메뉴 건너뛰기




Volumn 13, Issue 1, 2017, Pages

Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models

Author keywords

accelerometer; continuous hidden Markov model; gyroscope; hierarchical classifier; Human activity recognition; sensors; smartphone

Indexed keywords

ACCELEROMETERS; GYROSCOPES; MARKOV PROCESSES; PATTERN RECOGNITION; SENSORS; SIGNAL ENCODING; SMARTPHONES; TRELLIS CODES;

EID: 85011854067     PISSN: 15501329     EISSN: 15501477     Source Type: Journal    
DOI: 10.1177/1550147716683687     Document Type: Article
Times cited : (86)

References (41)
  • 1
    • 84881311778 scopus 로고    scopus 로고
    • A survey on human activity recognition using wearable sensors
    • Lara OD and Labrador MA. A survey on human activity recognition using wearable sensors. IEEE Commun Surveys Tutor 2013; 15(3): 1192-1209.
    • (2013) IEEE Commun Surveys Tutor , vol.15 , Issue.3 , pp. 1192-1209
    • Lara, O.D.1    Labrador, M.A.2
  • 2
    • 84867854576 scopus 로고    scopus 로고
    • Sensor-based activity recognition
    • Chen L, Hoey J, Nugent C, et al. Sensor-based activity recognition. IEEE T Syst Man Cy C 2012; 42(6): 790-808.
    • (2012) IEEE T Syst Man Cy C , vol.42 , Issue.6 , pp. 790-808
    • Chen, L.1    Hoey, J.2    Nugent, C.3
  • 3
    • 84893936376 scopus 로고    scopus 로고
    • A tutorial on human activity recognition using body-worn inertial sensors
    • Bulling A, Blanke U and Schiele B. A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv 2014; 46(3): 1-33.
    • (2014) ACM Comput Surv , vol.46 , Issue.3 , pp. 1-33
    • Bulling, A.1    Blanke, U.2    Schiele, B.3
  • 4
    • 84885617306 scopus 로고    scopus 로고
    • Recognizing physical activities using Wii remote
    • Khan AM. Recognizing physical activities using Wii remote. Int J Inf Educ Technol 2013; 3(1): 60-62.
    • (2013) Int J Inf Educ Technol , vol.3 , Issue.1 , pp. 60-62
    • Khan, A.M.1
  • 6
    • 84875153186 scopus 로고    scopus 로고
    • Human behavior cognition using smartphone sensors
    • Pei L, Guinness R, Chen R, et al. Human behavior cognition using smartphone sensors. Sensors 2013; 13(2): 1402-1424.
    • (2013) Sensors , vol.13 , Issue.2 , pp. 1402-1424
    • Pei, L.1    Guinness, R.2    Chen, R.3
  • 9
    • 77953617522 scopus 로고    scopus 로고
    • Comparative study on classifying human activities with miniature inertial and magnetic sensors
    • Altun K, Barshan B and Tuncxel O. Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recogn 2010; 43(10): 3605-3620.
    • (2010) Pattern Recogn , vol.43 , Issue.10 , pp. 3605-3620
    • Altun, K.1    Barshan, B.2    Tuncxel, O.3
  • 10
    • 84991669602 scopus 로고    scopus 로고
    • Investigating inter-subject and inter-activity variations in activity recognition using wearable motion sensors
    • Barshan B and Yurtman A. Investigating inter-subject and inter-activity variations in activity recognition using wearable motion sensors. Comput J 2016; 59(9): 1345-1362.
    • (2016) Comput J , vol.59 , Issue.9 , pp. 1345-1362
    • Barshan, B.1    Yurtman, A.2
  • 14
    • 84900795161 scopus 로고    scopus 로고
    • Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems
    • Gao L, Bourke AK and Nelson J. Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. Med Eng Phys 2014; 36(6): 779-785.
    • (2014) Med Eng Phys , vol.36 , Issue.6 , pp. 779-785
    • Gao, L.1    Bourke, A.K.2    Nelson, J.3
  • 15
    • 84891471224 scopus 로고    scopus 로고
    • Optimal placement of accelerometers for the detection of everyday activities
    • Cleland I, Kikhia B, Nugent C, et al. Optimal placement of accelerometers for the detection of everyday activities. Sensors 2013; 13: 9183-9200.
    • (2013) Sensors , vol.13 , pp. 9183-9200
    • Cleland, I.1    Kikhia, B.2    Nugent, C.3
  • 16
    • 30744462795 scopus 로고    scopus 로고
    • Activity classification using realistic data from wearable sensors
    • Parkka J, Ermes M, Korpipaa P, et al. Activity classification using realistic data from wearable sensors. IEEE T Inf Technol B 2006; 10(1): 119-128.
    • (2006) IEEE T Inf Technol B , vol.10 , Issue.1 , pp. 119-128
    • Parkka, J.1    Ermes, M.2    Korpipaa, P.3
  • 17
  • 19
    • 84901278500 scopus 로고    scopus 로고
    • Feature selection and activity recognition system using a single triaxial accelerometer
    • Gupta P and Dallas T. Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Trans Biomed Eng 2014; 61: 1780-1786.
    • (2014) IEEE Trans Biomed Eng , vol.61 , pp. 1780-1786
    • Gupta, P.1    Dallas, T.2
  • 22
    • 72049112876 scopus 로고    scopus 로고
    • Toward physical activity diary: Motion recognition using simple acceleration features with mobile phones
    • Beijing, China, 19-24 October, New York: ACM
    • Yang J. Toward physical activity diary: motion recognition using simple acceleration features with mobile phones. In: Proceedings of the 1st international workshop on interactive multimedia for consumer electronics, Beijing, China, 19-24 October 2009, pp.1-10. New York: ACM.
    • (2009) Proceedings of the 1st International Workshop on Interactive Multimedia for Consumer Electronics , pp. 1-10
    • Yang, J.1
  • 23
    • 80054964826 scopus 로고    scopus 로고
    • Activity recognition using cell phone accelerometers
    • Kwapisz J, Weiss G and Moore S. Activity recognition using cell phone accelerometers. ACM SIGKDD Explor Newsl 2010; 12(2): 74-82.
    • (2010) ACM SIGKDD Explor Newsl , vol.12 , Issue.2 , pp. 74-82
    • Kwapisz, J.1    Weiss, G.2    Moore, S.3
  • 24
    • 0024610919 scopus 로고
    • A tutorial on hidden Markov models and selected applications in speech recognition
    • Rabiner LR. A tutorial on hidden Markov models and selected applications in speech recognition. P IEEE 1989; 77(2): 257-286.
    • (1989) P IEEE , vol.77 , Issue.2 , pp. 257-286
    • Rabiner, L.R.1
  • 25
    • 35748978234 scopus 로고    scopus 로고
    • Empirical characterization of random forest variable importance measures
    • Archer K and Kimes R. Empirical characterization of random forest variable importance measures. Comput Stat Data An 2008; 52(4): 2249-2260.
    • (2008) Comput Stat Data An , vol.52 , Issue.4 , pp. 2249-2260
    • Archer, K.1    Kimes, R.2
  • 27
    • 84892568856 scopus 로고    scopus 로고
    • An unsupervised approach for automatic activity recognition based on hidden Markov model regression
    • Travelsi D, Mohammed S, Chamroukhi F, et al. An unsupervised approach for automatic activity recognition based on hidden Markov model regression. IEEE Trans Autom Sci Eng 2013; 10(3): 829-835.
    • (2013) IEEE Trans Autom Sci Eng , vol.10 , Issue.3 , pp. 829-835
    • Travelsi, D.1    Mohammed, S.2    Chamroukhi, F.3
  • 28
    • 77950247206 scopus 로고    scopus 로고
    • Machine learning methods for classifying human physical activity from on-body accelerometers
    • Mannini A and Sabatini A. Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 2010; 10: 1154-1175.
    • (2010) Sensors , vol.10 , pp. 1154-1175
    • Mannini, A.1    Sabatini, A.2
  • 29
    • 77956366233 scopus 로고    scopus 로고
    • A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer
    • Khan AM, Lee Y-K, Lee S-Y, et al. A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE T Inf Technol B 2010; 14(5): 1166-1172.
    • (2010) IEEE T Inf Technol B , vol.14 , Issue.5 , pp. 1166-1172
    • Khan, A.M.1    Lee, Y.-K.2    Lee, S.-Y.3
  • 30
    • 85037974473 scopus 로고    scopus 로고
    • Activity recognition using hierarchical hidden Markov models on a smartphone with 3D accelerometer
    • Wroclaw, 23-25 May, Berlin: Springer
    • Lee Y-S and Cho S-B. Activity recognition using hierarchical hidden Markov models on a smartphone with 3D accelerometer. In: Proceedings of the international conference on hybrid intelligent systems, Wroclaw, 23-25 May 2011, pp.460-467. Berlin: Springer.
    • (2011) Proceedings of the International Conference on Hybrid Intelligent Systems , pp. 460-467
    • Lee, Y.-S.1    Cho, S.-B.2
  • 33
    • 84868157006 scopus 로고    scopus 로고
    • Twostage hidden Markov model in gesture recognition for human robot interaction
    • Nguyen-Duc-Thanh N, Lee S-Y and Kim D-H. Twostage hidden Markov model in gesture recognition for human robot interaction. Int J Adv Robot Syst 2012; 9(39): 1-10.
    • (2012) Int J Adv Robot Syst , vol.9 , Issue.39 , pp. 1-10
    • Nguyen-Duc-Thanh, N.1    Lee, S.-Y.2    Kim, D.-H.3
  • 34
    • 84926615131 scopus 로고    scopus 로고
    • Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models
    • Xiamen, China, 19-21 August, New York: IEEE
    • Ronao C and Cho S-B. Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models. In: Proceedings of the international conference on natural computation, Xiamen, China, 19-21 August 2014, pp.681-686. New York: IEEE.
    • (2014) Proceedings of the International Conference on Natural Computation , pp. 681-686
    • Ronao, C.1    Cho, S.-B.2
  • 35
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman L. Random forests. Mach Learn 2001; 45(1): 5-32.
    • (2001) Mach Learn , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 37
    • 84866537883 scopus 로고    scopus 로고
    • Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach
    • Newcastle upon Tyne, 18-22 June, New York: IEEE
    • Yan Z, Subbaraju V, Chakraborty D, et al. Energy-efficient continuous activity recognition on mobile phones: an activity-adaptive approach. In: Proceedings of the international symposium on wearable computers, Newcastle upon Tyne, 18-22 June 2012, pp.17-24. New York: IEEE.
    • (2012) Proceedings of the International Symposium on Wearable Computers , pp. 17-24
    • Yan, Z.1    Subbaraju, V.2    Chakraborty, D.3
  • 41
    • 84879491205 scopus 로고    scopus 로고
    • USC-HAD: A daily activity dataset for ubiquitous activity recognition using wearable sensors
    • Pittsburgh, PA, 5-8 September, New York: ACM
    • Zhang M and Sawchuk AA. USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In: Proceedings of the ACM international conference on ubiquitous computing (UbiComp), Pittsburgh, PA, 5-8 September 2012, pp.1036-1043. New York: ACM.
    • (2012) Proceedings of the ACM International Conference on Ubiquitous Computing (UbiComp) , pp. 1036-1043
    • Zhang, M.1    Sawchuk, A.A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.