chen, Y.S.; Jen, G.T.; Lu, T.W. An Intelligent Positioning Approach: RSSI-Based Indoor and Outdoor Localization Scheme in Zigbee Networks. Proceedings of the International Conference on Machine Learning and Cybernetics, (ICMLC' 10), Qingdao, China, 11–14 July 2010; pp. 2754–2759.
[23]
Huang, C.N.; Chan, C.T. ZigBee-based indoor location system by k-nearest neighbor algorithm with weighted RSSI. Proc. Comp. Sci. 2011, 5, 58–65.
[24]
Tsuji, J.; Kawamura, H.; Suzuki, K.; Ikeda, T.; Sashima, A.; Kurumatani, K. ZigBee Based Indoor Localization with Particle Filter Estimation. Proceedings of the IEEE International Conference on Systems Man and Cybernetics, (SMC' 10), Istanbul, Turkey, 10–13 October 2010; pp. 1115–1120.
[25]
Ciurana, M.; Cugno, S.; Barcel-Arroyo, F. WLAN Indoor Positioning Based on TOA with Two Reference Points. Proceedings of the 4th Workshop on Positioning, Navigation and Communication, (WPNC' 07), Hannover, Germany, 22–22 March 2007; pp. 23–28.
[26]
Mautz, R. The Challenges of Indoor Environments and Specification on Some Alternative Positioning Systems. Proceedings of the 6th Workshop on Positioning, Navigation and Communication, (WPNC' 09), Hannover, Germany, 19 March 2009; pp. 29–36.
[27]
Priyantha, N.B.; Chakraborty, A.; Balakrishnan, H. The Cricket Location-Support System. Proceedings of the 6th Annual ACM International Conference on Mobile Computing and Networking, (MobiCom' 00), Boston, MA, USA, 6–11 August 2000; pp. 32–43.
[28]
Hazas, M.; Hopper, A. Broadband ultrasonic location system for improved indoor positioning. IEEE Trans. Mob. Comput. 2006, 5, 536–547.
[29]
Harter, A.; Hopper, A.; Steggles, P.; Ward, A.; Webster, P. The Anatomy of a Context-Aware Application. Proceedings of the 5th Annual ACM/IEEE International conference on Mobile computing and networking, (MobiCom' 99), Seattle, WA, USA, 17–19 August 1999; pp. 59–68.
[30]
Man, L.A.N. Committee, S. IEEE Standard for information technology- Telecommunications and information exchange between systems- Local and metropolitan area networks. Specific requirements Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) specifications. IEEE Standard 2006, 137, 1–305.
[31]
Daintree Networks. Available online: http://www.daintree.net/sna/sna.php (accessed on 12 June 2013).
[32]
Freescale. Available online: http://www.freescale.com/webapp/sps/site/homepage.jsp?code/=CW_HOME (accessed on 12 June 2013).
[33]
Casas, R.; Marco, A.; Guerrero, J.J.; Falco, J.L. Robust estimator for non-line-of-sight error mitigation in indoor localization. J. Appl. Sig. Process. 2006, 2006, 1–8.