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This review provides a comprehensive summary of the technical aspects linked to sensors (types, number, body positions, and technical characteristics) as well as a deep discussion on the protocols implemented in free-living conditions (environment, duration, instructions, activities, and annotation). Finally, it presents a description and a comparison of the main algorithms and processing tools used for assessing physical activity from raw signals.<\/jats:p>","DOI":"10.3390\/s20195625","type":"journal-article","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T09:04:12Z","timestamp":1601543052000},"page":"5625","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review"],"prefix":"10.3390","volume":"20","author":[{"given":"Sylvain","family":"Jung","sequence":"first","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-91190 Gif-sur-Yvette, France"},{"name":"Universit\u00e9 de Paris, CNRS, Centre Borelli, F-75005 Paris, France"},{"name":"Universit\u00e9 Sorbonne Paris Nord, L2TI, UR 3043, F-93430 Villetaneuse, France"},{"name":"ENGIE Lab CRIGEN, F-93249 Stains, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mona","family":"Michaud","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-91190 Gif-sur-Yvette, France"},{"name":"Universit\u00e9 de Paris, CNRS, Centre Borelli, F-75005 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4750-2265","authenticated-orcid":false,"given":"Laurent","family":"Oudre","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-91190 Gif-sur-Yvette, France"},{"name":"Universit\u00e9 de Paris, CNRS, Centre Borelli, F-75005 Paris, France"},{"name":"Universit\u00e9 Sorbonne Paris Nord, L2TI, UR 3043, F-93430 Villetaneuse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1606-6461","authenticated-orcid":false,"given":"Eric","family":"Dorveaux","sequence":"additional","affiliation":[{"name":"ENGIE Lab CRIGEN, F-93249 Stains, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Louis","family":"Gorintin","sequence":"additional","affiliation":[{"name":"ENGIE Lab CRIGEN, F-93249 Stains, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Vayatis","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-91190 Gif-sur-Yvette, France"},{"name":"Universit\u00e9 de Paris, CNRS, Centre Borelli, F-75005 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damien","family":"Ricard","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-91190 Gif-sur-Yvette, France"},{"name":"Universit\u00e9 de Paris, CNRS, Centre Borelli, F-75005 Paris, France"},{"name":"Service de Neurologie, Service de Sant\u00e9 des Arm\u00e9es, H\u00f4pital d\u2019Instruction des Arm\u00e9es Percy, F-92190 Clamart, France"},{"name":"Ecole du Val-de-Gr\u00e2ce, Ecole de Sant\u00e9 des Arm\u00e9es, F-75005 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,1]]},"reference":[{"key":"ref_1","unstructured":"World-Health-Organization (2018). Noncommunicable Diseases Country Profiles 2018, WHO."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/TITB.2007.899496","article-title":"Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions","volume":"12","author":"Ermes","year":"2008","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"S327","DOI":"10.1111\/j.1532-5415.2007.01339.x","article-title":"Quality indicators for falls and mobility problems in vulnerable elders","volume":"55","author":"Chang","year":"2007","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0140-6736(96)01440-7","article-title":"Fall-related factors and risk of hip fracture: The EPIDOS prospective study","volume":"348","author":"Favier","year":"1996","journal-title":"Lancet"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jauhiainen, M., Puustinen, J., Mehrang, S., Ruokolainen, J., Holm, A., Vehkaoja, A., and Nieminen, H. (2019). Identification of motor symptoms related to parkinson disease using motion-tracking sensors at home (K\u00c4VELI): Protocol for an observational case-control study. JMIR Res. Protoc., 8.","DOI":"10.2196\/12808"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/phy2.150","article-title":"Validation of SenseWear Armband and ActiHeart monitors for assessments of daily energy expenditure in free-living women with chronic obstructive pulmonary disease","volume":"1","author":"Farooqi","year":"2013","journal-title":"Physiol. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1109\/JBHI.2013.2282617","article-title":"Characterization of physical activity in COPD patients: Validation of a robust algorithm for actigraphic measurements in living situations","volume":"18","author":"Perriot","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1249\/00005768-199301000-00012","article-title":"A simultaneous evaluation of 10 commonly used physical activity questionnaires","volume":"25","author":"Jacobs","year":"1993","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.neucli.2018.10.011","article-title":"Le rotagramme: une m\u00e9thode de repr\u00e9sentation du demi-tour bas\u00e9e sur des capteurs inertiels. Illustration sur une cohorte de patients post-AVC","volume":"48","author":"Barrois","year":"2018","journal-title":"Neurophysiol. Clin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"261","DOI":"10.3389\/fneur.2020.00261","article-title":"Personalized template-based step detection from inertial measurement units signals in multiple sclerosis","volume":"11","author":"Oudre","year":"2020","journal-title":"Front. Neurol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mantilla, J., Oudre, L., Barrois, R., Vienne, A., and Ricard, D. (2017, January 11\u201315). Template-DTW based on inertial signals: Preliminary results for step characterization. Proceedings of the International Conference of Engineering in Medicine and Biology Society (EMBC), Jeju, Korea.","DOI":"10.1109\/EMBC.2017.8037307"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Oudre, L., Barrois-M\u00fcller, R., Moreau, T., Truong, C., Vienne-Jumeau, A., Ricard, D., Vayatis, N., and Vidal, P.P. (2018). Template-based step detection with inertial measurement units. Sensors, 18.","DOI":"10.3390\/s18114033"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/S0747-5632(99)00037-0","article-title":"Detection of posture and motion by accelerometry: A validation study in ambulatory monitoring","volume":"15","author":"Foerster","year":"1999","journal-title":"Comput. Hum. Behav."},{"key":"ref_14","unstructured":"DeVaul, R.W., and Dunn, S. (2001, December 07). Real-time motion classification for wearable computing applications. MIT Technical Report, Available online: http:\/\/digitalmechanics.net\/realtime.pdf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/JBHI.2014.2313473","article-title":"Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people","volume":"19","author":"Chernbumroong","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_16","first-page":"224","article-title":"Wavelet based automated postural event detection and activity classification with single IMU","volume":"49","author":"Lockhart","year":"2013","journal-title":"Biomed. Sci. Instrum."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1249\/MSS.0000000000000840","article-title":"Hip and wrist accelerometer algorithms for free-living behavior classification","volume":"48","author":"Ellis","year":"2016","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1088\/0967-3334\/37\/3\/442","article-title":"Wavelet-based algorithm for auto-detection of daily living activities of older adults captured by multiple inertial measurement units (IMUs)","volume":"37","author":"Ayachi","year":"2016","journal-title":"Physiol. Meas."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Vienne, A., Barrois, R.P., Buffat, S., Ricard, D., and Vidal, P.P. (2017). Inertial sensors to assess gait quality in patients with neurological disorders: A systematic review of technical and analytical challenges. Front. Psychol., 8.","DOI":"10.3389\/fpsyg.2017.00817"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1109\/TBME.1981.324820","article-title":"Portable accelerometer device for measuring human energy expenditure","volume":"28","author":"Wong","year":"1981","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_21","first-page":"195","article-title":"Measurement of physical activity in population studies: A review","volume":"56","author":"Montoye","year":"1984","journal-title":"Hum. Biol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/TBME.1978.326372","article-title":"Triaxial vector accelerometry: A method for quantifying tremor and ataxia","volume":"25","author":"Frost","year":"1978","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_23","unstructured":"Reswick, J., Perry, J., Antonelli, D., Su, N., and Freeborn, C. (September, January 28). Preliminary evaluation of the vertical acceleration gait analyzer (VAGA). Proceedings of the 6th Annual Symposium External Control Extremities, Dubrovnik, Croatia."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1080\/02701367.2000.11082779","article-title":"Issues and future directions in assessing physical activity: An introduction to the conference proceedings","volume":"71","author":"Wood","year":"2000","journal-title":"Res. Q. Exerc. Sport"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bao, L., and Intille, S.S. (2004, January 18\u201319). Activity recognition from user-annotated acceleration data. Proceedings of the International Conference on Pervasive Computing, Berlin, Germany.","DOI":"10.1007\/978-3-540-24646-6_1"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dot, T., Quijoux, F., Oudre, L., Vienne-Jumeau, A., Moreau, A., Vidal, P.P., and Ricard, D. (2020). Non-linear template-based approach for the study of locomotion. Sensors, 20.","DOI":"10.3390\/s20071939"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1159\/000330046","article-title":"Validation of a compact motion sensor for the measurement of physical activity in patients with chronic obstructive pulmonary disease","volume":"83","author":"Sugino","year":"2012","journal-title":"Respiration"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vooijs, M., Alpay, L., Snoeck-Stroband, J., Beerthuizen, T., Siemonsma, P., Abbink, J., Sont, J., and R\u00f6vekamp, T. (2015). Validity and usability of low-cost accelerometers for internet-based self-monitoring of physical activity in patients with COPD. Interact. J. Med Res., 3.","DOI":"10.2196\/ijmr.3056"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1123\/jab.2013-0319","article-title":"Spatiotemporal gait patterns during overt and covert evaluation in patients with Parkinson\u2019s disease and healthy subjects: Is there a Hawthorne effect?","volume":"31","author":"Espinosa","year":"2015","journal-title":"J. Appl. Biomech."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.gaitpost.2016.11.024","article-title":"A model of free-living gait: A factor analysis in Parkinson\u2019s disease","volume":"52","author":"Morris","year":"2017","journal-title":"Gait Posture"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1093\/oxfordjournals.aje.a114121","article-title":"A validation of two motion sensors in the prediction of child and adult physical activity levels","volume":"122","author":"Klesges","year":"1985","journal-title":"Am. J. Epidemiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1249\/00005768-199010000-00023","article-title":"The Caltrac accelerometer as a physical activity monitor for school-age children","volume":"22","author":"Sallis","year":"1990","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1249\/MSS.0b013e3182a42a2d","article-title":"A method to estimate free-living active and sedentary behavior from an accelerometer","volume":"46","author":"Lyden","year":"2014","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s00421-006-0307-5","article-title":"Estimating energy expenditure using accelerometers","volume":"98","author":"Crouter","year":"2006","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"S547","DOI":"10.1097\/00005768-199911001-00010","article-title":"Physical activity in the treatment of the adulthood overweight and obesity: Current evidence and research issues","volume":"31","author":"Wing","year":"1999","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1055\/s-2003-37196","article-title":"Ability of different physical activity monitors to detect movement during treadmill walking","volume":"24","author":"Leenders","year":"2003","journal-title":"Int. J. Sport. Med."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/JBHI.2018.2820179","article-title":"Physical activity classification for elderly people in free-living conditions","volume":"23","author":"Awais","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1249\/MSS.0000000000002099","article-title":"Estimating sedentary time from a hip-and wrist-worn accelerometer","volume":"52","author":"Marcotte","year":"2020","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1089\/neu.2019.6450","article-title":"Analysis of free-living mobility in people with mild traumatic brain injury and healthy controls: Quality over quantity","volume":"37","author":"Stuart","year":"2020","journal-title":"J. Neurotrauma"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Carcreff, L., Gerber, C.N., Paraschiv-Ionescu, A., De Coulon, G., Newman, C.J., Aminian, K., and Armand, S. (2020). Comparison of gait characteristics between clinical and daily life settings in children with cerebral palsy. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-59002-6"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Alam, M.A.U., Roy, N., Holmes, S., Gangopadhyay, A., and Galik, E. (2016, January 27\u201329). Automated functional and behavioral health assessment of older adults with dementia. Proceedings of the 1st International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Washington, DC, USA.","DOI":"10.1109\/CHASE.2016.16"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1016\/j.medengphy.2016.07.008","article-title":"Can a single lower trunk body-fixed sensor differentiate between level-walking and stair descent and ascent in older adults? Preliminary findings","volume":"38","author":"Weiss","year":"2016","journal-title":"Med. Eng. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"345","DOI":"10.3233\/AIS-180493","article-title":"Combining wearable physiological and inertial sensors with indoor user localization network to enhance activity recognition","volume":"10","author":"Fiorini","year":"2018","journal-title":"J. Ambient. Intell. Smart Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1249\/MSS.0b013e3182351913","article-title":"Evaluation of activity monitors in controlled and free-living environments","volume":"44","author":"Feito","year":"2012","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s11556-013-0132-x","article-title":"Accelerometry analysis of physical activity and sedentary behavior in older adults: A systematic review and data analysis","volume":"11","author":"Gorman","year":"2014","journal-title":"Eur. Rev. Aging Phys. Act."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1177\/0269215508090675","article-title":"Wearable systems for monitoring mobility-related activities in older people: A systematic review","volume":"22","author":"Hartmann","year":"2008","journal-title":"Clin. Rehabil."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.cct.2016.01.006","article-title":"Measuring free-living physical activity in COPD patients: Deriving methodology standards for clinical trials through a review of research studies","volume":"47","author":"Byrom","year":"2016","journal-title":"Contemp. Clin. Trials"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Tedesco, S., Barton, J., and O\u2019Flynn, B. (2017). A review of activity trackers for senior citizens: Research perspectives, commercial landscape and the role of the insurance industry. Sensors, 17.","DOI":"10.3390\/s17061277"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.ypmed.2008.12.001","article-title":"Review of physical activity measurement using accelerometers in older adults: Considerations for research design and conduct","volume":"48","author":"Murphy","year":"2009","journal-title":"Prev. Med."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"de Oliveira Gondim, I.T.G., de Souza, C.d.C.B., Rodrigues, M.A.B., Azevedo, I.M., de Sales, M.d.G.W., and Lins, O.G. (2020). Portable accelerometers for the evaluation of spatio-temporal gait parameters in people with Parkinson\u2019s disease: An integrative review. Arch. Gerontol. Geriatr., 90.","DOI":"10.1016\/j.archger.2020.104097"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1007\/s11910-019-0997-9","article-title":"Next steps in wearable technology and community ambulation in multiple sclerosis","volume":"19","author":"Frechette","year":"2019","journal-title":"Curr. Neurol. Neurosci. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7772","DOI":"10.3390\/s100807772","article-title":"A review of accelerometry-based wearable motion detectors for physical activity monitoring","volume":"10","author":"Yang","year":"2010","journal-title":"Sensors"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"31314","DOI":"10.3390\/s151229858","article-title":"Physical human activity recognition using wearable sensors","volume":"15","author":"Attal","year":"2015","journal-title":"Sensors"},{"key":"ref_54","unstructured":"Narayanan, A., Mackay, L., and Stewart, T. (2019). Application of Machine Learning in the Measurement of Free-Living Physical Activity Behaviours Human Potential Centre Supervisors, Auckland University of Technology. Technical Report."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1007\/s00391-013-0559-8","article-title":"Fall detection with body-worn sensors","volume":"46","author":"Schwickert","year":"2013","journal-title":"Z. F\u00dcR Gerontol. Und Geriatr."},{"key":"ref_56","first-page":"438","article-title":"Measuring physical activity using triaxial wrist worn polar activity trackers: A systematic review","volume":"13","author":"Henriksen","year":"2020","journal-title":"Int. J. Exerc. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yang, Z., and Dong, T. (2017). A review of wearable technologies for elderly care that can accurately track indoor position, recognize physical activities and monitor vital signs in real time. Sensors, 17.","DOI":"10.3390\/s17020341"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1007\/s40279-018-0878-4","article-title":"Wearable inertial sensor systems for lower limb exercise detection and evaluation: A systematic review","volume":"48","author":"Caulfield","year":"2018","journal-title":"Sport. Med."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.gaitpost.2019.07.123","article-title":"Quantification of the validity and reliability of sprint performance metrics computed using inertial sensors: A systematic review","volume":"73","author":"Macadam","year":"2019","journal-title":"Gait Posture"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.medengphy.2018.04.008","article-title":"Detection of daily postures and walking modalities using a single chest-mounted tri-axial accelerometer","volume":"57","author":"Nazarahari","year":"2018","journal-title":"Med. Eng. Phys."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Cajamarca, G., Rodr\u00edguez, I., Herskovic, V., Campos, M., and Riofr\u00edo, J.C. (2018). StraightenUp+: Monitoring of posture during daily activities for older persons using wearable sensors. Sensors, 18.","DOI":"10.3390\/s18103409"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1111\/sms.13017","article-title":"Reliable recognition of lying, sitting, and standing with a hip-worn accelerometer","volume":"28","author":"Husu","year":"2018","journal-title":"Scand. J. Med. Sci. Sport."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s12984-017-0241-2","article-title":"Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson\u2019s disease using multiple inertial sensors","volume":"14","author":"Nguyen","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1249\/MSS.0000000000001291","article-title":"Ensemble methods for classification of physical activities from wrist accelerometry","volume":"49","author":"Chowdhury","year":"2017","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s12966-019-0835-0","article-title":"Comparison of physical behavior estimates from three different thigh-worn accelerometers brands: A proof-of-concept for the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS)","volume":"16","author":"Crowley","year":"2019","journal-title":"Int. J. Behav. Nutr. Phys. Act."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1186\/s12984-018-0456-x","article-title":"Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy","volume":"15","author":"Ahmadi","year":"2018","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2269","DOI":"10.1088\/0967-3334\/35\/11\/2269","article-title":"A comparison of activity classification in younger and older cohorts using a smartphone","volume":"35","author":"Wang","year":"2014","journal-title":"Physiol. Meas."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s12984-020-0655-0","article-title":"Quantifying dosage of physical therapy using lower body kinematics: A longitudinal pilot study on early post-stroke individuals","volume":"17","author":"Shin","year":"2020","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1183\/09031936.00134312","article-title":"Validity of physical activity monitors during daily life in patients with COPD","volume":"42","author":"Rabinovich","year":"2013","journal-title":"Eur. Respir. J."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/TNSRE.2017.2745418","article-title":"Using inertial sensors to automatically detect and segment activities of daily living in people with parkinson\u2019s disease","volume":"26","author":"Nguyen","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Leavy, B., L\u00f6fgren, N., Nilsson, M., and Franz\u00e9n, E. (2018). Patient-reported and performance-based measures of walking in mild\u2013moderate Parkinson\u2019s disease. Brain Behav., 8.","DOI":"10.1002\/brb3.1081"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s12984-016-0154-5","article-title":"Free-living gait characteristics in ageing and Parkinson\u2019s disease: Impact of environment and ambulatory bout length","volume":"13","author":"Godfrey","year":"2016","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1016\/j.jsams.2017.04.017","article-title":"Comparability and feasibility of wrist- and hip-worn accelerometers in free-living adolescents","volume":"20","author":"Scott","year":"2017","journal-title":"J. Sci. Med. Sport"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1080\/17461391.2011.643925","article-title":"Comparison of three models of actigraph accelerometers during free living and controlled laboratory conditions","volume":"13","author":"Lee","year":"2013","journal-title":"Eur. J. Sport Sci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/1479-5868-10-22","article-title":"Using wearable cameras to categorise type and context of accelerometer-identified episodes of physical activity","volume":"10","author":"Doherty","year":"2013","journal-title":"Int. J. Behav. Nutr. Phys. Act."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1249\/MSS.0b013e31823bf95c","article-title":"Physical activity classification using the GENEA wrist-worn accelerometer","volume":"44","author":"Zhang","year":"2012","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1007\/s11517-015-1357-9","article-title":"Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different","volume":"54","author":"Brodie","year":"2016","journal-title":"Med Biol. Eng. Comput."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jsams.2016.06.003","article-title":"Field evaluation of a random forest activity classifier for wrist-worn accelerometer data","volume":"20","author":"Pavey","year":"2017","journal-title":"J. Sci. Med. Sport"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s40798-019-0191-2","article-title":"Prehabilitation and acute postoperative physical activity in patients undergoing radical prostatectomy: A secondary analysis from an RCT","volume":"5","author":"Au","year":"2019","journal-title":"Sport. Med. Open"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Knaier, R., H\u00f6chsmann, C., Infanger, D., Hinrichs, T., and Schmidt-Trucks\u00e4ss, A. (2019). Validation of automatic wear-time detection algorithms in a free-living setting of wrist-worn and hip-worn ActiGraph GT3X+. BMC Public Health, 19.","DOI":"10.1186\/s12889-019-6568-9"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Wang, W., and Adamczyk, P.G. (2019). Analyzing gait in the real world using wearable movement sensors and frequently repeated movement paths. Sensors, 19.","DOI":"10.3390\/s19081925"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Barth, J., Oberndorfer, C., Kugler, P., Schuldhaus, D., Winkler, J., Klucken, J., and Eskofier, B. (2013, January 3\u20137). Subsequence dynamic time warping as a method for robust step segmentation using gyroscope signals of daily life activities. Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6611104"},{"key":"ref_83","unstructured":"Tran, K., Le, T., and Dinh, T. (2012, January 12\u201315). A high-accuracy step counting algorithm for iPhones using Accelerometer. Proceedings of the International Symposium on Signal Processing and Information Technology, ISSPIT, Saigon, Vietnam."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1249\/MSS.0000000000001578","article-title":"Improving Hip-Worn accelerometer estimates of sitting using machine learning methods","volume":"50","author":"Kerr","year":"2018","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1249\/MSS.0000000000000841","article-title":"Objective assessment of physical activity: Classifiers for public health","volume":"48","author":"Kerr","year":"2016","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Garcia-Gonzalez, D., Rivero, D., Fernandez-Blanco, E., and Luaces, M.R. (2020). A public domain dataset for real-life human activity recognition using smartphone sensors. Sensors, 20.","DOI":"10.3390\/s20082200"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"266","DOI":"10.2174\/1874609808666150416121011","article-title":"Differentiating walking from other activities of daily living in older adults using wrist-based accelerometers","volume":"8","author":"Papadopoulos","year":"2015","journal-title":"Curr. Aging Sci."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Leutheuser, H., Schuldhaus, D., and Eskofier, B.M. (2013). Hierarchical, multi-sensor based classification of daily life activities: Comparison with state-of-the-art algorithms using a benchmark dataset. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0075196"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"57","DOI":"10.3389\/fbioe.2018.00057","article-title":"Longitudinal walking analysis in hemiparetic patients using wearable motion sensors: Is there convergence between body sides?","volume":"6","author":"Derungs","year":"2018","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Derungs, A., Schuster-Amft, C., and Amft, O. (2018). Physical activity comparison between body sides in hemiparetic patients using wearable motion sensors in free-living and therapy: A case series. Front. Bioeng. Biotechnol., 6.","DOI":"10.3389\/fbioe.2018.00136"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Straczkiewicz, M., Glynn, N.W., and Harezlak, J. (2019). On placement, location and orientation of wrist-worn tri-axial accelerometers during free-living measurements. Sensors, 19.","DOI":"10.3390\/s19092095"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/10.554760","article-title":"A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity","volume":"44","author":"Bouten","year":"1997","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"5290","DOI":"10.1109\/JSEN.2017.2722105","article-title":"Recognizing human activity in free-living using multiple body-worn accelerometers","volume":"17","author":"Fullerton","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/j.medengphy.2014.02.012","article-title":"Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems","volume":"36","author":"Gao","year":"2014","journal-title":"Med. Eng. Phys."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITB.2005.856864","article-title":"Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring","volume":"10","author":"Karantonis","year":"2006","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1109\/TNSRE.2010.2070807","article-title":"Barometric pressure and triaxial accelerometry-based falls event detection","volume":"18","author":"Bianchi","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"12852","DOI":"10.3390\/s131012852","article-title":"Real-time human ambulation, activity, and physiological monitoring: Taxonomy of issues, techniques, applications, challenges and limitations","volume":"13","author":"Khusainov","year":"2013","journal-title":"Sensors"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"M\u00fcnch, M., Weibel, R., Sofios, A., Huang, H., Infanger, D., Portegijs, E., Giannouli, E., Mundwiler, J., Conrow, L., and Rantanen, T. (2019). MOBIlity assessment with modern TEChnology in older patients\u2019 real-life by the General Practitioner: The MOBITEC-GP study protocol. BMC Public Health, 19.","DOI":"10.1186\/s12889-019-8069-2"},{"key":"ref_99","unstructured":"Clements, C.M., Buller, M.J., Welles, A.P., and Tharion, W.J. (September, January 28). Real time gait pattern classification from chest worn accelerometry during a loaded road march. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (EMBS), San Diego, CA, USA."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Hu, M., Li, W., Li, L., Houston, D., and Wu, J. (2016). Refining time-activity classification of human subjects using the global positioning system. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0148875"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MPRV.2014.73","article-title":"Sensor placement variations in wearable activity recognition","volume":"13","author":"Kunze","year":"2014","journal-title":"IEEE Pervasive Comput."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.gaitpost.2014.10.020","article-title":"The impact of testing protocol on recorded gait speed","volume":"41","author":"Sustakoski","year":"2015","journal-title":"Gait Posture"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Rehman, R.Z.U., Del Din, S., Shi, J.Q., Galna, B., Lord, S., Yarnall, A.J., Guan, Y., and Rochester, L. (2019). Comparison of walking protocols and gait assessment systems for machine learning-based classification of parkinson\u2019s disease. Sensors, 19.","DOI":"10.3390\/s19245363"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Awais, M., Palmerini, L., Bourke, A.K., Ihlen, E.A., Helbostad, J.L., and Chiari, L. (2016). Performance evaluation of state of the art systems for physical activity classification of older subjects using inertial sensors in a real life scenario: A benchmark study. Sensors, 16.","DOI":"10.3390\/s16122105"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"9183","DOI":"10.3390\/s130709183","article-title":"Optimal placement of accelerometers for the detection of everyday activities","volume":"13","author":"Cleland","year":"2013","journal-title":"Sensors"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/s12984-015-0060-2","article-title":"Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients","volume":"12","author":"Gonzenbach","year":"2015","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1249\/MSS.0000000000000844","article-title":"Performance of activity classification algorithms in free-living older adults","volume":"48","author":"Sasaki","year":"2016","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1109\/TITB.2010.2051955","article-title":"A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer","volume":"14","author":"Khan","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"4405","DOI":"10.1007\/s11042-015-3177-1","article-title":"A survey of depth and inertial sensor fusion for human action recognition","volume":"76","author":"Chen","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.gaitpost.2018.08.024","article-title":"Validation of an accelerometer for measurement of activity in frail older people","volume":"66","author":"Chigateri","year":"2018","journal-title":"Gait Posture"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Ahmadi, M.N., O\u2019neil, M.E., Baque, E., Boyd, R.N., and Trost, S.G. (2020). Machine learning to quantify physical activity in children with cerebral palsy: Comparison of group, group-personalized, and fully-personalized activity classification models. Sensors, 20.","DOI":"10.3390\/s20143976"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1016\/0895-7061(95)00151-E","article-title":"White coat hypertension and white coat effect similarities and differences","volume":"8","author":"Verdecchia","year":"1995","journal-title":"Am. J. Hypertens."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1016\/j.medengphy.2015.06.010","article-title":"Influence of contextual task constraints on preferred stride parameters and their variabilities during human walking","volume":"37","author":"Ojeda","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Smith, Z.J., Chu, K., Espenson, A.R., Rahimzadeh, M., Gryshuk, A., Molinaro, M., Dwyre, D.M., Lane, S., Matthews, D., and Wachsmann-Hogiu, S. (2011). Cell-phone-based platform for biomedical device development and education applications. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0017150"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"H\u00f6chsmann, C., Knaier, R., Infanger, D., and Schmidt-Trucks\u00e4ss, A. (2020). Validity of smartphones and activity trackers to measure steps in a free-living setting over three consecutive days. Physiol. Meas., 41.","DOI":"10.1088\/1361-6579\/ab635f"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Dominick, G.M., Winfree, K.N., Pohlig, R.T., and Papas, M.A. (2016). Physical activity assessment between consumer-and research-grade accelerometers: A comparative study in free-living conditions. JMIR mHealth uHealth, 4.","DOI":"10.2196\/mhealth.6281"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1002\/acr2.11099","article-title":"Comparison of physical activity measures derived from the Fitbit Flex and the ActiGraph GT3X+ in an employee population with chronic knee symptoms","volume":"2","author":"Semanik","year":"2020","journal-title":"ACR Open Rheumatol."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Burton, E., Hill, K.D., Lautenschlager, N.T., Th\u00f8gersen-Ntoumani, C., Lewin, G., Boyle, E., and Howie, E. (2018). Reliability and validity of two fitness tracker devices in the laboratory and home environment for older community-dwelling people. BMC Geriatr., 18.","DOI":"10.1186\/s12877-018-0793-4"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Alinia, P., Cain, C., Fallahzadeh, R., Shahrokni, A., Cook, D., and Ghasemzadeh, H. (2017). How accurate is your activity tracker? A comparative study of step counts in low-intensity physical activities. JMIR mHealth uHealth, 5.","DOI":"10.2196\/mhealth.6321"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1080\/17461391.2016.1255261","article-title":"How valid are wearable physical activity trackers for measuring steps?","volume":"17","author":"An","year":"2017","journal-title":"Eur. J. Sport Sci."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"381","DOI":"10.5201\/ipol.2019.265","article-title":"A data set for the study of human locomotion with inertial measurements units","volume":"9","author":"Truong","year":"2019","journal-title":"Image Process. Line"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","article-title":"Deep learning for sensor-based activity recognition: A survey","volume":"119","author":"Wang","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Steven Eyobu, O., and Han, D.S. (2018). Feature representation and data augmentation for human activity classification based on wearable IMU sensor data using a deep LSTM neural network. Sensors, 18.","DOI":"10.3390\/s18092892"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Gil-Mart\u00edn, M., San-Segundo, R., Fern\u00e1ndez-Mart\u00ednez, F., and Ferreiros-L\u00f3pez, J. (2020). Improving physical activity recognition using a new deep learning architecture and post-processing techniques. Eng. Appl. Artif. Intell., 92.","DOI":"10.1016\/j.engappai.2020.103679"},{"key":"ref_125","first-page":"1","article-title":"Stationary exercise classification using IMUs and deep learning","volume":"3","author":"Heroy","year":"2020","journal-title":"SMU Data Sci. Rev."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/19\/5625\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:15:40Z","timestamp":1760177740000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/19\/5625"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,1]]},"references-count":125,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["s20195625"],"URL":"https:\/\/doi.org\/10.3390\/s20195625","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,1]]}}}