M. Chan, D. Estève, J. Fourniols, C. Escriba, and E. Campo, Smart wearable systems: Current status and future challenges, Artificial Intelligence in Medicine, vol.56, issue.3, pp.137-156, 2012.
DOI : 10.1016/j.artmed.2012.09.003

X. Teng, Y. Zhang, C. C. Poon, and P. Bonato, Wearable Medical Systems for p-Health, IEEE Reviews in Biomedical Engineering, vol.1, pp.62-74, 2008.
DOI : 10.1109/RBME.2008.2008248

S. J. Preece, J. Y. Goulermas, L. P. Kenney, D. Howard, K. Meijer et al., Activity identification using body-mounted sensors?A review of classification techniques A review of wearable sensors and systems with application in rehabilitation, Physiol. Meas. J. Neuroeng. Rehabil, vol.30, issue.2012, p.9, 2009.

K. Altun, B. Barshan, O. Tunçel, J. M. Keller, D. T. Anderson et al., Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognit A system for change detection and human recognition in voxel space using the Microsoft Kinect sensor, Proceedings of the Applied Imagery Pattern Recognition Workshop (AIPR), pp.3605-3620, 2010.

F. Chamroukhi, S. Mohammed, D. Trabelsi, L. Oukhellou, and Y. Amirat, Joint segmentation of multivariate time series with hidden process regression for human activity recognition, Neurocomputing, vol.120, pp.633-644, 2013.
DOI : 10.1016/j.neucom.2013.04.003

URL : https://hal.archives-ouvertes.fr/hal-00864854

L. Gao, A. Bourke, and J. Nelson, Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems Elderly activities recognition and classification for applications in assisted living, Med. Eng. Phys, vol.2014, issue.40, pp.779-785

K. Altun and B. Barshan, Human Activity Recognition Using Inertial/Magnetic Sensor Units, In Human Behavior Understanding, vol.43, issue.10, pp.38-51, 2010.
DOI : 10.1016/j.patcog.2010.04.019

E. T. Mcadams, C. Gehin, N. Noury, C. Ramon, R. Nocua et al., Biomedical Sensors for Ambient Assisted Living, Advances in Biomedical Sensing, pp.240-262, 2010.
DOI : 10.1007/978-3-642-05167-8_14

I. Cleland, B. Kikhia, C. Nugent, A. Boytsov, J. Hallberg et al., Optimal Placement of Accelerometers for the Detection of Everyday Activities, Sensors, vol.13, issue.7, pp.9183-9200, 2013.
DOI : 10.3390/s130709183

B. Najafi, K. Aminian, A. Paraschiv-ionescu, F. Loew, C. J. Bula et al., Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly, IEEE Transactions on Biomedical Engineering, vol.50, issue.6, pp.711-723, 2003.
DOI : 10.1109/TBME.2003.812189

A. G. Bonomi, A. Goris, B. Yin, and K. R. Westerterp, Detection of Type, Duration, and Intensity of Physical Activity Using an Accelerometer, Medicine & Science in Sports & Exercise, vol.41, issue.9, pp.1770-1777, 2009.
DOI : 10.1249/MSS.0b013e3181a24536

D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell, and B. G. Celler, Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring, IEEE Transactions on Information Technology in Biomedicine, vol.10, issue.1, pp.156-167, 2006.
DOI : 10.1109/TITB.2005.856864

C. Yang and Y. Hsu, A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring, Sensors, vol.10, issue.8, pp.7772-7788, 2010.
DOI : 10.3390/s100807772

J. Parkka, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola et al., Activity Classification Using Realistic Data From Wearable Sensors, IEEE Transactions on Information Technology in Biomedicine, vol.10, issue.1, pp.119-128, 2006.
DOI : 10.1109/TITB.2005.856863

M. Mathie, B. G. Celler, N. H. Lovell, and A. Coster, Classification of basic daily movements using a triaxial accelerometer, Medical & Biological Engineering & Computing, vol.22, issue.5, pp.679-687, 2004.
DOI : 10.1007/BF02347551

W. Yeoh, I. Pek, Y. Yong, X. Chen, and A. B. Waluyo, Ambulatory monitoring of human posture and walking speed using wearable accelerometer sensors, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.20-25, 2008.
DOI : 10.1109/IEMBS.2008.4650382

J. Yang, J. Wang, and Y. Chen, Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers, Pattern Recognition Letters, vol.29, issue.16, pp.2213-2220, 2008.
DOI : 10.1016/j.patrec.2008.08.002

S. Pirttikangas, K. Fujinami, and T. Nakajima, Feature Selection and Activity Recognition from Wearable Sensors, Ubiquitous Computing Systems, pp.516-527, 2006.
DOI : 10.1007/11890348_39

D. O. Olgu?n and A. S. Pentland, Human activity recognition: Accuracy across common locations for wearable sensors, Proceedings of 2006 10th IEEE International Symposium on Wearable Computers, pp.11-14, 2006.

G. Lyons, K. Culhane, D. Hilton, P. Grace, and D. Lyons, A description of an accelerometer-based mobility monitoring technique, Medical Engineering & Physics, vol.27, issue.6, pp.497-504, 2005.
DOI : 10.1016/j.medengphy.2004.11.006

A. Salarian, H. Russmann, F. J. Vingerhoets, P. R. Burkhard, and K. Aminian, Ambulatory Monitoring of Physical Activities in Patients With Parkinson's Disease, IEEE Transactions on Biomedical Engineering, vol.54, issue.12, pp.2296-2299, 2007.
DOI : 10.1109/TBME.2007.896591

H. Gjoreski, M. Lustrek, and M. Gams, Accelerometer Placement for Posture Recognition and Fall Detection, 2011 Seventh International Conference on Intelligent Environments, pp.25-28, 2011.
DOI : 10.1109/IE.2011.11

A. Bayat, M. Pomplun, and D. A. Tran, A Study on Human Activity Recognition Using Accelerometer Data from Smartphones, Procedia Computer Science, vol.34, pp.450-457, 2014.
DOI : 10.1016/j.procs.2014.07.009

A. Moncada-torres, K. Leuenberger, R. Gonzenbach, A. Luft, and R. Gassert, Activity classification based on inertial and barometric pressure sensors at different anatomical locations, Physiological Measurement, vol.35, issue.7, p.35, 2014.
DOI : 10.1088/0967-3334/35/7/1245

P. Gupta and T. Dallas, Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer, IEEE Transactions on Biomedical Engineering, vol.61, issue.6, pp.1780-1786, 2014.
DOI : 10.1109/TBME.2014.2307069

E. Garcia-ceja, R. F. Brena, J. C. Carrasco-jimenez, and L. Garrido, Long-Term Activity Recognition from Wristwatch Accelerometer Data, Sensors, vol.14, issue.12, pp.22500-22524, 2014.
DOI : 10.3390/s141222500

F. Massé, R. R. Gonzenbach, A. Arami, A. Paraschiv-ionescu, A. R. Luft et al., Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients, Journal of NeuroEngineering and Rehabilitation, vol.114, issue.1, p.12, 2015.
DOI : 10.1186/s12984-015-0060-2

A. Raj, A. Subramanya, D. Fox, and J. Bilmes, Rao-Blackwellized Particle Filters for Recognizing Activities and Spatial Context from Wearable Sensors, Experimental Robotics, pp.211-221, 2008.
DOI : 10.1007/978-3-540-77457-0_20

D. S. Morillo, J. L. Ojeda, L. F. Foix, and A. L. Jiménez, An Accelerometer-Based Device for Sleep Apnea Screening, IEEE Transactions on Information Technology in Biomedicine, vol.14, issue.2, pp.491-499, 2010.
DOI : 10.1109/TITB.2009.2027231

Y. Kuo, K. M. Culhane, P. Thomason, O. Tirosh, and R. Baker, Measuring distance walked and step count in children with cerebral palsy: An evaluation of two portable activity monitors, Gait & Posture, vol.29, issue.2, pp.304-310, 2009.
DOI : 10.1016/j.gaitpost.2008.09.014

H. B. Menz, S. R. Lord, and R. C. Fitzpatrick, Age-related differences in walking stability, Age and Ageing, vol.32, issue.2, pp.137-142, 2003.
DOI : 10.1093/ageing/32.2.137

J. Park, H. J. Kim, and S. Kang, Validation of the AMP331 monitor for assessing energy expenditure of free-living physical activity, Res. Quart. Exerc. Sport, vol.77, p.40, 2006.

C. A. Ronao and S. Cho, Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models, 2014 10th International Conference on Natural Computation (ICNC), pp.19-21, 2014.
DOI : 10.1109/ICNC.2014.6975918

K. K. Peetoom, M. A. Lexis, M. Joore, C. D. Dirksen, and L. P. De-witte, Literature review on monitoring technologies and their outcomes in independently living elderly people, Disability and Rehabilitation: Assistive Technology, vol.2010, issue.4, pp.271-294, 2014.
DOI : 10.1080/08839514.2011.617248

J. Farringdon, A. J. Moore, N. Tilbury, J. Church, and P. Biemond, Wearable sensor badge and sensor jacket for context awareness, Digest of Papers. Third International Symposium on Wearable Computers, pp.18-19, 1999.
DOI : 10.1109/ISWC.1999.806681

C. V. Bouten, K. T. Koekkoek, M. Verduin, R. Kodde, and J. D. Janssen, A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity, IEEE Transactions on Biomedical Engineering, vol.44, issue.3, pp.136-147, 1997.
DOI : 10.1109/10.554760

D. Figo, P. C. Diniz, D. R. Ferreira, and J. M. Cardoso, Preprocessing techniques for context recognition from accelerometer data. Pers. Ubiquitous Comput, pp.645-662, 2010.

A. V. Oppenheim, R. W. Schafer, and J. Buck, Discrete-Time Signal Processing, 1989.

B. Nham, K. Siangliulue, and S. Yeung, Predicting Mode of Transport from Iphone Accelerometer Data, 2008.

J. J. Ho, Interruptions: Using Activity Transitions to Trigger Proactive Messages. Master's Thesis, Massachusetts Institute of Technology, 2004.

L. Bao and S. S. Intille, Activity Recognition from User-Annotated Acceleration Data, Pervasive Computing, pp.1-17, 2004.
DOI : 10.1007/978-3-540-24646-6_1

S. J. Preece, J. Y. Goulermas, L. P. Kenney, and D. Howard, A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data, IEEE Transactions on Biomedical Engineering, vol.56, issue.3, pp.871-879, 2009.
DOI : 10.1109/TBME.2008.2006190

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res, vol.3, pp.1157-1182, 2003.

H. Liu and L. Yu, Toward integrating feature selection algorithms for classification and clustering, IEEE Trans. Knowl. Data Eng, vol.17, pp.491-502, 2005.

R. Kohavi and G. H. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X

S. Das, Filters, wrappers and a boosting-based hybrid for feature selection, Proceedings of the Eighteenth International Conference on Machine Learning, pp.74-81, 2001.

M. Zhang and A. A. Sawchuk, A Feature Selection-Based Framework for Human Activity Recognition Using Wearable Multimodal Sensors, Proceedings of the 6th International ICST Conference on Body Area Networks, pp.7-8, 2011.
DOI : 10.4108/icst.bodynets.2011.247018

B. Fish, A. Khan, N. H. Chehade, C. Chien, and G. Pottie, Feature selection based on mutual information for human activity recognition, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.25-30, 2012.
DOI : 10.1109/ICASSP.2012.6288232

T. Chau, A review of analytical techniques for gait data. Part 1: fuzzy, statistical and fractal methods, Gait & Posture, vol.13, issue.1, pp.49-66, 2001.
DOI : 10.1016/S0966-6362(00)00094-1

A. M. Martínez and A. C. Kak, PCA versus LDA, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.2, pp.228-233, 2001.
DOI : 10.1109/34.908974

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 1999.

A. Krause, D. P. Siewiorek, A. Smailagic, and J. Farringdon, Unsupervised, dynamic identification of physiological and activity context in wearable computing, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings., pp.21-23, 2003.
DOI : 10.1109/ISWC.2003.1241398

A. Webb, Statistical Pattern Recognition, 2003.

S. Theodoridis, A. Pikrakis, K. Koutroumbas, and D. Cavouras, Introduction to Pattern Recognition: A Matlab Approach, 2010.

V. Vapnik, The Nature of Statistical Learning Theory, 2000.

L. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, IEEE Proc. 1989, pp.257-286

D. Trabelsi, S. Mohammed, F. Chamroukhi, L. Oukhellou, and Y. Amirat, An Unsupervised Approach for Automatic Activity Recognition Based on Hidden Markov Model Regression, IEEE Transactions on Automation Science and Engineering, vol.10, issue.3, pp.829-835, 2013.
DOI : 10.1109/TASE.2013.2256349

URL : https://hal.archives-ouvertes.fr/hal-00865069

F. Foerster, M. Smeja, and J. Fahrenberg, Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring, Computers in Human Behavior, vol.15, issue.5, pp.571-583, 1999.
DOI : 10.1016/S0747-5632(99)00037-0

F. Foerster and J. Fahrenberg, Motion pattern and posture: Correctly assessed by calibrated accelerometers, Behavior Research Methods, Instruments, & Computers, vol.18, issue.3, pp.450-457, 2000.
DOI : 10.3758/BF03200815

T. Zhang, J. Wang, L. Xu, and P. Liu, Using Wearable Sensor and NMF Algorithm to Realize Ambulatory Fall Detection, Advances in Natural Computation, pp.488-491, 2006.
DOI : 10.1007/11881223_60

B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 2001.

T. M. Cover, Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition, IEEE Transactions on Electronic Computers, vol.14, issue.3, pp.14-326, 1965.
DOI : 10.1109/PGEC.1965.264137

T. Huynh and B. Schiele, Towards Less Supervision in Activity Recognition from Wearable Sensors, 2006 10th IEEE International Symposium on Wearable Computers, pp.11-14, 2006.
DOI : 10.1109/ISWC.2006.286336

A. Krause, M. Ihmig, E. Rankin, D. Leong, S. Gupta et al., Trading off Prediction Accuracy and Power Consumption for Context-Aware Wearable Computing, Ninth IEEE International Symposium on Wearable Computers (ISWC'05), pp.18-21, 2005.
DOI : 10.1109/ISWC.2005.52

C. Doukas and I. Maglogiannis, Advanced patient or elder fall detection based on movement and sound data, Proceedings of the 2008 Second International Conference on Pervasive Computing Technologies for Healthcare, pp.103-107, 2008.

L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and Regression Trees, 1984.

L. Bedogni, M. Di-felice, and L. Bononi, By train or by car? Detecting the user's motion type through smartphone sensors data, 2012 IFIP Wireless Days, pp.21-23, 2012.
DOI : 10.1109/WD.2012.6402818

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Royal Stat. Soc. Ser. B (Methodol, vol.39, pp.1-38, 1977.

A. Mannini and A. M. Sabatini, Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers, Sensors, vol.10, issue.2, pp.1154-1175, 2010.
DOI : 10.3390/s100201154

W. Ong, T. Koseki, and L. Palafox, An Unsupervised Approach for Human Activity Detection and Recognition, Int. J. Simul. Syst. Sci. Technol, p.14, 2013.

P. Cottone, G. L. Re, G. Maida, and M. Morana, Motion sensors for activity recognition in an ambient-intelligence scenario, 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp.18-22, 2013.
DOI : 10.1109/PerComW.2013.6529573

G. D. Forney and . Jr, The viterbi algorithm, Proceedings of the IEEE, vol.61, issue.3, pp.268-278, 1973.
DOI : 10.1109/PROC.1973.9030

J. Lester, T. Choudhury, N. Kern, G. Borriello, and B. Hannaford, A Hybrid Discriminative/ Generative Approach for Modeling Human Activities, Proceedings of the IJCAI'05 19th international joint conference on Artificial intelligence, pp.766-772, 2005.

K. V. Laerhoven, H. Gellersen, and Y. G. Malliaris, Long-Term Activity Monitoring with a Wearable Sensor Node, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06), pp.3-5, 2006.
DOI : 10.1109/BSN.2006.39

P. Lukowicz, J. A. Ward, H. Junker, M. Stäger, G. Tröster et al., Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers, Pervasive Computing, pp.18-32, 2004.
DOI : 10.1007/978-3-540-24646-6_2

J. A. Ward, P. Lukowicz, G. Troster, and T. Starner, Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.10, pp.1553-1567, 2006.
DOI : 10.1109/TPAMI.2006.197

J. Boyle, M. Karunanithi, T. Wark, W. Chan, and C. Colavitti, Quantifying Functional Mobility Progress for Chronic Disease Management, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp.5916-5919, 2006.
DOI : 10.1109/IEMBS.2006.260426

K. Culhane, G. Lyons, D. Hilton, P. Grace, and D. Lyons, Long-term mobility monitoring of older adults using accelerometers in a clinical environment, Clinical Rehabilitation, vol.32, issue.3, pp.335-343, 2004.
DOI : 10.1191/0269215504cr734oa

B. Najafi, K. Aminian, F. Loew, Y. Blanc, and P. A. Robert, Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly, IEEE Transactions on Biomedical Engineering, vol.49, issue.8, pp.843-851, 2002.
DOI : 10.1109/TBME.2002.800763

A. Bourke, K. O-'donovan, and G. Olaighin, The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls, Medical Engineering & Physics, vol.30, issue.7, pp.937-946, 2008.
DOI : 10.1016/j.medengphy.2007.12.003

M. Marin-perianu, C. Lombriser, O. Amft, P. Havinga, and G. Tröster, Distributed Activity Recognition with Fuzzy-Enabled Wireless Sensor Networks, Distributed Computing in Sensor Systems, pp.296-313, 2008.
DOI : 10.1007/978-3-540-69170-9_20

J. L. Mcclelland, D. E. Rumelhart, and P. Group, Parallel Distributed Processing: Explorations in the Microstructures of Cognition, 1986.

U. Maurer, A. Rowe, and A. Smailagic, Siewiorek, D. Location and Activity Recognition Using eWatch: A Wearable Sensor Platform, In Ambient Intelligence in Everyday Life, pp.86-102, 2006.

N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman, Activity recognition from accelerometer data, Proceedings of the IAAI'05 17th conference on Innovative applications of artificial intelligence, pp.9-13, 2005.

J. Fahrenberg, W. Muller, F. Foerster, and M. Smeja, A multi-channel investigation of physical activity, J. Psychophysiol, vol.10, pp.209-217, 1996.

S. Lee, H. Park, S. Hong, K. Lee, and Y. Kim, A study on the activity classification using a triaxial accelerometer, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), pp.17-21, 2003.
DOI : 10.1109/IEMBS.2003.1280534

M. Ermes, J. Parkka, and J. Mantyjarvi, Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions, IEEE Transactions on Information Technology in Biomedicine, vol.12, issue.1, pp.20-26, 2008.
DOI : 10.1109/TITB.2007.899496

K. Liu, T. Liu, K. Shibata, and Y. Inoue, Ambulatory measurement and analysis of the lower limb 3D posture using wearable sensor system, Proceedings of the 2009 ICMA International Conference on Mechatronics and Automation, pp.9-12, 2009.

J. Favre, F. Luthi, B. Jolles, O. Siegrist, B. Najafi et al., A new ambulatory system for comparative evaluation of the three-dimensional knee kinematics, applied to anterior cruciate ligament injuries, Knee Surgery, Sports Traumatology, Arthroscopy, vol.84, issue.4, pp.592-604, 2006.
DOI : 10.1007/s00167-005-0023-4

C. Chang and C. Lin, LIBSVM, TIST) 2011
DOI : 10.1145/1961189.1961199