. Actigraph, What are counts?, 2016.

J. K. Aggarwal and M. S. Ryoo, Human activity analysis: A review, ACM Computing Surveys (CSUR), vol.43, p.16, 2011.

P. N. Ainslie, T. Reilly, and K. R. Westerterp, Estimating human energy expenditure. Sports medicine, vol.33, pp.683-698, 2003.

B. E. Ainsworth, Compendium of physical activities: classification of energy costs of human physical activities, Medicine & Science in Sports & Exercise, pp.71-80, 1993.

A. Ali, R. C. King, and G. Yang, Semi-supervised segmentation for activity recognition with Multiple Eigenspaces, MDBS 2008. 5th International Summer School and Symposium on, pp.314-317, 2008.

K. Altun, B. Barshan, and O. Tunçel, Comparative study on classifying human activities with miniature inertial and magnetic sensors, Pattern Recognition, vol.43, pp.3605-3620, 2010.

U. Anliker, AMON: a wearable multiparameter medical monitoring and alert system, IEEE Transactions on information technology in Biomedicine, vol.8, pp.415-427, 2004.

W. O. Atwater and F. G. Benedict, A respiration calorimeter, 1905.

L. Bao and S. S. Intille, Activity recognition from user-annotated acceleration data, Pervasive computing, pp.1-17, 2004.

M. Bao, Distance measures for signal processing and pattern recognition. Signal processing, vol.8, pp.349-369, 1989.

T. E. Bernard, E. Kamon, and B. A. Franklin, Estimation of oxygen consumption from pulmonary ventilation during exercise, Human Factors: The Journal of the Human Factors and Ergonomics Society, vol.21, pp.417-421, 1979.

W. Bianchi, Revitalizing a vital sign: improving detection of tachypnea at primary triage. Annals of emergency medicine, vol.61, pp.37-43, 2013.

A. Bonomi and K. Westerterp, Advances in physical activity monitoring and lifestyle interventions in obesity: a review, International Journal of Obesity, vol.36, pp.167-177, 2012.

A. G. Bonomi, Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer, Journal of Applied Physiology, vol.107, pp.655-661, 2009.

A. K. Bourke, A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology-The ADAPT Study Data-Set, Sensors, vol.17, p.559, 2017.

U. Boutellier, R. Arieli, and L. E. Farhi, Ventilation and CO 2 response during+ Gz acceleration. Respiration physiology, vol.62, pp.141-151, 1985.

C. V. Bouten, A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity, Biomedical Engineering, vol.44, pp.136-147, 1997.

J. Bussmann, Measuring daily behavior using ambulatory accelerometry: the Activity Monitor, Behavior Research Methods, Instruments, & Computers, vol.33, pp.349-356, 2001.

N. F. Butte, U. Ekelund, and K. R. Westerterp, Assessing physical activity using wearable monitors: measures of physical activity. Medicine and science in sports and exercise, vol.44, pp.5-12, 2012.

J. A. Carlson, Validity of PALMS GPS Scoring of Active and Passive Travel Compared with SenseCam, Med. Sci. Sports Exerc, 2014.

P. Celka, Wearable biosensing: signal processing and communication architectures issues, Journal of Telecommunications and Information Technology, pp.90-104, 2005.

K. Y. Chen and D. R. Bassett, The technology of accelerometry-based activity monitors: current and future. Medicine and science in sports and exercise, vol.37, p.490, 2005.

Y. Chen, Online classifier construction algorithm for human activity detection using a tri-axial accelerometer, Applied Mathematics and Computation, vol.205, pp.849-860, 2008.

L. Chow and N. Bambos, Real-time physiological stream processing for health monitoring services, e-Health Networking, Applications & Services (Healthcom), pp.611-616, 2013.

C. F. Clarenbach, Monitoring of ventilation during exercise by a portable respiratory inductive plethysmograph, CHEST Journal, vol.128, pp.1282-1290, 2005.

I. Cleland, Optimal Placement of Accelerometers for the Detection of Everyday Activities, Sensors, vol.13, pp.9183-9200, 2013.

. Cominlabs and . Sherpam, , 2014.

S. Corbin-jallow and R. Moore, Projections of Expenditures for Long-Term Care Services for the Elderly, 1999.

S. E. Crouter, J. R. Churilla, and D. R. Bassett, Estimating energy expenditure using accelerometers, European journal of applied physiology, vol.98, pp.601-612, 2006.

S. E. Crouter, K. G. Clowers, and D. R. Bassett, A novel method for using accelerometer data to predict energy expenditure, Journal of applied physiology, vol.100, pp.1324-1331, 2006.

B. Cvetkovic, R. Milic, and M. Lustrek, Estimating Energy Expenditure with Multiple Models using Different Wearable Sensors, 2015.

,. De-müllenheim, Using GPS, accelerometry and heart rate to predict outdoor graded walking energy expenditure, Journal of Science and Medicine in Sport, 2017.

S. I. De-vries, Evaluation of neural networks to identify types of activity using accelerometers, Med Sci Sports Exerc, vol.43, pp.101-108, 2011.

R. Devaul and S. Dunn, Real-Time Motion Classification for Wearable Computing Applications, 2001.

E. P. Doheny, Falls classification using tri-axial accelerometers during the five-times-sit-to-stand test, Gait & posture, vol.38, pp.1021-1025, 2013.

A. R. Doherty, Using wearable cameras to categorise type and context of accelerometer-identified episodes of physical activity, Int J Behav Nutr Phys Act, issue.10, p.1186, 2013.

M. E. Druyan, Clinical guidelines for the use of parenteral and enteral nutrition in adult and pediatric patients: applying the GRADE system to development of ASPEN clinical guidelines, Journal of Parenteral and Enteral Nutrition, vol.36, pp.77-80, 2012.

L. R. Dugas, A novel energy expenditure prediction equation for intermittent physical activity. Medicine and science in sports and exercise, vol.37, p.2154, 2005.

L. Dugdill, D. Crone, and R. Murphy, Physical activity and health promotion: evidence-based approaches to practice, 2009.

R. Dumond, Estimation of respiratory volume from thoracoabdominal breathing distances: comparison of two models of machine learning, European Journal of Applied Physiology, pp.1-23, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01585982

R. Dunnewold, C. Jacobi, and J. Van-hilten, Quantitative assessment of bradykinesia in patients with Parkinson's disease, Journal of neuroscience methods, vol.74, pp.107-112, 1997.

J. Durnin and R. Edwards, Pulmonary ventilation as an index of energy expenditure. Quarterly journal of experimental physiology and cognate medical sciences, vol.40, pp.370-377, 1955.

K. Ellis, Identifying active travel behaviors in challenging environments using GPS, accelerometers, and machine learning algorithms, Public Health, vol.2, pp.39-46, 2014.

M. Ermes, Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions, IEEE transactions on information technology in biomedicine, vol.12, pp.20-26, 2008.

H. Fang, R. Srinivasan, and D. J. Cook, Feature selections for human activity recognition in smart home environments, Int. J. Innov. Comput. Inf. Control, vol.8, pp.3525-3535, 2012.

I. Farkas and E. Doran, Activity Recognition from Acceleration Data Collected With a Tri-Axial Accelerometer. Lucrare publicata la Acta Tehnica Napocensis Electronics and Telecommunication, pp.52-90, 2011.

D. Figo, Preprocessing techniques for context recognition from accelerometer data. Personal and Ubiquitous Computing, vol.14, pp.645-662, 2010.

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, pp.571-583, 1999.

J. Franke and S. Halim, Fraunhofer-Institut für Techno-und Wirtschaftsmathematik, Fraunhofer (ITWM), 2007.

P. S. Freedson, E. Melanson, and J. Sirard, Calibration of the Computer Science and Applications, Inc. accelerometer. Medicine and science in sports and exercise, vol.30, pp.777-781, 1998.

P. S. Freedson and K. Miller, Objective monitoring of physical activity using motion sensors and heart rate. Research quarterly for exercise and sport, vol.71, pp.21-29, 2000.

M. L. Fruin and J. W. Rankin, Validity of a multi-sensor armband in estimating rest and exercise energy expenditure. Medicine and science in sports and exercise, vol.36, pp.1063-1069, 2004.

S. Gastinger, Estimates of ventilation from measurements of rib cage and abdominal distances: a portable device, European journal of applied physiology, vol.109, pp.1179-1189, 2010.
URL : https://hal.archives-ouvertes.fr/halshs-00651197

S. Gastinger, A comparison between ventilation and heart rate as indicator of oxygen uptake during different intensities of exercise, Journal of sports science & medicine, vol.9, p.110, 2010.
URL : https://hal.archives-ouvertes.fr/halshs-00649701

S. Gastinger, A review of the evidence for the use of ventilation as a surrogate measure of energy expenditure, Journal of Parenteral and Enteral Nutrition, vol.38, pp.926-938, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01159355

H. Gjoreski, Context-based ensemble method for human energy expenditure estimation, Applied Soft Computing, 2015.

S. Gradl, Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices, 2012 Annual International Conference of the IEEE, pp.2452-2455, 2012.

P. M. Grant, The validation of a novel activity monitor in the measurement of posture and motion during everyday activities, British journal of sports medicine, vol.40, pp.992-997, 2006.

M. Hall, The WEKA data mining software: an update, ACM SIGKDD explorations newsletter, vol.11, pp.10-18, 2009.

S. P. Helmrich, Physical activity and reduced occurrence of non-insulin-dependent diabetes mellitus, New England journal of medicine, vol.325, pp.147-152, 1991.

F. C. Hoppensteadt and C. S. Peskin, Modeling and simulation in medicine and the life sciences, vol.10, 2012.

F. B. Hu, Adiposity as compared with physical activity in predicting mortality among women, New England Journal of Medicine, vol.351, pp.2694-2703, 2004.

M. Hu, Refining Time-Activity Classification of Human Subjects Using the Global Positioning System, PloS one, vol.11, p.148875, 2016.

A. Huss, Using GPS-derived speed patterns for recognition of transport modes in adults, International journal of health geographics, vol.13, p.40, 2014.

T. Huynh and B. Schiele, Towards less supervision in activity recognition from wearable sensors, 10th IEEE International Symposium on, pp.3-10, 2006.

N. Jalloul, Detection of Levodopa Induced Dyskinesia in Parkinson's Disease patients based on activity classification, 37th Annual International Conference of the IEEE, pp.5134-5137, 2015.

L. C. Jatobá, Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity, 30th Annual International Conference of the IEEE, pp.5250-5253, 2008.

Y. Jia, Diatetic and exercise therapy against diabetes mellitus, Intelligent Networks and Intelligent Systems, 2009. ICINIS'09. Second International Conference on, pp.693-696, 2009.

J. A. Johnstone, Bioharness? multivariable monitoring device: part. II: reliability, Journal of sports science & medicine, vol.11, p.409, 2012.

J. A. Johnstone, BioHarness? multivariable monitoring device: part. I: validity. Journal of sports science & medicine, vol.11, p.400, 2012.

J. A. Johnstone, Field based reliability and validity of the BioHarness? multivariable monitoring device, Journal of sports science & medicine, vol.11, p.643, 2012.

E. Jovanov, A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation, Journal of NeuroEngineering and rehabilitation, vol.2, p.6, 2005.

R. J. Kate, Comparative evaluation of features and techniques for identifying activity type and estimating energy cost from accelerometer data, Physiological measurement, vol.37, p.360, 2016.

S. Katz, Progress in development of the index of ADL. The gerontologist, vol.10, pp.20-30, 1970.

P. Kelly, An ethical framework for automated, wearable cameras in health behavior research. American journal of preventive medicine, vol.44, pp.314-319, 2013.

J. Kerr, Using the SenseCam to improve classifications of sedentary behavior in free-living settings. American journal of preventive medicine, vol.44, pp.290-296, 2013.

J. Kerr, Objective assessment of physical activity: classifiers for public health. Medicine and science in sports and exercise, vol.48, p.951, 2016.

R. Khusainov, Real-time human ambulation, activity, and physiological monitoring: Taxonomy of issues, techniques, applications, challenges and limitations, Sensors, vol.13, pp.12852-12902, 2013.

J. Kim, Measurement accuracy of heart rate and respiratory rate during graded exercise and sustained exercise in the heat using the Zephyr BioHarnessTM, International journal of sports medicine, vol.34, pp.497-501, 2013.

K. Konno and J. Mead, Measurement of the separate volume changes of rib cage and abdomen during breathing, Journal of applied physiology, vol.22, pp.407-422, 1967.

S. L. Kozey, Accelerometer output and MET values of common physical activities. Medicine and science in sports and exercise, vol.42, p.1776, 2010.

J. Krumm and E. Horvitz, Predestination: Inferring destinations from partial trajectories, UbiComp 2006: Ubiquitous Computing, pp.243-260, 2006.

L. I. Kuncheva, Combining pattern classifiers: methods and algorithms, 2004.

K. Kunze, Towards recognizing tai chi-an initial experiment using wearable sensors, Applied Wearable Computing (IFAWC), pp.1-6, 2006.

Y. T. Lagerros and P. Lagiou, Assessment of physical activity and energy expenditure in epidemiological research of chronic diseases, European journal of epidemiology, vol.22, pp.353-362, 2007.

R. E. Laporte, H. J. Montoye, and C. J. Caspersen, Assessment of physical activity in epidemiologic research: problems and prospects, Public health reports, vol.100, p.131, 1985.

O. D. Lara and M. A. Labrador, A mobile platform for real-time human activity recognition, Consumer Communications and Networking Conference (CCNC), pp.667-671, 2012.

O. D. Lara and M. A. Labrador, A survey on human activity recognition using wearable sensors, Communications Surveys & Tutorials, IEEE, vol.15, pp.1192-1209, 2013.

Ó. D. Lara, Centinela: A human activity recognition system based on acceleration< i> and</i> vital sign data. Pervasive and mobile computing, vol.8, pp.717-729, 2012.

L. Faucheur and A. , Measurement of walking distance and speed in patients with peripheral arterial disease, Circulation, vol.117, pp.897-904, 2008.

L. Masurier, G. C. Tudor-locke, and C. , Comparison of pedometer and accelerometer accuracy under controlled conditions, Medicine and Science in Sports and Exercise, vol.35, pp.867-871, 2003.

V. Le-rolle, Recursive Model Identification for the Evaluation of Baroreflex Sensitivity, Acta biotheoretica, vol.64, pp.469-478, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01417320

M. F. Leitzmann, Physical activity recommendations and decreased risk of mortality. Archives of internal medicine, vol.167, pp.2453-2460, 2007.

W. R. Leonard and S. J. Ulijaszek, Energetics and evolution: an emerging research domain, American Journal of Human Biology, vol.14, pp.547-550, 2002.

W. R. Leonard, Measuring human energy expenditure: What have we learned from the flex-heart rate method? American journal of human biology, vol.15, pp.479-489, 2003.

H. Leutheuser, D. Schuldhaus, and B. M. Eskofier, Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset, PloS one, vol.8, p.75196, 2013.

L. Liao, Learning and inferring transportation routines, Artificial Intelligence, vol.171, pp.311-331, 2007.

R. P. Lippmann, Pattern classification using neural networks, IEEE communications magazine, vol.27, pp.47-50, 1989.

B. Lo and G. Yang, Appendix A: Wireless Sensor Development Platforms. Body Sensor Networks, vol.527, 2014.

B. P. Lo, Body sensor network-a wireless sensor platform for pervasive healthcare monitoring, Effective clinical practice: ECP, vol.4, pp.256-262, 2001.

C. Luca and A. Salceanu, On the physiological influence of electromagnetic waves considering an electrical model of pulmonary ventilation, Word Energy System Conference (WESC). Vol, pp.28-30, 2012.

K. Lyden, A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations, European journal of applied physiology, vol.111, pp.187-201, 2011.

K. Lyden, A method to estimate free-living active and sedentary behavior from an accelerometer. Medicine and science in sports and exercise, vol.46, p.386, 2014.

J. Lynch, Moderately intense physical activities and high levels of cardiorespiratory fitness reduce the risk of noninsulin-dependent diabetes mellitus in middle-aged men. Archives of internal medicine, vol.156, pp.1307-1314, 1996.

A. Mannini and A. M. Sabatini, Machine learning methods for classifying human physical activity from on-body accelerometers, Sensors, vol.10, pp.1154-1175, 2010.

S. Martin, Self-monitoring of blood glucose in type 2 diabetes and long-term outcome: an epidemiological cohort study, Diabetologia, vol.49, pp.271-278, 2006.

M. J. Mathie, Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement, Physiological measurement, vol.25, p.1, 2004.

U. Maurer, Activity recognition and monitoring using multiple sensors on different body positions, Wearable and Implantable Body Sensor Networks, pp.4-116, 2006.

F. Mccool, Estimates of ventilation from body surface measurements in unrestrained subjects, Journal of Applied Physiology, vol.61, pp.1114-1119, 1986.

J. Mclaughlin, Validation of the COSMED K4 b2 portable metabolic system, International journal of sports medicine, vol.22, pp.280-284, 2001.

E. L. Melanson, P. S. Freedson, and S. Blair, Physical activity assessment: a review of methods, Critical Reviews in Food Science & Nutrition, vol.36, pp.385-396, 1996.

T. B. Moeslund and E. Granum, A survey of computer vision-based human motion capture. Computer vision and image understanding, vol.81, pp.231-268, 2001.

S. T. Moore, H. G. Macdougall, and W. G. Ondo, Ambulatory monitoring of freezing of gait in Parkinson's disease, Journal of neuroscience methods, vol.167, pp.340-348, 2008.

S. C. Mukhopadhyay, Wearable sensors for human activity monitoring: A review, IEEE sensors journal, vol.15, pp.1321-1330, 2015.

P. Y. Müllenheim, Clinical Interest of Ambulatory Assessment of Physical Activity and Walking Capacity in Peripheral Artery Disease. Scandinavian journal of medicine & science in sports, vol.26, pp.716-730, 2016.

S. L. Murphy, Review of physical activity measurement using accelerometers in older adults: considerations for research design and conduct. Preventive medicine, vol.48, pp.108-114, 2009.

J. Myers, Fitness versus physical activity patterns in predicting mortality in men. The American journal of medicine, vol.117, pp.912-918, 2004.

D. Nguyen, Assessment of physical activity and energy expenditure by GPS combined with accelerometry in reallife conditions, Journal of physical activity & health, vol.10, 2013.

D. Ojeda, Sensitivity Analysis of Vagus Nerve Stimulation Parameters on Acute Cardiac Autonomic Responses: Chronotropic, Inotropic and Dromotropic Effects, PloS one, vol.11, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01395131

N. Owen, Sedentary behavior: emerging evidence for a new health risk, Mayo Foundation, vol.85, p.1138, 2010.

J. Pan and W. J. Tompkins, A real-time QRS detection algorithm, Biomedical Engineering, pp.230-236, 1985.

A. Pande, Energy Expenditure Estimation in boys with Duchene muscular dystrophy using accelerometer and heart rate sensors, Healthcare Innovation Conference (HIC), pp.26-29, 2014.

J. Parkka, Activity classification using realistic data from wearable sensors. Information Technology in Biomedicine, IEEE Transactions on, vol.10, pp.119-128, 2006.

J. Pärkkä, Analysis of personal health monitoring data for physical activity recognition and assessment of energy expenditure, mental load and stress, Science, vol.183, pp.922-932, 1974.

A. Pentland, Healthwear: medical technology becomes wearable, Computer, vol.37, pp.42-49, 2004.

A. J. Perez, M. A. Labrador, and S. J. Barbeau, G-sense: a scalable architecture for global sensing and monitoring, IEEE Network, p.24, 2010.

D. M. Pober, Development of novel techniques to classify physical activity mode using accelerometers. Medicine and science in sports and exercise, vol.38, p.1626, 2006.

K. E. Powell, A. E. Paluch, and S. N. Blair, Physical activity for health: What kind? How much? How intense? On top of what? Public Health, vol.32, p.349, 2011.

S. J. Preece, Activity identification using body-mounted sensors-a review of classification techniques. Physiological measurement, vol.30, p.1, 2009.

T. Psota and K. Chen, Measuring energy expenditure in clinical populations: rewards and challenges, European journal of clinical nutrition, vol.67, pp.436-442, 2013.

P. Puska, H. Benaziza, and D. Porter, Physical Activity, WHO Information Sheet on Physical Activity, WHO, 2004.

M. R. Puyau, Prediction of activity energy expenditure using accelerometers in children. Medicine and science in sports and exercise, vol.36, pp.1625-1631, 2004.

N. Ravi, Activity recognition from accelerometer data, In: AAAI, vol.5, pp.1541-1546, 2005.

E. Ravussin and C. Bogardus, A brief overview of human energy metabolism and its relationship to essential obesity. The American journal of clinical nutrition, vol.55, pp.242-245, 1992.

S. Reddy, Using mobile phones to determine transportation modes, ACM Transactions on Sensor Networks (TOSN), vol.6, p.13, 2010.

D. Roetenberg, P. J. Slycke, and P. H. Veltink, Ambulatory position and orientation tracking fusing magnetic and inertial sensing, IEEE Transactions on Biomedical Engineering, vol.54, pp.883-890, 2007.

H. Rosdahl, Evaluation of the Oxycon Mobile metabolic system against the Douglas bag method, European journal of applied physiology, vol.109, pp.159-171, 2010.

D. Rosenberg, Classifiers for Accelerometer-Measured Behaviors in Older Women, Medicine & Science in Sports & Exercise, 2016.

P. E. Ross, Managing care through the air [remote health monitoring, IEEE spectrum, vol.41, pp.26-31, 2004.

M. P. Rothney, An artificial neural network model of energy expenditure using nonintegrated acceleration signals, Journal of applied physiology, vol.103, pp.1419-1427, 2007.

S. Roy and J. Mccrory, Validation of maximal heart rate prediction equations based on sex and physical activity status, International journal of exercise science, vol.8, p.318, 2015.

J. Ryder, Ambulation: A tool for monitoring mobility patterns over time using mobile phones, CSE'09. International Conference on, vol.4, pp.927-931, 2009.

A. M. Sabatini, Inertial sensing in biomechanics: a survey of computational techniques bridging motion analysis and personal navigation, Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques, pp.70-100, 2006.

J. F. Sallis and B. E. Saelens, Assessment of physical activity by self-report: status, limitations, and future directions. Research quarterly for exercise and sport, vol.71, pp.1-14, 2000.

J. E. Sasaki, Performance of activity classification algorithms in free-living older adults. Medicine and science in sports and exercise, vol.48, p.941, 2016.

M. B. Schneller, Validation of Five Minimally Obstructive Methods to Estimate Physical Activity Energy Expenditure in Young Adults in Semi-Standardized Settings, Sensors, vol.15, pp.6133-6151, 2015.

D. A. Schoeller and S. B. Racette, A review of field techniques for the assessment of energy expenditure, The Journal of nutrition, vol.120, pp.1492-1495, 1990.

L. B. Sherar, International children's accelerometry database (ICAD): design and methods, BMC Public Health, vol.11, p.485, 2011.

M. Shoaib, H. Scholten, and P. J. Havinga, Towards physical activity recognition using smartphone sensors, IEEE 10th International Conference on and 10th International Conference on Autonomic and Trusted Computing (UIC/ATC). Vol, pp.80-87, 2013.

M. Shoaib, A survey of online activity recognition using mobile phones, Sensors, vol.15, pp.2059-2085, 2015.

J. Shotton, Real-time human pose recognition in parts from single depth images, Communications of the ACM, vol.56, pp.116-124, 2013.

J. Skotte, Detection of physical activity types using triaxial accelerometers, Journal of Physical Activity and Health, 2012.

M. St-onge, Evaluation of a portable device to measure daily energy expenditure in free-living adults. The American journal of clinical nutrition, vol.85, pp.742-749, 2007.

P. Standing, Changes in referral patterns to cardiac out-patient clinics with ambulatory ECG monitoring in general practice, British Journal of Cardiology, vol.8, pp.394-399, 2001.

J. Staudenmayer, An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer, Journal of Applied Physiology, vol.107, pp.1300-1307, 2009.

K. L. Steimle, A mathematical physiological model of the pulmonary ventilation, The 7th IFAC Symposium on Modeling and Control in Biomedical Systems, pp.222-229, 2009.

K. L. Steimle, A model of ventilation of the healthy human lung. computer methods and programs in biomedicine, vol.101, pp.144-155, 2011.

K. L. Storti, Gait speed and step-count monitor accuracy in community-dwelling older adults, Medicine & Science in Sports & Exercise, vol.40, pp.59-64, 2008.

S. J. Strath, Evaluation of heart rate as a method for assessing moderate intensity physical activity. Medicine and science in sports and exercise, vol.32, pp.465-70, 2000.

M. Sung, C. Marci, and A. Pentland, Wearable feedback systems for rehabilitation, Journal of neuroengineering and rehabilitation, vol.2, p.17, 2005.

A. M. Swartz, Estimation of energy expenditure using CSA accelerometers at hip and wrist sites, Medicine and Science in Sports and Exercise, vol.32, pp.450-456, 2000.

Y. Tao, H. Hu, and H. Zhou, Integration of vision and inertial sensors for 3D arm motion tracking in home-based rehabilitation, The International Journal of Robotics Research, vol.26, pp.607-624, 2007.

E. M. Tapia, The design of a portable kit of wireless sensors for naturalistic data collection, Pervasive Computing, pp.117-134, 2006.

E. M. Tapia, Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart monitor, In: Proc. Int. Symp. on Wearable Comp, 2007.

E. M. Tapia, Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor, 11th IEEE International Symposium on, pp.37-40, 2007.

X. Teng, Wearable medical systems for p-health, IEEE reviews in Biomedical engineering, vol.1, pp.62-74, 2008.

B. Tessendorf, Recognition of hearing needs from body and eye movements to improve hearing instruments. Pervasive Computing, pp.314-331, 2011.

T. Wyss, M. Méder, and U. , Recognition of military-specific physical activities with body-fixed sensors. Military medicine, vol.175, p.111858, 2010.

D. Thompson, Multidimensional physical activity: an opportunity not a problem. Exercise and sport sciences reviews, vol.43, p.67, 2015.

D. Trabelsi, An unsupervised approach for automatic activity recognition based on hidden Markov model regression. Automation Science and Engineering, IEEE Transactions on, vol.10, pp.829-835, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00865069

S. G. Trost, Artificial neural networks to predict activity type and energy expenditure in youth. Medicine and science in sports and exercise, vol.44, p.1801, 2012.

G. Uswatte, Objective measurement of functional upper-extremity movement using accelerometer recordings transformed with a threshold filter, Stroke, vol.31, pp.662-667, 2000.

H. Vähä-ypyä, A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer, Clinical physiology and functional imaging, 2014.

E. Valanou, C. Bamia, and A. Trichopoulou, Methodology of physical-activity and energy-expenditure assessment: a review, Journal of Public Health, vol.14, pp.58-65, 2006.

V. T. Van-hees, Impact of study design on development and evaluation of an activity type classifier, Journal of Applied Physiology, 2013.

E. M. Van-sluijs, A. M. Mcminn, and S. J. Griffin, Effectiveness of interventions to promote physical activity in children and adolescents: systematic review of controlled trials, Bmj, vol.335, p.703, 2007.

W. J. Verberk, Self-measurement of blood pressure at home reduces the need for antihypertensive drugs, Hypertension, vol.50, pp.1019-1025, 2007.

T. Viéville and O. D. Faugeras, Cooperation of the inertial and visual systems, Traditional and non-traditional robotic sensors, pp.339-350, 1990.

J. Waldrop and S. M. Stern, , vol.3, 2000.

H. Wang, Resource-aware secure ECG healthcare monitoring through body sensor networks, vol.17, 2010.

W. Wang, Analysis of filtering methods for 3D acceleration signals in body sensor network, Bioelectronics and Bioinformatics, pp.263-266, 2011.

D. E. Warburton, C. W. Nicol, and S. S. Bredin, Health benefits of physical activity: the evidence, Canadian medical association journal, vol.174, pp.801-809, 2006.

K. Wijndaele, Utilization and Harmonization of Adult Accelerometry Data: Review and Expert Consensus. Medicine and science in sports and exercise, 2015.

J. Winkley, P. Jiang, and W. Jiang, Verity: an ambient assisted living platform, IEEE Transactions on Consumer Electronics, vol.58, 2012.

J. D. Witt, Measurement of exercise ventilation by a portable respiratory inductive plethysmograph. Respiratory physiology & neurobiology, vol.154, pp.389-395, 2006.

J. Wu, Automated time activity classification based on global positioning system (GPS) tracking data, Environmental Health, vol.10, p.101, 2011.

J. Yin, Q. Yang, and J. J. Pan, Sensor-based abnormal human-activity detection, IEEE Transactions on Knowledge and Data Engineering, vol.20, pp.1082-1090, 2008.

I. F. Zakeri, Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers, The Journal of nutrition, vol.143, pp.114-122, 2013.

K. Zhang, F. X. Pi-sunyer, and C. N. Boozer, Improving energy expenditure estimation for physical activity. Medicine and science in sports and exercise, vol.36, pp.883-889, 2004.

T. Zhang, Using wearable sensor and NMF algorithm to realize ambulatory fall detection. Advances in Natural Computation, pp.488-491, 2006.

Y. Zheng, Understanding mobility based on GPS data, Proceedings of the 10th international conference on Ubiquitous computing, pp.312-321, 2008.

R. Zhu and Z. Zhou, A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package, IEEE Transactions on Neural systems and rehabilitation engineering, vol.12, pp.295-302, 2004.

D. Zongker and A. Jain, Algorithms for feature selection: An evaluation, Proceedings of the 13th International Conference on, vol.2, pp.18-22, 1996.

A. Rahman, H. Di-ge, A. Le-faucheur, J. Prioux, and G. Carrault, Advanced classification of ambulatory activities using spectral density distances and heart rate, Biomedical Signal Processing and Control Journal, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01507628

A. Rahman, H. Bagot, M. Khreiss, S. Boulanger, J. Dumont et al., SHERPAM: an open

R. Dumond, S. Gastinger, A. Rahman, H. , L. Faucheur et al., Estimation of Respiratory Volume from Thoracoabdominal Breathing Distances: Comparison of Two Models of Machine Learning, European Journal of Applied Physiology, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01585982

A. Rahman, H. , G. Carrault, D. G. , J. Prioux et al., Amoud: Ambulatory physical activity representation and classification using spectral distances approach, 3rd International Conference on Advances in Biomedical Engineering (ICABME'15), 2015.

A. Rahman, H. Le-faucheur, A. Ge, D. Carrault, G. Prioux et al., Recognition of dynamic and stationary activity types in out-of-lab conditions using rate and accelerometers data fusion, 2016.

A. Rahman, H. , L. Faucheur, A. Ge, D. Carrualt et al., From bounded to pragmatic data collection protocol: Validity of state-of-the-art activity recognition in daily life context, 5th International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM'17), 2017.

R. Dumond, L. Faucheur, A. De-mullenheim, P. , A. Rahman et al., Couplage de l'accéléromètre, du géo-positionnement satellitaire et de la fréquence cardiaque dans la détection d'activités physiques et sédentaires, 2016.

R. Dumond, L. Faucheur, A. De-mullenheim, P. , A. Rahman et al., Couplage du géopositionnement satellitaire, de l'accélérométrie et de la fréquence cardiaque dans la détection de différents types d'activités physiques et sédentaires, Associations des Chercheurs en Activités Physiques et Sportives, 2015.