Identifying typical physical activity on smartphone with varying positions and orientations

BioMedical Engineering Online

Volumn 14, Issue 1, 2015, Pages

  Miao, Fen ^a,b   He, Yi ^c   Liu, Jinlei ^a,d   Li, Ye ^a   Ayoola, Idowu ^e  

^a SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^d GUANGDONG UNIVERSITY OF TECHNOLOGY   (China)

^e EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; OPTIMIZATION; SMARTPHONES;

ACTIVITY CLASSIFICATIONS; ACTIVITY RECOGNITION; BUILT-IN SENSORS; J48 CLASSIFIERS; ON-LINE CLASSIFIER; PHYSICAL ACTIVITY; RECOGNITION ACCURACY; SEQUENTIAL MINIMAL OPTIMIZATION;

SIGNAL ENCODING;

ACCELEROMETER; ADULT; ARTICLE; BAYESIAN LEARNING; BIOSENSOR; CONTROLLED STUDY; DAILY LIFE ACTIVITY; FEASIBILITY STUDY; FEMALE; GYROSCOPE; HUMAN; HUMAN EXPERIMENT; LIGHT SENSOR; MAGNETIC SENSOR; MALE; MEASUREMENT ACCURACY; MEDICAL DEVICE; MOBILE PHONE; NORMAL HUMAN; ORIENTATION; PHYSICAL ACTIVITY; PRIORITY JOURNAL; PROCESS OPTIMIZATION; RECOGNITION; RUNNING; SEQUENTIAL MINIMAL OPTIMIZATION; TASK PERFORMANCE; WALKING; ACCELERATION; ACTIMETRY; BODY MASS; BODY POSTURE; CLOTHING; COMPARATIVE STUDY; DECISION TREE; DEVICES; GRAVITY; LIGHT; MAGNETISM; MOTOR ACTIVITY; PROCEDURES; REFERENCE VALUE; ROTATION; SMARTPHONE;

ACCELERATION; ACTIGRAPHY; ADULT; BODY MASS INDEX; CLOTHING; DECISION TREES; FEMALE; GRAVITATION; HUMANS; LIGHT; MAGNETICS; MALE; MOTOR ACTIVITY; POSTURE; REFERENCE VALUES; ROTATION; RUNNING; SMARTPHONE; WALKING;

EID: 84928340569     PISSN: None     EISSN: 1475925X     Source Type: Journal    
DOI: 10.1186/s12938-015-0026-4     Document Type: Article

References (34)

1. Blair SN. Physical inactivity: the biggest public health problem of the 21st century. Br J Sports Med. 2009;43:1-2.
2. Pitta F, Takaki MY, Oliveira NHd, Sant'Anna TJ, Fontana AD, Kovelis D, et al. Relationship between pulmonary function and physical activity in daily life in patients with COPD. Respir Med. 2008;102:1203-7.
3. Steele RM, Van Sluijs E, Sharp SJ, Landsbaugh JR, Ekelund U, Griffin SJ. An investigation of patterns of children's sedentary and vigorous physical activity throughout the week. Int J Behav Nutr Phys Act. 2010;7:88.
4. Preece SJ, Goulermas JY, Kenney LPJ, Howard D, Meijer K, Crompton R. Activity identification using body-mounted sensors-a review of classification techniques. Physiol Meas. 2009;30:R1-33.
5. Lotan M. Quality physical intervention activity for persons with Down syndrome. Sci World J. 2007;7:7-19.
6. Nelson T, Megens C, Peeters M. Bouncers: a design exploration into sharing and triggering personal activity amongst friends. Pers Technol. 2012;2012:33-6.
7. Ben-Arie J, Wang Z, Pandit P, Rajaram S. Human activity recognition using multidimensional indexing. IEEE Trans Pattern Anal Mach Intell. 2002;24:1091-104.
8. Maglogiannis I, Doukas C. Emergency fall incidents detection in assisted living environments utilizing motion sound and visual perceptual components. IEEE Trans Pattern Anal Mach Intell. 2011;15:277-89.
9. Gyllensten I, Bonomi AG. Identifying types of physical activity with a single accelerometer: evaluating laboratory-trained algorithms in daily life. IEEE Trans Biomed Eng. 2011;58:2656-63.
10. Karantonis DM, Narayanan MR, Mathie M, Lovell NH, Celler BG. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans Inf Technol Biomed. 2006;10:156-7.
11. Ermes M, Parkka J, Mantyjarvi J, Korhonen I. Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans Inf Technol Biomed. 2008;12:20-6.
12. Khan AM, Lee YK, Lee SY, Kim TS. A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans Inf Technol Biomed. 2010;14:1166-72.
13. Nishiguchi S, Yamada M, Nagai K, Mori S, Kajiwara Y, Sonoda T, et al. Reliability and validity of gait analysis by android-based Smartphone. Telemedicine and e-Health. 2012;18:292-6.
14. Cho J, Kim J, Kim T. Smart phone-based human activity classification and energy expenditure generation in building environments. Proceedings of the 7th International Symposium on Sustainable Healthy Buildings. 2012;2012:97-105.
15. Anjum A, Muhammad Usman I. Activity recognition using smartphone sensors. IEEE conference on Consumer Communications and Networking. 2013. p. 914-9.
16. Arif M, Bilal M, Kattan A, Ahamed SI. Better physical activity classification using Smartphone acceleration sensor. J Med Syst. 2014;38:1-10.
17. Guidoux R, Duclos M, Fleury G, Lacomme P, Lamaudièr N, Manenq P, et al. A smartphone-driven methodology for estimating physical activities and energy expenditure in free living conditions. J Biomed Inform. 2014;52:271-8.
18. Lee Y-S, Cho S-B. Activity recognition with android phone using mixture-of-experts co-trained with labeled and unlabeled data. Neurocomputing. 2014;126:106-15.
19. Yongjin K, Kyuchang K, Changseok B. Unsupervised learning for human activity recognition using smartphone sensors. Expert Systems with Applications. 2014;41:6067-74.
20. Bhattacharya S, Nurmi P, Hammerl N, Plötz T. Using unlabeled data in a sparse-coding framework for human activity recognition. Pervasive Mob Comput. 2014;15:242-62.
21. He Y, Li Y, Bao S-D. Fall Detection by built-in tri-accelerometer of smartphone. IEEE-EMBS International Conference on Biomedical and Health Informatics. 2012;2012:184-7.
22. He Y, Li Y. Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone. Int J Distrib Sens Netw. 2013. 2013. Article ID: 481580.
23. Sun L, Zhang D, Li B, Guo B, Li S. Activity recognition on an accelerometer embedded mobile phone with varying positions and orientations. Ubiquitous Intelligence and Computing. 2010;6406:548-62.
24. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter. 2009;11:10-8.
25. Pantelopoulos A, Bourbakis NG. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man Cybern Part C Appl Rev. 2010;40:1-12.
26. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence. 1995;14:1137-45.
27. Theodoridis S, Pikrakis A, Koutroumbas K, Cavouras D. Introduction to Pattern Recognition, A Matlab Approach: A Matlab Approach. Greece: Academic; 2010.
28. Platt J. Sequential minimal optimization: A fast algorithm for training support vector machines. 1998.
29. Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P. An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Appl Physiol. 2009;107:1300-7.
30. Vathsangam H, Emken A, Spruijt-Metz D, Schroeder E, Sukhatme G. Determining energy expenditure from treadmill walking using Hip-worn inertial sensors: an experimental study. IEEE Trans Biomed Eng. 2011;58:2804-15.
31. Parkka J, Ermes M, Korpipaa P, Mantyjarvi J, Peltola J, Korhonen I. Activity classification using realistic data from wearable sensors. IEEE Trans Inf Technol Biomed. 2006;10:119-28.
32. He C, Fan X, Li Y. Toward ubiquitous healthcare services with a novel efficient cloud platform. IEEE Trans Biomed Eng. 2013;60:230-4.
33. Miao F, Bao S-D, Li Y. Biometric key distribution solution with energy distribution information of physiological signals for body sensor network security. IET Inf Secur. 2013;7:87-96.
34. Drosou A, Ioannidis D, Moustakas K, Tzovaras D. Unobtrusive behavioral and activity-related multimodal biometrics: the ACTIBIO authentication concept. Sci World J. 2011;11:503-19.