Energy-efficient motion related activity recognition on mobile devices for pervasive healthcare

Mobile Networks and Applications

Volumn 19, Issue 3, 2014, Pages 303-317

  Liang, Yunji ^a   Zhou, Xingshe ^a   Yu, Zhiwen ^a   Guo, Bin ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

Activity recognition; Energy efficient; Healthcare; Mobile devices; Tri axial accelerometer

Indexed keywords

ACCELEROMETERS; ENERGY EFFICIENCY; HEALTH CARE; MOBILE DEVICES;

ACTIVITY RECOGNITION; ENERGY EFFICIENT; HIERARCHICAL RECOGNITION; PERVASIVE HEALTHCARE; RECOGNITION ACCURACY; RECOGNITION ALGORITHM; SAMPLING FREQUENCIES; TRIAXIAL ACCELEROMETER;

PATTERN RECOGNITION;

EID: 84904159626     PISSN: 1383469X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11036-013-0448-9     Document Type: Article

References (44)

1. Mihailidis A, Boger JN, Craig T, Hoey J (2008) The COACH prompting system to assist older adults with dementia through handwashing: an efficacy study. BMC Geriatr 8(28):1-18
2. Tang L, Zhou X, Zhi Y, Liang Y, Zhang D, Ni H (2011) MHS: a multimedia system for improving medication adherence in elderly care. IEEE Syst J 5(4):506-617
3. Chen J, Chi P, Chu H, Chen C, Huang P (2010) A smart kitchen for nutrition-aware cooking. IEEE Pervasive Comput 9(4):58-65
4. Ni H, Abdulrazak B, Zhang D, Wu S, Yu Z, Zhou X, Wang S (2012) Towards non-intrusive sleep pattern recognition in elder assistive environment. J Ambient Intell Human Comput 3(2):167-175
5. Kawahara Y, Ryu N, Asami T (2009) Monitoring daily energy expenditure using a 3-axis accelerometer with a low-power microprocessor. Int J Hum Comput Interact 1:145-154
6. Ryu N, Kawahara Y, Asami T (2008) A calorie count application for a mobile phone based on METS value. In: Proceedings of 5th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks, San Francisco, pp 583-584
7. Rothney MP, Neumann M, Beziat A, Chen KY (2007) An artificial neural network model of energy expenditure using nonintegrated acceleration signals. J Appl Physiol 103:1419-1427 (Pubitemid 47605982)
8. Sánchez D, Tentori M, Favela J (2008) Activity recognition for the smart hospital. IEEE Intell Syst 23(2):50-57 (Pubitemid 351459539)
9. Bouten C, Koekkoek K, Verduin M, Kodde R, Janssen JD (1997) A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Trans Biomed Eng 44:136-147 (Pubitemid 27084199)
10. Khan AM, Lee Y, Lee SY, Kim T (2010) A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans Inf Technol Biomed 14:1166-1172
11. Kwapisz JR, Weiss GM, Moore SA (2010) Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter 12
12. Maurer U, Smailagic A, Siewiorek DP, Deisher M (2006) Activity recognition and monitoring using multiple sensors on different body positions. In: Proceedings of international workshop on wearable and implantable body sensor networks, Cambridge, pp 113-116 (Pubitemid 44711293)
13. Krahnstoever N, Rittscher J, Tu P, Chen K, Tomlinson T (2005) Activity recognition using visual tracking and RFID. In: Proceedings of the 7th IEEE workshop on applications of computer vision, vol 1, pp 494-500
14. Inomata T, Naya F, Kuwahara N, Hattori F, Koqure K (2009) Activity recognition from interactions with objects using dynamic bayesian network. In: Proceedings of 3rd ACM international workshop on context-awareness for self-managing systems, Nara, pp 39-42
15. Patterson DJ, Fox D, Kautz H, Philipose M (2005) Fine grained activity recognition by aggregating abstract object usage. In: Proceedings of the 9th international symposium on wearable computers, Osaka, pp 44-51 (Pubitemid 44867493)
16. Wu J, Osuntogun A, Choudhury T, Philipose M, Rehg JM (2007) A scalable approach to activity recognition based on object use. In: Proceedings of the 11th international conference on computer vision, Rio de Janeiro, pp 1-8
17. Bouchard K, Bouchard B, Bouzouane A (2011) Qualitative spatial activity recognition using a complete platform based on passive RFID tags: experiments and results. In: Proceedings of ICOST 2011, Montreal, pp 308-312
18. Yang J, Lee J, Choi J (2011) Activity recognition based on RFID object usage for smart mobile devices. J Comput Sci Technol 26:239-246
19. Chen L, Nugent CD, Cook D, Yu Z (2011) Knowledge-driven activity recognition in intelligent environment. Pervasive Mob Comput 7:285-286
20. Mizuno H, Nagai H, Sasaki K, Hosaka H, Sugimoto C, Khalil K, Tatsuta S (2007) Wearable sensor system for human behavior recognition. In: Proceedings of 4th international conference on solid-state sensors, actuators and microsystems, Lyon, pp 435-438
21. Cho Y, Nam Y, Choi Y, Cho W (2008) SmartBuckle: human activity recognition using a 3-axis accelerometer and a wearable camera. In: Proceedings of HealthNet'08, Brechenridge
22. Lukowicz P, Ward JA, Junker H, et al (2004) Recognizing workshop activity using body worn microphone and accelerometers. Pervasive Comput Lect Notes Comput Sci 3001:18-32
23. Ward JA, Lukowicz P, Gerhard T, Starner TE (2006) Activtiy recogntion of assembly tasks using body-worn microphones and accelerometers. IEEE Trans Pattern Anal Mach Intell 28:1553-1566 (Pubitemid 46405077)
24. Inooka H, Ohtaki Y, Hayasaka H, Suzuki A, Naqatomi R (2006) Development of advanced portable device for daily physical assessment. In: Proceedings of SICE-ICASE international joint conference, Busan, pp 5878-5881 (Pubitemid 46950089)
25. Choudhury T, Consolvo S, Harrison B, Consolvo S, Haehnel D, et al (2008) The mobile sensing platform: an embedded activity recognition system. IEEE Pervasive Comput 7:32-41 (Pubitemid 351575120)
26. Ribeiro PC, Santos-Victor J (2006) Human activity recognition from video: modeling, feature selection and classification architecture. In: Proceedings of international workshop on human activity recognition and modeling, Oxford, pp 1175-1178
27. Gupta S, Mooney RJ (2010) Using closed captions as supervision for video activity recognition. In: Proceedings of national conference on artificial intelligence, Atlanta, pp 1083-1088
28. Fusier F, Valentin V, Bremond F, Thonnat M, Borg M, Thirde D, Ferryman J (2007) Video understanding for complex activity recognition. Mach Vis Appl 18:167-188 (Pubitemid 46870861)
29. Györbíró N, Fábían A, Hományi G (2009) An activity recognition system for mobile phones. Mob Netw Appl 14:82-91
30. Kern N, Schiele B, Schmidt A (2003) Multi-sensor activity context detection for wearable computing. Ambient Intell Lect Notes Comput Sci 2875:220-232 (Pubitemid 137631976)
31. Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. Pervasive Comput Lect Notes Comput Sci 3001:1-17
32. Mannini A, Sabatini AM (2010) Machine learning methods for classifying human physical activity from on-body accelerometers. Sensor 10:1154-1175
33. Krishnan NC, Juillard C, Colbry D (2009) Recognition of hand movements using wearable accelerometers. J Ambient Intell Smart Environ 1:143-155
34. Ruch N, Rumo M, Mader U (2011) Recognition of activities in children by two uniaxial accelerometers in free-living conditions. Eur J Appl Physiol 111:1917-1927
35. He Z, Liu Z, Jin L, Zhen L, Huang J (2008) Weightlessness feature - a novel feature for single tri-axial accelerometer based activity recognition. In: Proceedings of 19th international conference on pattern recognition, Tampa, pp 1-4
36. Ravi N, Dander N, Mysore P, Littman ML (2005) Activity recognition from accelerometer data. In: Proceedings of the 20th national conference on artificial intelligence and the 17th innovative applications of artificial intelligence conference, pp 1541-1546 (Pubitemid 43006650)
37. Mathie MJ, Celler BG, Lovell NH, Coster ACF (2004) Classification of basic daily movements using a triaxial accelerometer. Med Biol Eng Comput 42:679-687 (Pubitemid 39386950)
38. Krassing G, Tantinger D, Hofmann C, Wittenberg T, Struck M (2010) User-friendly system for recognition of activities with an accelerometer. In: Proceedings of 4th international conference on pervasive computing technologies for healthcare, Erlangen, pp 1-8
39. Lee M, Khan AM, Kim J, Cho Y, Kim T (2010) A single tri-axial accelerometer-based real-time personal life log system capable of activity classification and exercise information generation. In: Proceedings of 2010 annual international conference of the IEEE engineering in medicine and biology society, pp 1390-1393
40. Wang Y, Lin J, Annavaram M, Quinn JA, Jason H, Bhaskar K, Sadeh N (2009) A framework of energy efficient mobile sensing for automatic user state recognition. In: Proceedings of the 7th ACM international conference on mobile systems, applications, and services, New York, pp 179-192
41. Zappi P, Lombriser C, Stiefmeier T, Farella E, Roggen D, Benini L, Troster G (2008) Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection. Wirel Sens Netw Lect Notes Comput Sci 4943:17-33
42. Li X, Cao H, Chen E, Tian J (2012) Learning to infer the status of heavy-duty senors for energy efficient context-sensing. ACM Trans Intell Syst Technol 3(2):1-23
43. Lane ND, Xu Y, Lu H, Hu S, Choudhury T, Campbell AT, Zhao F (2011) Enabling large-scale human activity inference on Smartphones using Community Similarity Networks (CSN). In: Proceedings of the 13th internation conference on ubiquitous computing, Beijing, pp 355-364
44. Yan Z, Subbaraju V, Chakraborty D, Misra A, Aberer K (2012) Energy-efficient continuous activity recognition on mobile phones, an activity-adaptive approach. In: Proceedings of the 16th internation symposium on wearable computers, Newcastle, pp 17-24