Seamless tracing of human behavior using complementary wearable and house-embedded sensors

Sensors (Switzerland)

Volumn 14, Issue 5, 2014, Pages 7831-7856

  Augustyniak, Piotr ^a   Smoleń, Magdalena ^a   Mikrut, Zbigniew ^a   Kańtoch, Eliasz ^a  

^a AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Poland)

Author keywords

Aging society; Ambient assisted living; Home care; Surveillance

Indexed keywords

ACCELEROMETERS; COMPLEX NETWORKS; SENSOR NETWORKS; SENSORS; SPACE SURVEILLANCE; WEARABLE COMPUTERS;

AGING SOCIETIES; AMBIENT ASSISTED LIVING; HISTOGRAM-BASED METHOD; HOME CARE; LIVING ENVIRONMENT; OUTDOOR ACTIVITIES; VIDEO-BASED SYSTEMS; WEARABLE SENSOR NETWORKS;

MOTION ESTIMATION;

EID: 84899745798     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s140507831     Document Type: Article

References (74)

1. Klonoff, D. C. Diabetes and telemedicine-is the technology sound, effective, cost-effective, and practical? Diabet. Care 2003, 26, 1626-1628.
2. Farmer, A.; Gibson, O. J.; Tarassenko, L.; Neil, A. A Systematic review of telemedicine interventions to support blood glucose self-monitoring in diabetes. Diabet. Med. 2005, 6, 1372-1378.
3. Wainwright, C.; Wootton, R. A review of telemedicine and asthma. Dis. Manag. Health Outcome. 2003, 11, 557-563.
4. Ostojic, V.; Cvoriscec, B.; Ostojic, S. B.; Reznikoff, D.; Stipic-Markovic, A.; Tudjman, Z. Improving asthma control through telemedicine: A study of short-message service. Telemed. J. E-Health 2005, 11, 28-35.
5. Atoui, H.; Telisson, D.; Fayn, J.; Rubel, P. Ambient intelligence and pervasive architecture designed within the EPI-MEDICS personal ECG monitor. Int. J. Health. Inform. Syst. Inform. 2008, 3, 68-80.
6. Augustyniak, P.; Tadeusiewicz, R. Ubiquitous Cardiology: Emerging Wireless Telemedical Applications; Medical Information Science Reference: Hershey, NY, USA, 2009.
7. Nishiguchi, S.; Yamada, M.; Nagai, K.; Shuhei, M.; Yuu, K.; Takuya S.; Kazuya, Y.; Hiroyuki, Y.; Hiromu, I.; Kazuya, O.; et al. Reliability and validity of gait analysis by android based smartphone. Telemed. E-health 2012, 18, 292-296.
8. Guo, Y.; Wu, D.; Liu, G.; Zhao, G.; Huang, B.; Wang L. A low-cost body inertial-sensing network for practical gait discrimination of hemiplegia patients. Telemed. J. E-Health 2012, 18, 748-75.
9. Bourke, A. K.; Lyons, G. M. A Threshold-based fall-detection algorithm using a Bi-axial gyroscope sensor. Med. Eng. Phys. 2008, 30, 84-90.
10. Kangas, M.; Konttila, A.; Lindgren, P.; Winblad, I.; Jämsä, T. Comparison of low complexity fall detection algorithms for body attached accelerometers. Gait Posture 2008, 28, 285-291.
11. Sardini, E.; Serpelloni, M. Instrumented wearable belt for wireless health monitoring. Proced. Eng. J. 2010, 5, 580-583.
12. Lee, Y. D.; Chung, W. Y. Wireless sensor network based wearable smart shirt for ubiquitous health and activity monitoring. Sens. Actuators B Chem. 2009, 140, 390-395.
13. Alemdar, H.; Ersoy, C. Wireless sensor networks for healthcare: A survey. Int. J. Comput. Telecommun. Netw. 2010, 54, 2688-2710.
14. Dilmaghani, R. S.; Bobarshad, H.; Ghavami, M.; Choobkar, S.; Wolfe, C. Wireless sensor networks for monitoring physiological signals of multiple patients. Biomed. Circuit Syst. 2011, 5, 347-356.
15. Chen, B.; Pompili, D. Transmission of patient vital signs using wireless body area networks. Mob. Netw. Appl. 2011, 6, 663-682.
16. Ullah, S.; Higgins, H.; Braem, B.; Latre, B.; Blondia, C.; Moerman, I.; Saleem, S.; Rahman, Z.; Kwak, K. A comprehensive survey of wireless body area networks. J. Med. Syst. 2010, 10, 1-30.
17. Latré, B.; Braem, B.; Moerman, I.; Blondia, C.; Demeester, P. A Survey on Wireless Body Area Networks. Wirel. Netw. 2011, 17, 1-18.
18. Chan, M.; Campo, E.; Estève, D.; Fourniols, J. Y. Smart Houses-Current Feature and Future Perspectives. Maturitas 2009, 64, 490-497.
19. Tamura, T. Home Geriatric Physiological Measurements. Physiolog. Meas. 2012, 33, R47-R65.
20. Wtorek, J.; Bujnowski, A.; Rumiński, J.; Poliński, A.; Kaczmarek, M.; Nowakowski, A. Assessment of cardiovascular risk in assisted living. Metrol. Meas. Syst. 2012, 19, 231-244.
21. Denman, S. P.; Chandran, V.; Sridharan, S. An adaptive optical flow technique for person tracking systems. Patt. Recog. Lett. 2007, 10, 1232-1239.
22. Patel, H.; Wankhade, M. P. Human tracking in video surveillance. Int. J. Emerg. Technol. Adv. Eng. 2011, 2, 1-4.
23. Jang, B. J.; Wi, S. H.; Yook, J. G.; Lee, M. Q.; Lee, K. J. Wireless bio-radar sensor for heartbeat and respiration detection. Prog. Electromag. Res. C 2008, 5, 149-168.
24. Srinivasan, S.; Pandharipande, A.; Caicedo, D. Presence detection using wideband audio-ultrasound sensor. Electr. Lett. 2012, 48, 1577-1578.
25. Lotfi, A.; Langensiepen, C.; Mahmoud, S. M.; Akhlaghinia, M. J. Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behavior. J. Ambient Intell. Humaniz. Comput. 2012, 3, 205-218.
26. Bujnowski, A.; Skalski, L.; Wtorek, J. Monitoring of a bathing person. J. Med. Imag. Health Inform. 2012, 2, 27-34.
27. Mubashir, M.; Shao, L.; Seed, L. A survey on fall detection: Principles and approaches. Neurocomputing 2013, 100, 144-152.
28. Holte, M. B.; Tran, C.; Trivedi, M. M.; Moeslund, T. B. Human pose estimation and activity recognition from multi-view videos: Comparative explorations of recent developments. IEEE J. Select. Topic. Signal Process. 2012, 6, 538-552.
29. Brdiczka, O.; Crowley, J. L.; Reignier, P. Learning situation models in a Smart Home. IEEE Trans. Systems, Man, Cybernetics-part B: Cybernet. 2009, 39, 56-63.
30. Rashidi, P.; Mihailidis, A. A survey on ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inform. 2013, 17, 579-590.
31. Memon, M.; Wagner, S. R.; Pedersen, C. F.; Hassan, F.; Beevi, A.; Hansen, F. O. Ambient assisted living healthcare frameworks, platforms, standards, and quality attributes. Sensors 2014, 14, 4312-4341.
32. Damas, M.; Pomares, H.; Gonzalez, S.; Olivares, A.; Rojas, I. Ambient assisted living devices interoperability based on OSGi and the X73 standard. Telemed. e-Health Jan. 2013, doi: 10. 1089/tmj. 2012. 0052.
33. Matern, D.; Condurache, A.; Mertins, A. Adaptive and automated ambiance surveillance and event detection for ambient assisted living. In Proceedings of the 35th Annual International Conference of the IEEE EMBS, Osaka, Japan, 3-7 July 2013, pp. 7318-7321.
34. Aquino-Santos, R.; Martinez-Castro, D.; Edwards-Block, A.; Murillo-Piedrahita, A. F. Wireless sensor networks for ambient assisted living. Sensors 2013, 13, 16384-16405.
35. Lühr, S.; West, G.; Venkatesh, S. Recognition of emergent human behaviour in a smart home: A data mining approach. Perv. Mob. Comput. 2007, 3, 95-116.
36. Xin, H.; Chris, N.; Maurice, M.; Sally, M.; Bryan, S.; Steven, D. Evidential fusion of sensor data for activity recognition in smart homes. Perv. Mob. Comput. 2009, 5, 236-252.
37. Bouziane, A.; Chahir, Y.; Molina, M.; Jouen, F. Unified framework for human behaviour recognition: Anapproach using 3D zernike moments. Neurocomputing 2013, 100, 107-116.
38. Ros, M.; Cuéllar, M. P.; Delgado, M.; Vila, A. Online recognition of human activities and adaptation to habit changes by means of learning automata and fuzzy temporal windows. Inform. Sci. 2013, 220, 86-101.
39. Ayu, M. A.; Ismail, S. A.; Matin, A. F. A.; Mantorob, T. A comparison study of classifier algorithms for mobile-phone's accelerometer based activity recognition. Proc. Eng. 2012, 41, 224-229.
40. Garcia-Ceja, E.; Brena, R. Long-term activity recognition from accelerometer data. Proc. Technol. 2013, 7, 248-256.
41. Amini, N.; Sarrafzadeh, M.; Vahdatpour, A.; Xu, W. Accelerometer-based on-body sensor localization for health and medical monitoring applications. Perv. Mob. Comput. 2011, 7, 746-760.
42. Bagala, F.; Becker, C.; Cappello, A.; Chiari, L.; Aminian, K.; Hausdorff, J. M.; Zijlstra, W.; Klenk, J. Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS One 2012, 7, e37062.
43. Bourke, A. K.; O'Brien, J. V.; Lyons, G. M. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm, Gait Posture 2007, 26, 194-199.
44. Liu, J.; Wang, Z.; Zhong, L.; Wickramasuriya, J.; Vasudevan, V. UWave: Accelerometer-based personalized gesture recognition and its applications. Perv. Mob. Comput. 2009, 5, 657-675.
45. Poppe, R. A survey on vision-based human action recognition, Image Vis. Comput. 2010, 28, 976-990.
46. Weinland, D.; Ronfard, R.; Boyer, E. A survey of vision-based methods for action representation, segmentation and recognition. Comput. Vis. Image Underst. 2011, 115, 224-241.
47. Chen, L.; Wei, H.; Ferryman, J. A survey of human motion analysis using depth imagery. Patt. Recog. Lett. 2013, 34, 1995-2006.
48. Li, C.; Hua, T. Human action recognition based on template matching. Proc. Eng. 2011, 15, 2824-2830.
49. Roshtkhari, M. J.; Levine, M. D. Human activity recognition in videos using a single example. Image Vis. Comput. 2013, 31, 864-876.
50. Rahman, S. A.; Leung, M. K. H.; Cho, S-Y. Human action recognition employing negative space features, J. Vis. Commun. Image R. 2013, 24, 217-231.
51. Dudzinski, D.; Kryjak, T.; Mikrut, Z. Human action recognition using simple geometric features and a finite state machine. Image Proc. Commun. 2013, 18, 49-60.
52. Juang, C. F.; Chang, C. M. Human body posture classification by a neural fuzzy network and home care system application. IEEE Trans. Systems, Man, Cybernetics-Part A: Syst. Humans 2007, 37, 984-994.
53. Li, C. C.; Chen, Y. Y. Human Posture Recognition by Simple Rules. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Taipei, Taiwan, 8-11 October 2006, pp. 3237-3240.
54. Juang, C. F.; Chang, C. M.; Wu, J. R.; Lee, D. Computer vision-based human body segmentation and posture estimation. IEEE Trans. Systems, Man, Cybernetics-Part A: Syst. Humans 2009, 39, 119-133.
55. Otto, C.; Milenkovic, A.; Sanders, C.; Jovanov, E. System architecture of a wireless body area sensor network for ubiquitous health monitoring. J. Mob. Multimed. 2006, 1, 307-326.
56. Tompkins, W. J.; Pan, J. A real-time QRS detection algorithm, biomedical signal analysis. IEEE Press 1985, BME-32, 230-236.
57. Ślusarczyk, G.; Augustyniak, P. A graph representation of subject's time-state space. Inform. Technol. Biomed. 2010, 2, 379-390.
58. Barron, J. L.; Fleet, D. J.; Beauchemin, S. S. Performance of optical flow techniques. Int. J. Comput. Vis. 1994, 12, 43-77.
59. Liu, H.; Hong, T.; Herman, M.; Camus, T.; Chellappa, R. Accuracy vs. efficiency trade-offs in optical flow algorithms. Comput. Vis. Image Underst. 1998, 72, 271-286.
60. Mikrut, Z.; Smoleń, M. A neural network approach to recognition of the selected human motion patterns. Automatics/Automatyka 2011, 15, 521-534.
61. Głowacz, A.; Mikrut, Z.; Pawlik, P. Video detection algorithm using an optical flow calculation method. Multimed. Commun. Serv. Secur. Commun. Comput. Sci. 2012, 287, 118-129.
62. Horn, B. K. P.; Schunck, B. G. Determining optical flow: A retrospective. Artif. Intell. 1993, 59, 81-87.
63. Lucas, B. D.; Kanade, T. An iterative image registration technique with an application to stereo vision. In Proceedings of 7th International Joint Conference on Artificial Intelligence, Vancouver, Canada, 24-28 Month 1981, pp. 674-679.
64. Institute of Medical Technology and Equipment. Manual for Revitus System; IU 468: 2009-08, 2009.
65. Smoleń, M. Analiza wybranych modalności rejestracji ruchu dla monitorowania pacjentów (Analysis of the selected modalities of motion recording for patients' monitoring application), in Polish. Ph. D. Thesis, AGH University of Science and Technology, Kraków, Poland, 2013.
66. Jóźwik, A. Minimalno-odległościowe i inne metody konstrukcji klasyfikatorów odcinkowo-liniowych, (Minimal-distance methods and other methods of construction the piecewise linear classifiers), in Polish. DSc. Thesis, Polish Academy of Sciences, Nałe{ogonek}cz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Poland, 2006.
67. Głowacz, A. Diagnostics of induction motor based on analysis of acoustic signals with the application of eigenvector method and k-nearest neighbor classifier. Arch. Metallur. Mater. 2012, 57, 403-407.
68. Głowacz, A.; Głowacz, W. Diagnostics of direct current motor with application of acoustic signals, reflection coefficients and k-nearest neighbor classifier. Electrotech. Rev. 2012, 88, 231-233.
69. Augustyniak, P.; Smoleń M.; Broniec, A.; Chodak, J. Data integration in multimodal home care surveillance and communication system. Inform. Technol. Biomed. 2010, 2, 391-402.
70. Augustyniak, P. Distance Measures in Behavioral Pattern Analysis. In Proceedings of the IFMBE, Budapest, Hungary, 14-18 September 2011, pp. 894-897.
71. Rabiner, L. Considerations in dynamic time-warping algorithms for discrete word recognition. IEEE Trans. Signal Process. 1978, 26, 575-582.
72. Syed, Z.; Guttag, J.; Stultz, C. Clustering and symbolic analysis of cardiovascular signals: discovery and visualization of medically relevant patterns in long-term data with limited prior knowledge. EURASIP J. Appl. Signal Process. 2007, 1, 97.
73. Kańtoch, E. Technical Verification of applying Wearable Physiological Sensors in Ubiquitous Health Monitoring. Computing in Cardiology, 2013, 40, 269-272.
74. Kańtoch, E.; Augustyniak, P. Wearable mobile network as an integrated part of assisted living technologies. Lect. Note. Comput. Sci. 2012, 7339, 549-559.