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A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring
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Characteristics of physical activity are indicative of one’s mobility level, latent chronic diseases and aging process. Accelerometers have been widely accepted as useful and practical sensors for wearable devices to measure and assess physical activity. This paper reviews the development of wearable accelerometry-based motion detectors. The principle of accelerometry measurement, sensor properties and sensor placements are first introduced. Various research using accelerometry-based wearable motion detectors for physical activity monitoring and assessment, including posture and movement classification, estimation of energy expenditure, fall detection and balance control evaluation, are also reviewed. Finally this paper reviews and compares existing commercial products to provide a comprehensive outlook of current development status and possible emerging technologies.
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“A centrally located sensor on the torso (embedded into vests, mounted by straps or attached to the skin) is best suited for detecting posture, acceleration and whole-body movements and offers the least errors compared with other body locations7.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [7]
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“These devices were selected based on review of sales data in 3 categories of wearable devices—smart watches, basic wearable bands, and portable navigation devices [13-16].”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [14]
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Cites
The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons
Podsiadlo, D., & Richardson, S. (1991) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.1991.tb01616.x · Cited by 14,418
(1985) · Europe PMC (PubMed Central) · Cited by 9,143
Measuring balance in the elderly: preliminary development of an instrument
Berg, K. (1989) · Physiotherapy Canada · doi:10.3138/ptc.41.6.304 · Cited by 2,746
THE MAJOR DETERMINANTS IN NORMAL AND PATHOLOGICAL GAIT
SAUNDERS, J. B. D. M., Inman, V. T., & Eberhart, H. D. (1953) · Journal of Bone and Joint Surgery · doi:10.2106/00004623-195335030-00003 · Cited by 1,303
Clinical and laboratory measures of postural balance in an elderly population.
Berg, K., Maki, B. E., Williams, J. I., Holliday, P. J., & Wood-Dauphinée, S. (1992) · PubMed · Cited by 1,251
Karantonis, D. M., Narayanan, M. R., Mathie, M. J., Lovell, N. H., & Celler, B. G. (2006) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2005.856864 · Cited by 1,239
The Technology of Accelerometry-Based Activity Monitors: Current and Future
CHEN, K. Y., & BASSETT, D. R. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000185571.49104.82 · Cited by 996
A Physiological Profile Approach to Falls Risk Assessment and Prevention
Lord, S. R., Menz, H. B., & Tiedemann, A. (2003) · Physical Therapy · doi:10.1093/ptj/83.3.237 · Cited by 921
Bouten, C. V. C., Koekkoek, K. K., Verduin, M., Kodde, R., & Janssen, J. J. (1997) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.554760 · Cited by 911
A physiological profile approach to falls risk assessment and prevention
Lord, S. R., Sherrington, C., Menz, H. B., & Close, J. (2007) · Cambridge University Press eBooks · doi:10.1017/cbo9780511722233.018 · Cited by 891
Research works printed but not linked: 2
- Park, 2006, Validation of the AMP331 monitor for assessing energy expenditure of free-living physical activity, Res. Quart. Exerc. Sport, 77, A40
- Sherrington, C (2000). The Effect of Exercise on Physical Ability Following Fall-Related Hip fracture, PhD thesis,.
Cited by
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Ishida, T., Kakushima, K., & Fujita, H. (2013) · Journal of Microelectromechanical Systems · doi:10.1109/jmems.2013.2248123 · Cited by 19
Wearable step counting using a force myography-based ankle strap
Chu, K. H. T., Jiang, X., & Menon, C. (2017) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/2055668317746307 · Cited by 19
McGill, A., Houston, S., & Lee, R. (2018) · Arts & Health · doi:10.1080/17533015.2018.1443947 · Cited by 19
Hoyt, C. R., Brown, S., Sherman, S. K., Wood-Smith, M., Van, A. N., Ortega, M., Nguyen, A. L., Lang, C. E., Schlaggar, B. L., & Dosenbach, N. U. F. (2019) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2019.103546 · Cited by 20
Ferrari, G., Marques, A., Barreira, T., Kovalskys, I., Gómez, G., Rigotti, A., Cortés, L., García, M. C. Y., Pareja, R. G., Herrera-Cuenca, M., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18094641 · Cited by 17
Moghaddam, G. K., & Lowe, C. R. (2018) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-030-01557-2_2 · Cited by 21
Bonilla, D. A., Peralta-Alzate, J. O., Bonilla-Henao, J. A., Cannataro, R., Cardozo, L. A., Vargas-Molina, S., Stout, J. R., Kreider, R. B., & Petro, J. L. (2023) · Journal of Functional Morphology and Kinesiology · doi:10.3390/jfmk8020044 · Cited by 24
Cai, X., Han, G., Song, X., & Wang, J. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2653246 · Cited by 18
Emerging NUI-Based Methods for User Authentication: A New Taxonomy and Survey
Sae-Bae, N., Wu, J., Memon, N. D., Konrad, J., & Ishwar, P. (2019) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2019.2893297 · Cited by 18
de Müllenheim, P., Chaudru, S., Mahé, G., Prioux, J., & Faucheur, A. L. (2015) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.12512 · Cited by 18
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Howard, R. M., Conway, R., & Harrison, A. J. (2016) · Sports Biomechanics · doi:10.1080/14763141.2016.1174289 · Cited by 18
Reliability of accelerometric assessment of balance in children aged 6–12 years
García-Liñeira, J., Soidán, J. L. G., Romo-Pérez, V., & Leirós‐Rodríguez, R. (2020) · BMC Pediatrics · doi:10.1186/s12887-020-02073-1 · Cited by 18
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Luna, J. C., Rocha, J. M., Monacelli, É., Foggea, G., Hirata, Y., & Delaplace, S. (2022) · Sensors · doi:10.3390/s22114221 · Cited by 17
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Weich, C., & Vieten, M. (2020) · Sensors · doi:10.3390/s20143810 · Cited by 17
Corrà, M. F., Warmerdam, E., Vila‐Chã, N., Maetzler, W., & Maia, L. F. (2020) · Sensors · doi:10.3390/s20226627 · Cited by 17
Staranowicz, A. N., Ray, C. T., & Mariottini, G.-L. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319513 · Cited by 17
Gait oscillation analysis during gait and stair-stepping in elder patients with knee osteoarthritis
Mine, T., Kajino, M., Sato, J., Itou, S., Ihara, K., Kawamura, H., Kuriyama, R., & Tominaga, Y. (2019) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-019-1064-6 · Cited by 17
Acker, B. B. V., Conradie, P. D., Vlerick, P., & Saldien, J. (2020) · Cognition Technology & Work · doi:10.1007/s10111-020-00633-0 · Cited by 18
Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review
Lim, Y. T., Yi, W., & Wang, H. (2024) · Applied Sciences · doi:10.3390/app142210605 · Cited by 16
Woll, S., Birkenmaier, D., Biri, G. I., Nissen, R., Lutz, L., Schroth, M., Ebner‐Priemer, U., & Giurgiu, M. (2025) · JMIR mhealth and uhealth · doi:10.2196/59660 · Cited by 21
Classification of physical activities using wearable sensors
Arif, M., Kattan, A. J., & Ahamed, S. I. (2015) · Intelligent Automation & Soft Computing · doi:10.1080/10798587.2015.1118275 · Cited by 17
Wireless Sensor Devices in Sports Performance
Howard, R. M. (2016) · IEEE Potentials · doi:10.1109/mpot.2015.2501679 · Cited by 16
Miranda-Duro, M. D. C., Nieto-Riveiro, L., Concheiro-Moscoso, P., Groba, B., Pousada, T., Canosa, N., & Pereira, J. (2021) · Sensors · doi:10.3390/s21103341 · Cited by 16
Wearable Weight Estimation System
Hellström, P. A. R., Folke, M., & Ekstrom, M. C. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.08.475 · Cited by 16
Development of MATLAB Kinect Skeletal Tracking System (MKSTS) for gait analysis
Abiddin, W. Z. W. Z., Jailani, R., Omar, A. R., & Yassin, A. I. M. (2016) · IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) · doi:10.1109/iscaie.2016.7575066 · Cited by 16
The relationship between movement speed and duration during soccer matches
Röecker, K., Mahler, H., Heyde, C., Roell, M., & Gollhofer, A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181781 · Cited by 16
Biofeedback Methodology: a Narrative Review
Vasilyev, V., Борисов, В., & Syskov, A. (2019) · International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) · doi:10.1109/sibircon48586.2019.8958019 · Cited by 17
Exploiting accelerometers to estimate displacement
Ferrero, R., Gandino, F., Hemmatpour, M., Montrucchio, B., & Rebaudengo, M. (2016) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco.2016.7525741 · Cited by 15
on behalf of the HDFIT Study Investigators, Pecoits‐Filho, R., Larkin, J. W., Poli‐de‐Figueiredo, C. E., Neto, A. L. C., Barra, A. B. L., Canhada, S., de Campos, L. G., Woehl, J., Gonçalves, P. B., et al. (2019) · BMC Nephrology · doi:10.1186/s12882-019-1247-8 · Cited by 15
Smartphones within Psychological Science
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408 · Cited by 14
Jeon, H., Kim, S. L., Kim, S., & Lee, D. (2020) · Sensors · doi:10.3390/s20174996 · Cited by 16
Zimmo, L., Almudahka, F., Ibrahim, I. A. E., Al‐Kuwari, M. G., & Farooq, A. (2019) · Journal of Teaching in Physical Education · doi:10.1123/jtpe.2018-0351 · Cited by 15
IMU-Based quantitative assessment of stroke from gait
Sun, Y., Song, Z., Mo, L., Li, B., Liang, F., Yin, M., & Wang, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-94167-y · Cited by 14
Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking
Haghi, M., Ershadi, A., & Deserno, T. M. (2023) · Sensors · doi:10.3390/s23042066 · Cited by 14
Orientation Measurement Based on Magnetic Inductance by the Extended Distributed Multi-Pole Model
Wu, F., Moon, S. K., & Son, H. (2014) · Sensors · doi:10.3390/s140711504 · Cited by 14
Reha@Home - A Vision Based Markerless GAIT Analysis System for Rehabilitation at Home
Natarajan, S. K., Wang, X., Spranger, M., & Gräser, A. (2017) · Biomedical Engineering · doi:10.2316/p.2017.852-042 · Cited by 14
Validity and reliability of a tool for accelerometric assessment of static balance in women
Leirós‐Rodríguez, R., Romo-Pérez, V., & Soidán, J. L. G. (2017) · European Journal of Physiotherapy · doi:10.1080/21679169.2017.1347707 · Cited by 14
Efficient Bluetooth Low Energy Operation for Low Duty Cycle Applications
Liendo, A., Morche, D., Guizzetti, R., & Rousseau, F. S. (2018) · International Conference on Communications · doi:10.1109/icc.2018.8423011 · Cited by 14
Chowdhury, D., & Chattopadhyay, M. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.2984142 · Cited by 14
Droghini, D., Ferretti, D., Principi, E., Squartini, S., & Piazza, F. (2018) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-95095-2_18 · Cited by 14
Goel, R. K., Danoff, J. R., Petrera, M., Blevins, K. M., Purtill, J. J., & Chen, A. F. (2019) · Journal of the American Academy of Orthopaedic Surgeons · doi:10.5435/jaaos-d-18-00319 · Cited by 14
Marin, F., Lepetit, K., Fradet, L., Hansen, C., & Ben-Mansour, K. (2020) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2020.109834 · Cited by 13
MEMS accelerometers and their bio-applications
Trifunovic, M., Vadiraj, A. M., & van Driel, W. D. (2012) · International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) · doi:10.1109/esime.2012.6191749 · Cited by 14
Ruiz-Zafra, A., Orantes-González, E., Noguera, M., Benghazi, K., & Heredia-Jimenez, J. (2015) · Sensors · doi:10.3390/s150818270 · Cited by 13
Flexible circuits engineered for complex and extreme environments
Chen, Y., Liang, M., Tian, M., Lu, X., Wang, W., Xu, J., & You, R. (2025) · Soft Science · doi:10.20517/ss.2025.69 · Cited by 14
Current and Potential Applications of Wearables in Sports Cardiology
Rao, P., Seshadri, D. R., & Hsu, J. J. (2021) · Current Treatment Options in Cardiovascular Medicine · doi:10.1007/s11936-021-00942-1 · Cited by 18
Vincent, A. C., Furman, H., Slepian, R. C., Ammann, K. R., Maria, C. D., Chien, J. H., Siu, K., & Slepian, M. J. (2022) · Applied Sciences · doi:10.3390/app12126173 · Cited by 13
A survey of wearable devices to capture human factors for human-robot collaboration
Sarvghadi, H., Reinhardt, A., & Semmelhack, E. A. (2025) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2025.102048 · Cited by 13
Haescher, M., Trimpop, J., Matthies, D. J. C., Bieber, G., Urban, B., & Kirste, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20916-6_68 · Cited by 13
Triantafyllou, A. I., Papagiannis, G. I., Stasi, S., Bakalidou, D., Kyriakidou, M., Papathanasiou, G., Papadopoulos, E. C., Papagelopoulos, P. J., & Koulouvaris, P. (2022) · Biology · doi:10.3390/biology11030398 · Cited by 13
Cai, Y., Wang, Z., Zhang, W., Kong, W., Jiang, J., Zhao, R., Wang, D., Feng, L., & Ni, G. (2022) · Sensors · doi:10.3390/s22134661 · Cited by 13
Blood Pressure Drop Prediction by using HRV Measurements in Orthostatic Hypotension
Sannino, G., Melillo, P., Stranges, S., Pietro, G. D., & Pecchia, L. (2015) · Journal of Medical Systems · doi:10.1007/s10916-015-0292-5 · Cited by 13
Short term Heart Rate Variability to predict blood pressure drops due to standing: a pilot study
Sannino, G., Melillo, P., Stranges, S., Pietro, G. D., & Pecchia, L. (2015) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-15-s3-s2 · Cited by 13
Peruzzini, M., Grandi, F., Pellicciari, M., & Campanella, C. E. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-94706-8_44 · Cited by 13
Silver Nanowire-Based Flexible Transparent Composite Film for Curvature Measurements
Xu, W., Lü, Z., Xu, F., Shen, W., Song, W., & Chou, S. (2018) · ACS Applied Nano Materials · doi:10.1021/acsanm.8b00620 · Cited by 13
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Sqalli, M. T., & Al‐Thani, D. (2020) · IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · doi:10.1109/iciot48696.2020.9089538 · Cited by 13
Accelerometer-Based Activity Recognition of Workers at Construction Sites
Gondo, T., & Miura, R. (2020) · Frontiers in Built Environment · doi:10.3389/fbuil.2020.563353 · Cited by 13
HulaMove: Using Commodity IMU for Waist Interaction
Xu, X., Li, J., Yuan, T., He, L., Liu, X., Yan, Y., Wang, Y., Shi, Y., Mankoff, J. C., & Dey, A. K. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445182 · Cited by 14
Gait analysis: overview, trends, and challenges
Junior, A. G. L., & Frizera, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-385952-3.00011-1 · Cited by 12
Demeke, A. Y., Heravi, M. Y., Dola, I. S., Jang, Y., Le, C., Jeong, I., Lin, Z., & Wang, D. (2024) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981241265845 · Cited by 12
Melillo, P., Jović, A., LUCA, N. D., Morgan, S. P., & Pecchia, L. (2014) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-11128-5_11 · Cited by 12
Review of innovative models of aged care
Dyer, S. M., van den Berg, M., Brown, A., Johnstone, G., Laver, K., Lowthian, J. A., Maeder, A., Meyer, C., Moores, C. J., Ogrin, R., et al. (2020) · Cited by 12
Bovim, L. P., Gjesdal, B. E., Mæland, S., Aaslund, M. K., & Bogen, B. E. (2020) · Gait & Posture · doi:10.1016/j.gaitpost.2020.01.031 · Cited by 12
Wearable Device for Yogic Breathing with Real-Time Heart Rate and Posture Monitoring
Puranik, A., Kanthi, M., & Nayak, A. V. (2021) · Journal of Medical Signals & Sensors · doi:10.4103/jmss.jmss_54_20 · Cited by 12
Fall detection algorithm based on triaxial accelerometer data
Dumitrache, M., & Pasca, S. (2013) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb.2013.6707268 · Cited by 12
Real-time gait cycle parameters recognition using a wearable motion detector
Yang, C.-C., Hsu, Y., Lu, J.-M., Shih, K., & Chan, L. (2011) · International Conference on System Science and Engineering · doi:10.1109/icsse.2011.5961954 · Cited by 12
Self-citationSalvador, E. P., Ribeiro, E. H. C., García, L., Andrade, D. R., Guimarães, V. V., Aoki, M. S., & Florindo, A. A. (2014) · Archives of Public Health · doi:10.1186/2049-3258-72-8 · Cited by 13
Enhancing activity recognition by fusing inertial and biometric information
Martín, H., Bernardos, A. M., Tarrío, P., & Casar, J. R. (2011) · Hispana · Cited by 12
Trendy v pohybovém chování českých dětí a adolescentů
Sigmundová, D., & Sigmund, E. (2015) · doi:10.5507/ftk.15.24448398 · Cited by 13
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Kano, S., & Mekaru, H. (2020) · Microsystem Technologies · doi:10.1007/s00542-020-04889-4 · Cited by 12
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Hribernik, M., Keš, E., Umek, A., & Kos, A. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.04.082 · Cited by 12
Joint Pedestrian Motion State and Device Pose Classification
Kasebzadeh, P., Radnosrati, K., Hendeby, G., & Gustafsson, F. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2958005 · Cited by 12
Sensing human activity to improve sedentary lifestyle
Boerema, S. (2018) · doi:10.3990/1.9789036546041 · Cited by 12
Gait analysis by using electric signals from a triboelectric nanogenerator
Gulahmadov, O., Muradov, M., & Kim, J. (2022) · Engineering Research Express · doi:10.1088/2631-8695/ac8529 · Cited by 11
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Freddi, F., Mingazzi, L., Pozzi, E., & Aresi, N. (2023) · Sensors · doi:10.3390/s23208379 · Cited by 16
Bao, T., Gao, J., Wang, J., Yang, C., Xu, F., Qiao, G., & Li, F. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2023.1265543 · Cited by 18
Self-citationAccelerometer‐based heart rate adjustment for ambulatory stress research
van de Ven, S. R. B., Gevonden, M. J., Noordzij, M. L., & de Geus, E. J. C. (2024) · Psychophysiology · doi:10.1111/psyp.14721 · Cited by 11
Sheryl Oliver, A., Anuradha, M., Justus, J. J., & Maheshwari, N. (2018) · Biomedical Research - India · doi:10.4066/biomedicalresearch.29-18-1137 · Cited by 11
Kwon, S., Wan, N., Burns, R. D., Brusseau, T. A., Kim, Y., Kumar, S., Ertin, E., Wetter, D. W., Lam, C. Y., Wen, M., et al. (2021) · Sensors · doi:10.3390/s21041411 · Cited by 14
Feldner, H. A., Papazian, C., Peters, K. M., Creutzfeldt, C. J., & Steele, K. M. (2021) · Applied System Innovation · doi:10.3390/asi4020032 · Cited by 11
Frodi, D. M., Kolk, M. Z. H., Langford, J., Andersen, T. O., Knops, R. E., Tan, H. L., Svendsen, J. H., Tjong, F. V. Y., & Diederichsen, S. Z. (2021) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2021.10.002 · Cited by 11
Duncan, L., Zhu, S., Pergolotti, M., Giri, S., Salsabili, H., Faezipour, M., Ostadabbas, S., & Mirbozorgi, S. A. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3253061 · Cited by 11
Gait Monitoring and Analysis: A Mathematical Approach
Canonico, M., Desimoni, F., Ferrero, A., Grassi, P. A., Irwin, C., Campani, D., Molin, A. D., Panella, M., & Magistrelli, L. (2023) · Sensors · doi:10.3390/s23187743 · Cited by 11
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Kassanos, P., Anastasova, S., & Yang, G. (2018) · Springer eBooks · doi:10.1007/978-3-319-69748-2_3 · Cited by 11
Exel, J., Mateus, N., Abrantes, C. G., Leite, N. M. C., & Sampaio, J. E. (2019) · Sport Sciences for Health · doi:10.1007/s11332-019-00527-3 · Cited by 17
Objective evaluation of physical activity pattern using smart devices
Šimaitytė, M., Petrėnas, A., Kravčenko, J., Kaldoudi, E., & Marozas, V. (2019) · Scientific Reports · doi:10.1038/s41598-019-38638-z · Cited by 12
An entirely soft varifocal lens based on an electro-hydraulic actuator
Cheng, X., Yu, M., Ma, J., Li, B., Zhang, Y., Wang, P., & Jiao, Z. (2020) · Smart Materials and Structures · doi:10.1088/1361-665x/ab72e8 · Cited by 11
Ojie, O. D., Saatchi, R., & Saatchi, M. (2020) · Technologies · doi:10.3390/technologies8020020 · Cited by 11
State Derivation of a 12-Axis Gyroscope-Free Inertial Measurement Unit
Lu, J.-C., & Lin, P. (2011) · Sensors · doi:10.3390/s110303145 · Cited by 11
Developing and Applying a Tri-axial Accelerometer Sensor for Measuring Real Time Kayak Cadence
Croft, H. G., & Ribeiro, D. C. (2013) · Procedia Engineering · doi:10.1016/j.proeng.2013.07.038 · Cited by 11
Implantable and wearable sensors
Zhang, J. X. J., & Hoshino, K. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814862-4.00008-9 · Cited by 11
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Metadata
- Authors
- Yang, C.-C., & Hsu, Y.-L.
- Affiliations
- Yuan Ze University (TW)
- Corresponding author
- Hsu, Yeh-Liang
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2010-08-20
- Volume
- 10
- Issue
- 8
- Pages
- 7772-7788
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC3231187
- OpenAlex
- W2083539525
- Cited by
- 1,058
- References
- 91
- Citations by year
- 2026: 43 · 2025: 51 · 2024: 51 · 2023: 55 · 2022: 59 · 2021: 71 · 2020: 117 · 2019: 83 · 2018: 113 · 2017: 88 · 2016: 80 · 2015: 67 · 2014: 69 · 2013: 60 · 2012: 32
- FWCI
- 4.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- accelerometer, wearable devices, physical activity monitoring, motion detection, posture recognition, movement classification, energy expenditure estimation, fall detection, balance control, sensor placement
- MeSH terms
- Acceleration, Energy Metabolism, Equipment Design, Humans, Motor Activity, Reproducibility of Results, Exercise, Monitoring, Ambulatory, Wireless Technology
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