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SPHERE: A Sensor Platform for Healthcare in a Residential Environment

  • Przemyslaw WoznowskiEmail author
  • Alison Burrows
  • Tom Diethe
  • Xenofon Fafoutis
  • Jake Hall
  • Sion Hannuna
  • Massimo Camplani
  • Niall Twomey
  • Michal Kozlowski
  • Bo Tan
  • Ni Zhu
  • Atis Elsts
  • Antonis Vafeas
  • Adeline Paiement
  • Lili Tao
  • Majid Mirmehdi
  • Tilo Burghardt
  • Dima Damen
  • Peter Flach
  • Robert Piechocki
  • Ian Craddock
  • George Oikonomou

Abstract

It can be tempting to think about smart homes like one thinks about smart cities. On the surface, smart homes and smart cities comprise coherent systems enabled by similar sensing and interactive technologies. It can also be argued that both are broadly underpinned by shared goals of sustainable development, inclusive user engagement and improved service delivery. However, the home possesses unique characteristics that must be considered in order to develop effective smart home systems that are adopted in the real world [37].

Keywords

Ambient Assisted Living (AAL) Ambient intelligence (AmI) Internet of things (IoT) E-health Biomedical monitoring Sensors ADL recognition Smart home 

Notes

Acknowledgement

This work was performed under the SPHERE IRC, funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant EP/K031910/1.

References

  1. 1.
    Allen FR, Ambikairajah E, Lovell NH, Celler BG (2006) Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models. Physiol Meas 2006:935CrossRefGoogle Scholar
  2. 2.
    Atallah,L, Lo B, Ali R, King R, Yang G-Z (2009) Real-time activity classification using ambient and wearable sensors. IEEE Trans Inf Technol Biomed Publ IEEE Eng Med Biol Soc 13(6), 1031–1039Google Scholar
  3. 3.
    Berger JO (1993) Statistical decision theory and Bayesian analysis, 2nd edn. Springer-Verlag, New York, p 1993Google Scholar
  4. 4.
    Bernardo JM, Smith AFM (2008) Bayesian Theory. John Wiley & Sons, Hoboken, NJ, p 2008Google Scholar
  5. 5.
    Bian X, Abowd GD, Rehg JM (2005) Using sound source localization in a home environmentGoogle Scholar
  6. 6.
    Bishop CM (2013) Model-based machine learning. Phil Trans R Soc AGoogle Scholar
  7. 7.
    Bose A, Foh CH (2007) A practical path loss model for indoor WiFi positioning enhancement. Inf Commun Signal ProcessGoogle Scholar
  8. 8.
    Brugman H, Russel A (2004) Annotating multi-media/multi-modal resources with ELAN. In: Proceedings of the 4th International Conference on Language Resources and Language Evaluation (LREC 2004). Lisbon, 2004, pp 2065–2068Google Scholar
  9. 9.
    Brush AJ, Lee B, Mahajan R, Agarwal S, Saroiu S, Dixon C (2011) Home automation in the wild: challenges and opportunities. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2115–2124. ACMGoogle Scholar
  10. 10.
    Burrows A, Gooberman-Hill R, Coyle D (2015) Empirically derived user attributes for the design of home healthcare technologies. Pers Ubiquit Comput 19(8):1233–1245CrossRefGoogle Scholar
  11. 11.
    Coifman RR, Lafon S (2006) Diffusion maps. Appl Comput Harmon Anal 5–30, (Elsevier)Google Scholar
  12. 12.
    Diethe T, Twomey N, Flach P (2016) Active transfer learning for activity recognition. In: 24th European Symposium on Artificial Neural Networks. Bruges: ESANNGoogle Scholar
  13. 13.
    Diethe T, Twomey N, Flach P (2015) Bayesian modelling of the temporal aspects of smart home activity with circular statistics. Mach Learn Knowl Discov Databases, 279–294. Springer International Publishing, PortoGoogle Scholar
  14. 14.
    Exel R (2012) Receiver design for time‐based ranging with IEEE 802.11b signals. Int J Navig ObsGoogle Scholar
  15. 15.
    Fafoutis X, Janko B, Mellios E, Hilton G, Sherratt S, Piechocki R, Craddock I (2016) SPW-1: a low-maintenance wearable activity tracker for residential monitoring and healthcare applications. Int Conf Wearables Healthc (HealthWear). EAIGoogle Scholar
  16. 16.
    Fafoutis X, Mellios E, Twomey N, Diethe T, Hilton G, Piechocki R (2015) An RSSI-based wall prediction model for residential floor map construction. In: Proceedings of the 2nd IEEE World Forum on Internet of Things (WF-IoT). IEEEGoogle Scholar
  17. 17.
    Fafoutis X, Tsimbalo E, Mellios E, Hilton G, Piechocki R, Craddock I (2016) A residential maintenance-free long-term activity monitoring system for healthcare applications. EURASIP J Wirel Commun Netw 2016, 23Google Scholar
  18. 18.
    Flach PA, Kull M (2015) Precision-recall-gain curves: PR analysis done right. In: Proceedings of the Twenty-Ninth Annual Conference on Neural Information Processing Systems. NIPSGoogle Scholar
  19. 19.
    Fontana RJ, Gunderson SJ (2002) Ultra-wideband precision asset location system. Ultra Wideband Systems and Technologies, BaltimoreCrossRefGoogle Scholar
  20. 20.
    Günther A, Hoene C (2005) Measuring round trip times to determine the distance between WLAN nodesGoogle Scholar
  21. 21.
    Gelman A, Carlin JB, Stern HS, Rubin DB (2013) Bayesian data analysis, 3rd edn. Chapman and Hall, LondonzbMATHGoogle Scholar
  22. 22.
    Gerber S, Tasdizen T, Whitaker R (2007). Robust non-linear dimensionality reduction using successive 1-dimensional Laplacian eigenmaps. In: Proceedings 24th International Conference on Machine learning, pp 281–288. ACMGoogle Scholar
  23. 23.
    Harle R (2013) A survey of indoor inertial positioning systems for pedestrians. IEEE Commun Surv Tutor 15(3):1281–1293CrossRefGoogle Scholar
  24. 24.
    José H-O et al, Reframing in context: a methodology for model reuse in machine learning. AICOM, (in press)Google Scholar
  25. 25.
    Hernández-Orallo José, Flach Peter, Ferri Cèsar (2012) A unified view of performance metrics: translating threshold choice into expected classification loss. J Mach Learn Res 13(1):2813–2869MathSciNetzbMATHGoogle Scholar
  26. 26.
    Hoque E, Stankovic J (2012) AALO: Activity recognition in smart homes using Active Learning in the presence of Overlapped activities. In: Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare. IEEE, pp 139–146Google Scholar
  27. 27.
    Hui SY (2013) Planar wireless charging technology for portable electronic products and Qi. Proc IEEE 101(6):1290–1301CrossRefGoogle Scholar
  28. 28.
    Kim E, Helal S, Cook D (2010) Human activity recognition and pattern discovery. Pervasive Comput. 48–53Google Scholar
  29. 29.
    Kipp M (2012) Annotation facilities for the reliable analysis of human motion. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC). Istanbul, pp 4103–4107Google Scholar
  30. 30.
    Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT Press, Cambridge, MassachusettszbMATHGoogle Scholar
  31. 31.
    Lao L (2006) Location-based activity recognition. University of WashingtonGoogle Scholar
  32. 32.
    Logan B, Healey J, Philipose M, Tapia EM, Intille S (2007) A long-term evaluation of sensing modalities for activity recognition. In: Proceedings of the 9th International Conference on Ubiquitous Computing (UbiComp’07). Berlin: Springer-Verlag, pp 483–500Google Scholar
  33. 33.
    Longstaff B, Reddy S, Estrin D (2010) Improving activity classification for health applications on mobile devices using active and semi-supervised learning. In: 2010 4th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), NO PERMISSIONS. IEEE, pp 1–7Google Scholar
  34. 34.
    Ciurana M, Barcelo‐Arroyo F, Izquierdo F (2007) A ranging system with IEEE 802.11 data frames. In: IEEE Radio and Wireless Symposium. Long BeachGoogle Scholar
  35. 35.
    Maurer U, Smailagic A, Siewiorek DP, Deisher M (2006) Activity recognition and monitoring using multiple sensors on different body positions. Wearable Implant Body Sensor Netw. IEEE, Massachuset, pp 113–116Google Scholar
  36. 36.
    Mennicken S, Huang EM (2012) Hacking the natural habitat: an in-the-wild study of smart homes, their development, and the people who live in them. Pervasive Computing. Springer Berlin Heidelberg, pp 143–160Google Scholar
  37. 37.
    Mennicken S, Vermeulen J, Huang EM (2014) From today’s augmented houses to tomorrow’s smart homes: new directions for home automation research. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, pp 105–115Google Scholar
  38. 38.
    Munaro M, Menegatti E (2014) Fast RGB-D people tracking for service robots. Auton Robots, pp 1–16Google Scholar
  39. 39.
    Murphy Allan H, Winkler Robert L (1984) Probability forecasting in meteorology. J Am Stat Assoc 79:489–500Google Scholar
  40. 40.
    Narayana S, Prasad RV, Rao VS, Prabhakar TV, Kowshik SS, Iyer MS (2015) PIR Sensors: Characterization and Novel Localization TechniqueGoogle Scholar
  41. 41.
    Obayashi S, Zander J (1998) A body-shadowing model for indoor radio communication environments. IEEE Trans Antennas Propag 46(6):920–927CrossRefGoogle Scholar
  42. 42.
    Pärkkä Juha, Ermes Miikka, Korpipää Panu, Mäntyjärvi Jani, Peltola Johannes, Korhonen Ilkka (2006) Activity classification using realistic data from wearable sensors. IEEE Trans Inf Technol Biomed 10(1):119–128CrossRefGoogle Scholar
  43. 43.
    Paiement A, Tao L, Camplani M, Hannuna S, Damen D, Mirmehdi M (2014) Online quality assessment of human motion from skeleton data. In: Proceedings British Machine Vision Conference 2014Google Scholar
  44. 44.
    Roggen D et al (2010) Collecting complex activity datasets in highly rich networked sensor environments. In: 2010 Seventh International Conference on Networked Sensing Systems (INSS). IEEE, pp 233–240Google Scholar
  45. 45.
    Sahinoglu Z, Gezici S, Guvenc I (2008) Ultra‐wideband positioning systemsGoogle Scholar
  46. 46.
    Tan B (2015) Wi‐Fi based passive human motion sensing for in‐home healthcare applications. In: IEEE 2nd World Forum on Internet of Things. MilanGoogle Scholar
  47. 47.
    Tan B, Woodbridge K, Chetty K (2014) A real‐time high resolution passive WiFi Doppler‐radar and its applications. In: International Radar Conference. LilleGoogle Scholar
  48. 48.
    Tsimbalo E, Fafoutis X, Mellios E, Haghighi M, Tan B, Hilton G, Piechocki G, Craddock I (2015) Mitigating Packet Loss in Connectionless Bluetooth Low Energy. In: 2nd IEEE World Forum on Internet of Things (WF-IoT). Milan: IEEE. pp 291–296Google Scholar
  49. 49.
    Tsipouras MG, Tzallas AT, Rigas G, Tsouli S, Fotiadis DI, Konitsiotis S () An automated methodology for levodopa-induced dyskinesia: assessment based on gyroscope and accelerometer signals. Artif Intell Med 127–135. ElsevierGoogle Scholar
  50. 50.
    Twomey N, Diethe T, Flach P (2016) Unsupervised learning of sensor topologies for improving activity recognition in smart environments. NeurocomputingGoogle Scholar
  51. 51.
    Twomey N, Flach P (2014) Context modulation of sensor data applied to activity recognition in smart homes. In: Workshop on Learning over Multiple Contexts, European Conference on Machine Learning (ECML’14). Nancy, FranceGoogle Scholar
  52. 52.
    van Kasteren T, Noulas A, Englebienne G, Kröse B (2008) Accurate activity recognition in a home setting. In: 10th International Conference on Ubiquitous Computing—UbiComp’08. New York: ACM Press, pp 1–9Google Scholar
  53. 53.
    Wang, Y, Yang X, Zhao Y, Liu Y, Cuthbert L (2013) Bluetooth positioning using RSSI and triangulation methods. Las VegasGoogle Scholar
  54. 54.
    Winn John, Bishop Christopher M, Diethe Tom R (2015) Model-based machine learning. Microsoft Research, Cambridge, p 2015Google Scholar
  55. 55.
    Woodman, O, Harle R (2008) Pedestrian localisation for indoor environments. In: Proceedings of the 10th International Conference on Ubiquitous Computing (UbiComp’08)Google Scholar
  56. 56.
    Woznowski P, et al (2015) A multi-modal sensor infrastructure for healthcare in a residential environment. In: IEEE ICC Workshop on ICT-enabled services and technologies for eHealth and AAL. London: IEEE, pp 271–277Google Scholar
  57. 57.
    Tsai Y-L, Tu T-T, Bae H, Chou PH (2010) EcoIMU: a dual triaxial-accelerometer inertial measurement unit for wearable applications. 2010 International Conference on Body Sensor Networks (BSN), SingaporeGoogle Scholar
  58. 58.
    Zhu, Ni, et al. “Bridging e-Health and the Internet of Things: The SPHERE Project.” Intelligent Systems, IEEE (IEEE), 2015: 39–46Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Przemyslaw Woznowski
    • 1
    Email author
  • Alison Burrows
    • 1
  • Tom Diethe
    • 1
  • Xenofon Fafoutis
    • 1
  • Jake Hall
    • 1
  • Sion Hannuna
    • 1
  • Massimo Camplani
    • 1
  • Niall Twomey
    • 1
  • Michal Kozlowski
    • 1
  • Bo Tan
    • 1
  • Ni Zhu
    • 1
  • Atis Elsts
    • 1
  • Antonis Vafeas
    • 1
  • Adeline Paiement
    • 1
  • Lili Tao
    • 1
  • Majid Mirmehdi
    • 1
  • Tilo Burghardt
    • 1
  • Dima Damen
    • 1
  • Peter Flach
    • 1
  • Robert Piechocki
    • 1
  • Ian Craddock
    • 1
  • George Oikonomou
    • 1
  1. 1.Faculty of EngineeringUniversity of BristolBristolUK

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