Survey of Sensor-Based Personal Wellness Management Systems

BioNanoScience

Volumn 3, Issue 3, 2013, Pages 254-269

  Yumak, Zerrin ^a   Pu, Pearl ^a  

^a EPFL   (Switzerland)

Author keywords

Off the shelf sensors; Personal wellness management systems; User centric evaluation

Indexed keywords

HEALTHY LIFESTYLES; RESEARCH OPPORTUNITIES; SELF-MONITORING; SENSOR TECHNOLOGIES; TECHNOLOGICAL ADVANCES; TECHNOLOGICAL TOOLS; USER-CENTRIC EVALUATIONS; WELLNESS MANAGEMENT;

MANAGEMENT; SURVEYS;

SENSORS;

ACCELEROMETER; ARTICLE; BEHAVIOR CHANGE; ELECTROCARDIOGRAPHY; ELECTRODERMAL RESPONSE; ELECTROENCEPHALOGRAPHY; ELECTROMYOGRAPHY; ELECTROOCULOGRAPHY; EXERCISE; EYE MOVEMENT; HEALTH SURVEY; HEART RATE; LEARNING; LIFESTYLE MODIFICATION; MAGNETOMETRY; MOBILE SENSOR; NUTRITION; PEDOMETER; PERSUASIVE COMMUNICATION; PHOTOELECTRIC PLETHYSMOGRAPHY; PHYSICAL ACTIVITY; PNEUMOGRAPHY; POLYSOMNOGRAPHY; PRIVACY; SELF MONITORING; SENSOR; SLEEP; SLEEP DISORDERED BREATHING;

EID: 84887335812     PISSN: 21911630     EISSN: 21911649     Source Type: Journal    
DOI: 10.1007/s12668-013-0099-0     Document Type: Article

References (94)

1. Dishman, E. (2004). Inventing wellness systems for aging in place. Computer, 37(5), 34-41.
2. Pantelopoulos, A., & Bourbakis, N. (2010). A survey on wearable sensor-based systems for health monitoring and prognosis. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(1), 1-12.
3. Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A. T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140-150.
4. Lara, O. D., & Labrador, M. A. (2012). A survey on human activity recognition using wearable sensors. Tech. rep. http://www. cse. usf. edu/files/69521327941111Survey. pdf. Accessed March 2013.
5. Adidas MiCoach. http://www. adidas. com/us/micoach. Accessed March 2013.
6. Nike+iPod. http://www. apple. com/sg/ipod/nike. Accessed March 2013.
7. BodyMedia SenseWear. http://sensewear. bodymedia. com. Accessed March 2013.
8. Omron Pedometer. http://www. omronhealthcare. com. Accessed March 2013.
9. MyBasis. http://www. mybasis. com. Accessed March 2013.
10. BodyBugg. http://www. bodybugg. com. Accessed March 2013.
11. FitBit. http://www. fitbit. com. Accessed March 2013.
12. Affectiva Q. http://www. affectiva. com/q-sensor. Accessed March 2013.
13. Philips Direct Life. http://www. directlife. philips. com. Accessed March 2013.
14. JawBone UP. https://jawbone. com/up. Accessed March 2013.
15. WakeMate. http://www. wakemate. com. Accessed March 2013.
16. Metria. https://www. averydennison. com. Accessed March 2013.
17. Zeo SleepManager. http://www. myzeo. com/sleep. Accessed March 2013.
18. Valencell. http://www. valencell. com. Accessed March 2013.
19. Zephyr. http://www. zephyr-technology. com. Accessed March 2013.
20. Polar. http://www. polar. com/en. Accessed March 2013.
21. Nexus Respiration Sensor. http://www. mindmedia. nl. Accessed March 2013.
22. Quantified-Self. http://quantifiedself. com/. Accessed March 2013.
23. Personal Informatics. http://personalinformatics. org/. Accessed March 2013.
24. Amft, O., & Troster, G. (2008). Recognition of dietary activity events using on-body sensors. Artificial Intelligence in Medicine, 42(2), 121-136.
25. Amft, O., Bannach, D., Pirkl, G., Kreil, M., Lukowicz, P. (2010). Towards wearable sensing-based assessment of fluid intake. In Pervasive Computing and Communications Workshop on, (pp. 298-303).
26. MyFoodPhone. http://en. wikipedia. org/wiki/Myca. Accessed March 2012.
27. Noronha, J., Hysen, E., Zhang, H., Gajos, K. Z. (2011). Platemate: crowdsourcing nutritional analysis from food photographs. In Proceedings of the 24th annual ACM symposium on User Interface Software and Technology, UIST '11, (pp. 1-12). Santa Barbara, California, USA.
28. Chae, J., Woo, I., Kim, S., Maciejewski, R., Zhu, F., Delp E. J., et al. (2011). Volume estimation using food specific shape templates in mobile image-based dietary assessment. Proceedings of SPIE, International society for optics and photonics, 7873.
29. Arab, L., Estrin, D., Kim, D. H., Burke, J., Goldman, J. (2011). Feasibility testing of an automated image-capture method to aid dietary recall. European Journal of Clinical Nutrition, 65, 1156-1162.
30. Reddy, S., Parker, A., Hyman, J., Burke, J., Estrin, D., Hansen, M. (2007). Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype. In Proceedings of the 4th workshop on Embedded networked sensors (pp. 13-17).
31. Siek, K. A., Connelly, K. H., Rogers, Y., Rohwer, P., Lambert, D., Welch, J. L. (2006). When do we eat? An evaluation of food items input into an electronic food monitoring application. In Pervasive Health Conference and Workshops. (pp. 1-10).
32. Kallehave, O., Skov, M. B., Tiainen, N. (2011). Persuasion in-situ: shopping for healthy food in supermarkets. In The 2nd International Workshop on Persuasion, Influence, Nudge and Coercion through mobile devices, CHI '11.
33. Mankoff, J., Hsieh, G., Hung, H. C., Lee S., Nitao, E. (2002). Using low-cost sensing to support nutritional awareness. In Proceedings of the 4th international conference on Ubiquitous Computing, UbiComp '02. (pp. 371-376). London, UK.
34. Vyas, N., Farringdon, J., Andre, D., Stivoric, J. (2011). Machine learning and sensor fusion for estimating continuous energy expenditure. In D. G. Shapiro, & M. P. J. Fromherz (Eds.), 23rd IAAI Innovative Applications of Artificial Intelligence Conference.
35. Ainsworth, B. E., Haskell, W. L., Whitt, M. C., Irwin, M. L., Swartz, A. M., Strath, S. J., O'Brien, W. L., Bassett, D. R., Jr., Schmitz, K. H., Emplaincourt, P. O., Jacobs, D. R., Jr., Leon, A. S. (2000). Compendium of S codes and MET intensities. Medicine and Science in Sports and Exercise, 32(Suppl), S498-S516.
36. Ainsworth, B. E., Haskell, W. L., Herrmann, S. D., Meckes, N., Jr., Bassett, D. R., Tudor-Locke, C., Greer, J. L., Vezina, J., Whitt-Glover, M. C., Leon, A. S. (2011). 2011 compendium of physical activities: a second update of codes and MET values. Medicine and Science in Sports and Exercise, 43(8), 1575-1581.
37. Byrne, N. M., Hills, A. P., Hunter, G. R., Weinsier, R. L., Schutz, Y. (2010). Metabolic equivalent: one size does not fit all. Journal of Applied Physiology, 99(3), 1112-1119.
38. Bao, L., & Intille, S. S. (2004). Activity recognition from user-annotated acceleration data. In Pervasive Computing, Second International Conference, PERVASIVE 2004, Vienna, Austria, volume 3001, (pp. 1-17). Springer.
39. Ravi, N., Dandekar, N., Mysore, P., Littman, M. L. (2005). Activity recognition from accelerometer data. In Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence IAAI. (pp. 1541-1546).
40. Tapia, E. M., Intille, S. S., Haskell, W., Larson, K., Wright, J., King, A., et al. (2007). Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers, ISWC '07. (pp. 1-4). Washington, DC, USA. IEEE Computer Society.
41. Kwapisz, J. R., Weiss, G. M., Moore, S. A. (2011). Activity recognition using cell phone accelerometers. SIGKDD Exploration Newsletter, 12(2), 74-82.
42. Sazonov, E. S., Fulk, G., Hill, J., Schutz, Y., Browning, R. (2011). Monitoring of posture allocations and activities by a shoe-based wearable sensor. IEEE Transactions on Biomedical Engineering, 58, 983-990.
43. Lin, J. J., Mamykina, L., Lindtner, S., Delajoux, G., Strub, H. B. (2006). Fish'n'Steps: encouraging physical activity with an interactive computer game. In P. Dourish & A. Friday (Eds.), Ubicomp, volume 4206 of Lecture Notes in Computer Science. (pp. 261-278). Springer.
44. Barkhuus, L., Maitl, J., Anderson, I., Sherwood, S., Hall, M., Chalmers M. (2006). Shakra: sharing and motivating awareness of everyday activity. In Ubicomp 2006. ACM Press.
45. Consolvo, S., McDonald, D. W., Toscos, T., Chen, M. Y., Froehlich, J., Harrison, B., et al. (2008). Activity sensing in the wild: a field trial of Ubifit garden. In Proceedings of the 26th annual SIGCHI conference on Human factors in computing systems, CHI '08. (pp. 1797-1806), New York, NY, USA.
46. Choudhury, T., Borriello, G., Consolvo, S., Haehnel, D., Harrison, B., Hemingway, B., Hightower, J., Klasnja, P. P., Koscher, K., LaMarca, A., Landay, J. A., LeGrand, L., Lester, J., Rahimi, A., Rea, A., Wyatt, D. (2008). The mobile sensing platform: an embedded activity recognition system. IEEE Pervasive Computing, 7(2), 32-41.
47. Froehlich, J., Chen, M. Y., Consolvo, S., Harrison, B., Landay J. A. (2007). MyExperience: a system for in-situ tracing and capturing of user feedback on mobile phones. In Proceedings of the 5th international conference on Mobile systems, applications and services, MobiSys '07. (pp. 57-70), New York, NY, USA.
48. Rabbi, M., Ali, S., Choudhury, T., Berke, E. (2011). Passive and in-situ assessment of mental and physical well-being using mobile sensors. In Proceedings of the 13th international conference on Ubiquitous computing, UbiComp '11. (pp. 385-394).
49. Lane, N. D., Mohammod, M., Lin, M., Yang, X., Lu, H., Ali, S., et al. (2011). BeWell: a smartphone application to monitor, model and promote wellbeing. In 5th International ICST Conference on Pervasive Computing Technologies for Healthcare. (pp. 23-26).
50. Yang, J., Liu, Z., Lu, H., Lane, N. D., Tanzeem, C., Andrew, C. T. (2010). The Jigsaw continuous sensing engine for mobile phone applications. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems.
51. Li, I., Dey, A. K., Forlizzi, J. (2012). Using context to reveal factors that affect physical activity. ACM Transactions on Computer-Human Interaction, 19(1).
52. Bielik, P., Tomlein, M., Krátky, P., Mitrík, S., Barla, M., Bieliková, M. (2012). Move2Play: an innovative approach to encouraging people to be more physically active. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (IHI '12).
53. Buttussi, F., & Chittaro, L. (2008). MOPET: a context-aware and user-adaptive wearable system for fitness training. Artificial Intelligence in Medicine, 42(2), 153-163.
54. Bickmore, T., Mauer, D., Brown, T. (2009). Context awareness in a handheld exercise agent. Pervasive and Mobile Computing Special Issue on Pervasive Health and Wellness, 5, 226-235.
55. Bauer, J., Consolvo, S., Greenstein, B., Schooler, J., Wu, E., Watson, N. F., Kientz, J. (2012). Shuteye: Encouraging awareness of healthy sleep recommendations with a mobile, peripheral display. In Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems, CHI '12. (pp. 1401-1410).
56. Choe, E. K., Consolvo, S., Watson, N. F., Kientz, J. A. (2011). Opportunities for computing technologies to support healthy sleep behaviors. In Proceedings of the 2011 annual conference on Human factors in computing systems, CHI '11. (pp. 3053-3062).
57. Borazio, M., & van Laerhoven, K. (2012). Combining wearable and environmental sensing into an unobtrusive tool for long-term sleep studies. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, IHI '12. (pp. 71-80).
58. Sano, A., & Picard, R. (2011). Toward a taxonomy of autonomic sleep patterns with electrodermal activity. In Proceedings of IEEE Engineering in Medicine and Biology Society.
59. Sunseri, M., Liden, C., Farringdon, J., Pelletier, R., Safier, S., Stivoric, J., et al. (2012). The sensewear armband as a sleep detection device. BodyMedia SenseWear Whitepapers. Accessed June 2012.
60. Karlen, W., Mattiussi, C., Floreano, D. (2009). Sleep and wake classification with ECG and respiratory effort signals. Biomedical Circuits and Systems, IEEE Transactions on, 3(2), 71-78.
61. Setz, C., Arnrich, B., Schumm, J., La Marca, R., Troster, G., Ehlert, U. (2010). Discriminating stress from cognitive load using a wearable EDA device. IEEE Transactions on Information Technology in Biomedicine, 14(2), 410-417.
62. Healey, J. A., & Picard, R. W. (2005). Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems, 6, 156-166.
63. Hernandez, J., Morris, R. R., Picard, R. W. (2011). Call center stress recognition with person-specific models. In Proceedings of the 4th international conference on Affective computing and intelligent interaction-volume Part I, ACII'11. (pp. 125-134), Berlin: Springer-Verlag.
64. Poh, M. Z., Kim, K., Goessling, A. D., Swenson, N. C., Picard, R. W. (2009). Heartphones: sensor earphones and mobile application for non-obtrusive health monitoring. Proceedings of the 13th IEEE International Symposium on Wearable Computers. (pp. 153-154). Linz, Austria.
65. Morris, M., & Guilak, F. (2009). Mobile heart health: project highlight. IEEE Pervasive Computing, 8, 57-61.
66. Healey, L., Nachman, S., Subramanian, J. S., Morris, M. (2010). Out of the lab and into the fray: towards modeling emotion in everyday life. In P. Floreen, A. Kruger, & M. Spasojevic (Eds.), Pervasive computing, volume 6030 of Lecture Notes in Computer Science (pp. 156-173). Berlin: Springer.
67. Baker, J., Pechenizkiy, M., Sidorova, N. (2011). What's your current stress level? Detection of stress patterns from GSR sensor data. In Proc. of ICDM 2011 Workshops. IEEE Press.
68. Sun, F. T., Kuo, C., Cheng, H.-T., Buthpitiya, S., Collins, P., Griss, M. (2012). Activity-aware mental stress detection using physiological sensors. In: M. Gris, Yang, G. (Eds.), Mobile Computing, Applications, and Services, volume 76 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. (pp. 211-230).
69. Stahl, A., Höök, K., Svensson, M., Taylor, A. S., Combetto, M. (2009). Experiencing the affective diary. Personal and Ubiquitous Computing, 13(5), 365-378.
70. McDuff, D., Karlson, A., Kapoor, A., Roseway, A., Czerwinski, M. (2012). AffectAura: an intelligent system for emotional memory. In Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems, CHI '12. (pp. 849-858). New York, NY, USA.
71. Rachuri, K. K., Musolesi, M., Mascolo, C., Rentfrow, P. J., Longworth, C., Aucinas, A. (2010). EmotionSense: a mobile phones based adaptive platform for experimental social psychology research. In Proceedings of the 12th ACM international conference on Ubiquitous computing, Ubicomp '10. (pp. 281-290).
72. Matic, A., Osmani, V., Popleteev, A., Mayora-Ibarra, O. (2011). Smart phone sensing to examine effects of social interactions and non-sedentary work time on mood changes. In Proceedings of the 7th international and interdisciplinary conference on modeling and using context, CONTEXT'11, (pp. 200-213).
73. Eagle, N., & Pentland, A. (2006). Reality mining: sensing complex social systems. Personal and Ubiquitous Computing, 10(4), 255-268.
74. Do, T., Gatica-Perez, D. (2011). Human interaction discovery in smartphone proximity networks. Personal and Ubiquitous Computing, 1-19.
75. Prochaska, J. O., & Velicer, W. F. (1997). The transtheoretical model of health behavior change. American Journal of Health Promotion, 12(1), 38-48. Accessed 2009 Mar 18.
76. Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: a 35-year odyssey. American Psychologist, 57(9), 705-717.
77. Festinger, L. A. (1957). Theory of cognitive dissonance. Stanford: Stanford University Press.
78. Consolvo, S., McDonald, D. W., Landay, J. A. (2009). Theory-driven design strategies for technologies that support behavior change in everyday life. In Proceedings of the 27th international conference on Human factors in computing systems (CHI '09). New York: ACM.
79. Li, I., Dey, A., Forlizzi, J. (2010). A stage-based model of personal informatics systems. In Proceedings of the 28th international conference on Human factors in computing systems (CHI '10). (pp. 557-566) New York: ACM.
80. Cialdini, R. B. (2001). The science of persuasion, Scientific American-American Edition, vol 284, part 2, pp. 62-67.
81. Fogg, B. J. (2003). Persuasive technology: using computers to change what we think and do. Burlington: Morgan Kaufmann.
82. Pavel, D., Callaghan, V., Dey, A. K. (2011) From self-monitoring to self-understanding: going beyond physiological sensing for supporting wellbeing. International Workshop on Pervasive Computing Paradigms for Mental Health (MindCare 2011), Pervasive Health 2011 conference, Dublin, May 2011.
83. Scholtz, J., & Consolvo, S. (2004) Towards a discipline for evaluating ubiquitous computing applications. National Institute of Standards and Technology.
84. Fensli, R., Pedersen, P. E., Gundersen, T., Hejlesen, O. K. (2008). Sensor acceptance model: measuring patient acceptance of wearable sensors. Methods of Information in Medicine, 47(1), 89-95.
85. Workshop on Evaluating Off-the-Shelf Technologies for Personal Health Monitoring. In UbiComp 2012. http://www. ubicomp. org/ubicomp2012/calls/workshopsCFP. html#PerHealth. Accessed March 2013.
86. Kientz, J. A., Choe, E. K., Birch, B., Maharaj, R., Fonville, A., Glasson, C., Mundt, J. (2010). Heuristic evaluation of persuasive health technologies. In Proceedings of the 1st ACM International Health Informatics Symposium (IHI '10), T. Veinot (Ed.). ACM, New York, NY, USA, 555-564.
87. Cooper, A. (2007). About face 3: the essentials of interaction design. New York: Wiley (with Robert Reimann and David Cronin). ISBN 0-4700-8411-1.
88. Raij, A., Ghosh, A., Kumar, S., Srivastava, M. (2011). Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment. In Proceedings of the 2011 annual conference on Human factors in computing systems (CHI '11). New York: ACM.
89. Lane, N. D., Xu, Y., Lu, H., Hu, S., Choudhury, T., Campbell, A., et al. (2011). Enabling large-scale human activity inference on smartphones using community similarity networks. Proceedings of Ubicomp. Beijing, China.
90. Kapadia, A., Kotz, D., Triandopoulos, N. (2009). Opportunistic sensing: security challenges for the new paradigm. In Proceedings of the First international conference on COMmunication Systems And NETworks (COMSNETS'09). (pp. 127-136). Piscataway: IEEE Press.
91. Roggen, D., Förster, K., Calatroni, A., Holleczek, T., Fang, Y., Tröster, G., et al. (2009). Opportunity: towards opportunistic activity and context recognition systems. Third IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications.
92. Tollmar, K., Bentley, F., Viedma, C. (2012). Mobile health mashups: making sense of multiple streams of wellbeing and contextual data for presentation on a mobile device. Pervasive Health.
93. Farrell, R., Danis, C., Ramakrishnan, S., Kellogg, W. (2012). Intrapersonal retrospective recommendation: lifestyle change recommendations using stable patterns of personal behavior. Lifestyle Workshop in RecSys.
94. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.