Survey on fall detection and fall prevention using wearable and external sensors

Sensors (Switzerland)

Volumn 14, Issue 10, 2014, Pages 19806-19842

  Delahoz, Yueng Santiago ^a   Labrador, Miguel Angel ^a  

^a University of South Florida   (United States)

Author keywords

Environment awareness; Kinect; Machine learning; Mobile applications

Indexed keywords

ARTIFICIAL INTELLIGENCE; FINITE DIFFERENCE METHOD; LEARNING SYSTEMS; SURVEYS; UBIQUITOUS COMPUTING;

ENVIRONMENT AWARENESS; EXTERNAL SENSORS; FALL PREVENTION; FALL-RELATED INJURY; GENERAL DESCRIPTION; KINECT; MOBILE APPLICATIONS; STATE OF THE ART;

ACCIDENT PREVENTION;

EID: 84908519591     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s141019806     Document Type: Review

References (73)

1. Nihseniorhealth: About falls. Available online: http://nihseniorhealth.gov/falls/aboutfalls/01.html (accessed on 10 December 2013).
2. Nihseniorhealth: Causes and Risk Factors. Available online: http://nihseniorhealth.gov/falls/ causesandriskfactors/01.html (accessed on 10 January 2014).
3. Lord, S.R.; Menz, H.B.; Sherrington, C. Home environment risk factors for falls in older people and the efficacy of home modifications. Age Ageing 2006, 35 (Suppl 2), ii55-ii59.
4. Health: 10Ways to Prevent Falls at Home. Available online: http://www.health.com/health/gallery/ 0,20364937_2,00.html (accessed on 11 January 2014).
5. News & Information: Healthy Aging Column-Preventing Falls. Available online: http://www. news.colostate.edu/Release/452 (accessed on 10 January 2014).
6. CDC: Preventing Falls among Older Adults. Available online: http://www.news.colostate.edu/ Release/452 (accessed on January 15 2014).
7. Nihseniorhealth: Fall Proofing Your Home. Available online: http://nihseniorhealth.gov/falls/ homesafety/01.html (accessed on 19 January 2014).
8. Caporusso, N.; Lasorsa, I.; Rinaldi, O.; la Pietra, L. A pervasive solution for risk awareness in the context of fall prevention. In Proceedings of the 3rd International ICST Conference on Pervasive Computing Technologies for Healthcare, London, UK, 1-3 April 2009; pp. 1-8.
9. Yu, M.; Rhuma, A.; Naqvi, S.M.;Wang, L.; Chambers, J. A posture recognition based fall detection system for monitoring an elderly person in a smart home environment. IEEE Trans. Inf. Technol. Biomed. 2012, 16, 1274-1286.
10. Ozcan, K.; Mahabalagiri, A.; Casares, M.; Velipasalar, S. Automatic fall detection and activity classification by a wearable embedded smart camera. IEEE J. Emerg. Sel. Top. Circuits Syst. 2013, 3, 125-136.
11. Kim, E.; Helal, S.; Cook, D. Human activity recognition and pattern discovery. IEEE Pervasive Comput. 2010, 9, 48-53.
12. Lara, O.D.; Labrador, M.A. A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 2013, 15, 1192-1209.
13. Hirata, Y.; Komatsuda, S.; Kosuge, K. Fall prevention control of passive intelligent walker based on human model. In Proceedings of the International Conference on Intelligent Robots and Systems, Nice, France, 22-26 September 2008; pp. 1222-1228.
14. Yu, X. Approaches and principles of fall detection for elderly and patient. In Proceedings of the 10th International Conference on e-health Networking, Applications and Services, Singapore, 7-9 July 2008; pp. 42-47.
15. López, J.M.; Herrero, J.G. Técnicas de análisis de datos. Aplicaciones Prácticas Utilizando Microsoft Excel y WEKA; Technical Report; Universidad Carlos III De Madrid: Madrid, Spain, 2006.
16. Sapsford, R.; Jupp, V. Data Collection and Analysis, 2nd ed.; SAGE Publications Ltd.: London, UK, 2006.
17. Guyon, I.; Elisseeff, A. An Introduction to Feature Extraction. In Feature Extraction; Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L., Eds.; Springer Berlin Heidelberg: Zurich, Switzerland, 2006; Volume 207, pp. 1-25.
18. Witten, I.H.; Frank, E.; Hall, M.A. Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed.; Morgan Kaufmann Publishers Inc.: Burlington, MA, USA, 2011.
19. Mayfield, E.; Penstein-Rosé, C. Using feature construction to avoid large feature spaces in text classification. In Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, Portland, OR, USA, 7-11 July 2010; pp. 1299-1306.
20. Turaga, P.; Chellappa, R.; Subrahmanian, V.; Udrea, O. Machine recognition of human activities: A survey. IEEE Trans. Circuits Syst. Video Technol. 2008, 18, 1473-1488.
21. Quinlan, J.R. C4.5: Programs for Machine Learning, 1st ed.; Morgan Kaufmann Publishers Inc.: San Mateo, CA, USA, 1993.
22. Campbell, C.; Cristianini, N. Simple Learning Algorithms for Training Support Vector Machines; Technical Report; University of Bristol, Bristol, UK, 1998.
23. Chapelle, O. Training a support vector machine in the primal. Neural Comput. 2007, 19, 1155-1178.
24. Gunawardana, A.; Shani, G. A Survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res. 2009, 10, 2935-2962.
25. Szeliski, R. Computer Vision: Algorithms and Applications, 1st ed.; Springer Science and Business Media: Redmond, WA, USA, 2010.
26. Gasparrini, S.; Cippitelli, E.; Spinsante, S.; Gambi, E. A depth-based fall detection system using a Kinect sensor. Sensors 2014, 14, 2756-2775.
27. Lara, O.D. On the Automatic Recognition of Human Activities Using Heterogeneous Wearable Sensors. Ph.D. Thesis, University of South Florida, Tampa, FL, USA, 2012.
28. Lent, B. Practical Considerations of Accelerometers Noise; Technical Report; MEGGITT: Christchurch, UK, 2007.
29. Chen, Y.T.; Lin, Y.C.; Fang, W.H. A hybrid human fall detection scheme. In Proceedings of the International Conference on Image Processing, Hong Kong, China, 26-29 September 2010; pp. 3485-3488.
30. Hirata, Y.; Muraki, A.; Kosuge, K. Motion control of intelligent passive-type walker for fall-prevention function based on estimation of user state. In Proceedings of the International Conference on Robotics and Automation, Orlando, FL, USA, 15-19 May 2006; pp. 3498-3503.
31. Williams, A.; Ganesan, D.; Hanson, A. Aging in place: Fall detection and localization in a distributed smart camera network. In Proceedings of the 15th International Conference on Multimedia, Bavaria, Germany, 23-28 September 2007; pp. 892-901.
32. Vaidehi, V.; Ganapathy, K.; Mohan, K.; Aldrin, A.; Nirmal, K. Video based automatic fall detection in indoor environment. In Proceedings of the International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, Tamil Nadu, India, 3-5 June 2011; pp. 1016-1020.
33. Rougier, C.; Meunier, J.; St-Arnaud, A.; Rousseau, J. Fall detection from human shape and motion history using video surveillance. In Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops, Niagara Falls, ON, Canada, 21-23 May 2007; pp. 875-880.
34. Wong, W.K.; Lim, H.L.; Loo, C.K.; Lim, W.S. Home alone faint detection surveillance system using thermal camera. In Proceedings of the 2nd International Conference on Computer Research and Development, Kuala Lumpur, Malaysia, 7-10 May 2010; pp. 747-751.
35. Alwan, M.; Rajendran, P.J.; Kell, S.; Mack, D.; Dalal, S.; Wolfe, M.; Felder, R. A smart and passive floor-vibration based fall detector for elderly. In Proceedings of the International Conference on Information and Communication Technologies, Damascus, Syria, 24-28 April 2006; pp. 1003-1007.
36. Sixsmith, A.; Johnson, N.; Whatmore, R. Pyroelectric IR sensor arrays for fall detection in the older population. J. Phys. 2005, 128, 153-160.
37. Sixsmith, A.; Johnson, N. A smart sensor to detect the falls of the elderly. IEEE Pervasive Comput. 2004, 3, 42-47.
38. Scott, T. Bed Exit Detection Apparatus. Google Patents, 2000. Available online: http://www. google.com/patents/US6067019 (accessed on 19 January 2014).
39. Haselman, M.; Hauck, S. The future of integrated circuits: A survey of nanoelectronics. Proc. IEEE 2010, 98, 11-38.
40. Chen, G.; Huang, C.; Chiang, C. A reliable Fall Detection System Based on Wearable Sensor and Signal Magnitude Area for Elderly Residents. In Aging Friendly Technology for Health and Independence; Lee, Y., Bien, Z.Z., Mokhtari, M., Kim, J.T., Park, M., Kim, J., Lee, H., Khalil, I., Eds.; Springer Berlin Heidelberg: Berlin Heidelberg, Germany, 2010; pp. 267-270.
41. Sorvala, A.; Alasaarela, E.; Sorvoja, H.; Myllyla, R. A two-threshold fall detection algorithm for reducing false alarms. In Proceedings of the 6th International Symposium on Medical Information and Communication Technology (ISMICT), La Jolla, CA, USA, 25-29 March 2012; pp. 1-4.
42. Vallejo, M.; Isaza, C.V.; Lopez, J.D. Artificial neural networks as an alternative to traditional fall detection methods. In Proceedings of the 35th Annual International Conference on Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3-7 July 2013; pp. 1648-1651.
43. Hsieh, C.J.; Chiang, C.Y.; Huang, C.N.; Chan, C.T.; Hsu, S.J. Development of fall characteristics collection system for fall prevention strategies. In Proceedings of the 5th International Conference on Bioinformatics and Biomedical Engineering, Wuhan, China, 10-12 May 2011; pp. 1-4.
44. Zweifel, P.; Felder, S.; Meiers, M. Ageing of population and health care expenditure: A red herring? Health Econ. 1999, 8, 485-496.
45. Takeda, T.; Sakai, Y.; Kuramoto, K.; Kobashi, S.; Ishikawa, T.; Hata, Y. Foot age estimation for fall-prevention using sole pressure by fuzzy logic. In Proceedings of the International Conference on Systems, Man, and Cybernetics, Anchorage, AK, USA, 9-12 October 2011; pp. 769-774.
46. Di, P.; Huang, J.; Sekiyama, K.; Fukuda, T. A novel fall prevention scheme for intelligent cane robot by using a motor driven universal joint. In Proceedings of the International Symposium on Micro-NanoMechatronics and Human Science, Nagoya, Japan, 6-9 November 2011; pp. 391-396.
47. Shiozawa, N.; Arai, S.; Okada, S.; Makikawa, M. Gait analysis of sit-to-walk motion by using portable acceleration monitor device for fall prevention. In Proceedings of the International Conference on Biomedical and Health Informatics, Hong Kong, China, 5-7 January 2012; pp. 563-564.
48. Rantz, M.J.; Skubic, M.; Abbott, C.; Galambos, C.; Pak, Y.; Ho, D.K.C.; Stone, E.E.; Rui, L.; Back, J.; Miller, S.J. In-home fall risk assessment and detection sensor system. J. Gerontol. Nurs. 2013, 39, 18-22.
49. Sharpbrains: What Are Cognitive Abilities and Skills, and How to Boost Them? Available online: http://sharpbrains.com/blog/2006/12/18/what-are-cognitive-abilities/ (accessed on 10 February 2014).
50. Peel, N.M.; McClure, R.J.; Hendrikz, J.K. Psychosocial factors associated with fall-related hip fractures. Age Ageing 2007, 36, 145-151.
51. Bilgin, T.; Erdogan, S. A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data. IET Commun. 2012, 6, 3281-3287.
52. Yu, M.; Yu, Y.; Rhuma, A.; Naqvi, S.M.R.; Wang, L.; Chambers, J.A. An online one class support vector machine-based person-specific fall detection system for monitoring an elderly individual in a room environment. IEEE J. Biomed. Health Inf. 2013, 17, 1002-1014.
53. Bashir, F. Real life applicable fall detection system based on wireless body area network. In Proceedings of the 10th Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, 11-14 January 2013; pp. 62-67.
54. Dai, J.; Bai, X.; Yang, Z.; Shen, Z.; Xuan, D. PerFallD: A pervasive fall detection system using mobile phones. In Proceedings of the 8th International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), Mannheim, Germany, 29 March-2 April 2010; pp. 292-297.
55. Shi, Y.; Shi, Y.C.; Wang, X. Fall detection on mobile phones using features from a five-phase model. In Proceedings of the 9th International Conference on Ubiquitous Intelligence and Computing, and Autonomic and Trusted Computing, Fukuoka, Japan, 4-7 September 2012; pp. 951-956.
56. Werner, F.; Diermaier, J.; Panek, P.; Schmid, S. Fall detection with distributed floor-mounted accelerometers: An overview of the development and evaluation of a fall detection system within the project eHome. In Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, Dublin, Ireland, 23-26 May 2011; pp. 354-361.
57. Sengto, A.; Leauhatong, T. Human falling detection algorithm using back propagation neural network. In Proceedings of the 5th Biomedical Engineering International Conference, Ubon Ratchathani, Thailand, 5-7 December 2012 ; IEEE: Ubon Ratchathani, Thailand, 2012; pp. 1-5.
58. Humenberger, M.; Schraml, S.; Sulzbachner, C.; Belbachir, A.N.; Srp, A.; Vajda, F. Embedded fall detection with a neural network and bio-inspired stereo vision. In Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA, 16-21 June 2012; pp. 60-67.
59. Chen, D.; Feng, W.; Zhang, Y.; Li, X.; Wang, T. A wearable wireless fall detection system with accelerators. In Proceedings of the International Conference on Robotics and Biomimetics (ROBIO), Karon Beach, Phuket, 7-11 December 2011; pp. 2259-2263.
60. Yuwono, M.; Su, S.W.; Moulton, B. Fall detection using a gaussian distribution of clustered knowledge, augmented radial basis neural-network, and multilayer perceptron. In Proceedings of the 7th International Conference on Broadband Communications and Biomedical Applications, Melbourne, VIC, Australia, 21-24 November 2011; pp. 145-150.
61. Ojetola, O.; Gaura, E.I.; Brusey, J. Fall detection with wearable sensors-SAFE (Smart Fall DEtection). In Proceedings of the 7th International Conference on Intelligent Environments, Nottingham, UK, 25-28 July 2011; pp. 318-321.
62. Shoaib, M.; Dragon, R.; Ostermann, J. View-invariant fall detection for elderly in real home environment. In Proceedings of the Fourth Pacific-Rim Symposium on Image and Video Technology, Singapore, 14-17 November 2010; pp. 52-57.
63. Ni, B.; Nguyen, C.; Moulin, P. RGBD-camera based get-up event detection for hospital fall prevention. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 25-30 March 2012; pp. 1405-1408.
64. Majumder, A.J.A.; Zerin, I.; Uddin, M.; Ahamed, S.I.; Smith, R.O. smartPrediction: A real-time smartphone-based fall risk prediction and prevention system. In Proceedings of the Research in Adaptive and Convergent Systems, Montreal, QC, Canada, 1-4 October 2013; pp. 434-439.
65. Oliver, D.; Britton, M.; Seed, P.; Martin, F.C.; Hopper, A.H. Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: Case-control and cohort studies. BMJ (Clin. Res. ed.) 1997, 315, 1049-1053.
66. Oliver, D.; Papaioannou, A.; Giangregorio, L.; Thabane, L.; Reizgys, K.; Foster, G. A systematic review and meta-analysis of studies using the STRATIFY tool for prediction of falls in hospital patients: How well does it work? Age Ageing 2008, 37, 621-627.
67. Dalal, N.; Triggs, B. Histograms of oriented gradients for human detection. In Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, 25-25 June 2005; pp. 886-893.
68. The Geometry Center: Computing cOnstrained Delaunay Triangulation Algorithm. Available online: http://www.geom.uiuc.edu/~samuelp/del_project.html (accessed on 5 February 2014).
69. Belbachir, A.; Litzenberger, M.; Schraml, S.; Hofstatter, M.; Bauer, D.; Schon, P.; Humenberger, M.; Sulzbachner, C.; Lunden, T.; Merne, M. CARE: A dynamic stereo vision sensor system for fall detection. In Proceedings of the International Symposium on Circuits and Systems, Seoul, Korea, 20-23 May 2012; pp. 731-734.
70. Kuris, B.; Dishongh, T. SHIMMER-Sensing Health with Intelligence, Modularity, Mobility, and Experimental Reusability. Intel Corp. 2006, 23, 104-107.
71. Wathen-Dunn, W. Models for the Perception of Speech and Visual Form: Proceedings of a Symposium, 1st ed.; M.I.T. Press: Cambridge, MA, USA, 1967; p. 470.
72. Laptev, I. On space-time interest points. Int. J. Comput. Vis. 2005, 64, 107-123.
73. Collin, C.; Wade, D.T.; Davies, S.; Horne, V. The Barthel ADL Index: A reliability study. Int. Disabil. Stud. 1988, 10, 61-63.