A Comparative Study on the Suitability of Smartphones and IMU for Mobile, Unsupervised Energy Expenditure Calculi
Open accessAbstract
The metabolic equivalent of task (MET) is currently the most used indicator for measuring the energy expenditure (EE) of a physical activity (PA) and has become an important measure for determining and supervising a person's state of health. The use of new devices which are capable of measuring inertial movements by means of built-in accelerometers enable the PA to be measured objectively on the basis of the reckoning of "counts". These devices are also known as inertial measurement units (IMUs) and each count is an aggregated value indicating the intensity of a movement and can be used in conjunction with other parameters to determine the MET rate of a particular physical activity and thus it's associated EE. Various types of inertial devices currently exist that enable count calculus and physical activity to be monitored. The advent of mobile devices, such as smartphones, with empowered computation capabilities and integrated inertial sensors, has enabled EE to be measure in a distributed, ubiquitous and natural way, thereby overcoming the reluctance of users and practitioners associated with in-lab studies. From the point of view of the process analysis and infrastructure needed to manage data from inertial devices, there are also various differences in count computing: extra devices are required, out-of-device processing, etc. This paper presents a study to discover whether the estimation of energy expenditure is dependent on the accelerometer of the device used in measurements and to discover the suitability of each device for performing certain physical activities. In order to achieve this objective, we have conducted several experiments with different subjects on the basis of the performance of various daily activities with different smartphones and IMUs.
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PyBodyTrack's approach allows contactless, device-free tracking, eliminating the need for wearable sensors such as accelerometers.
“This approach allows contactless, device-free tracking, eliminating the need for wearable sensors like accelerometers, which have been the predominant trend in recent years [23].”
From PyBodyTrack: A python library for multi-algorithm motion quantification and tracking in videos (2025) · cites this paper as [23]
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Cites
Clegg, A., Young, J., Iliffe, S., Rikkert, M. O., & Rockwood, K. (2013) · The Lancet · doi:10.1016/s0140-6736(12)62167-9 · Cited by 9,503 · see citing works
Compendium of physical activities: an update of activity codes and MET intensities
AINSWORTH, B. E., HASKELL, W. L., WHITT, M. C., IRWIN, M. L., SWARTZ, A. M., STRATH, S. J., O???BRIEN, W. L., BASSETT, D. R., SCHMITZ, K. H., EMPLAINCOURT, P. O., et al. (2000) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-200009001-00009 · Cited by 8,497 · see citing works
Physical Activity and Health: A Report of the Surgeon General
(1995) · PsycEXTRA Dataset · doi:10.1037/e525162010-001 · Cited by 5,488 · see citing works
Jetté, M., Sidney, K., & Blümchen, G. (1990) · Clinical Cardiology · doi:10.1002/clc.4960130809 · Cited by 1,510 · see citing works
CHAMPS Physical Activity Questionnaire for Older Adults: outcomes for interventions
Stewart, A. L., Mills, K. M., King, A. C., Haskell, W. L., Gillis, D., & Ritter, P. L. (2001) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-200107000-00010 · Cited by 1,250 · see citing works
A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring
Yang, C.-C., & Hsu, Y.-L. (2010) · Sensors · doi:10.3390/s100807772 · Cited by 1,058 · see citing works
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 · see citing works
Mathie, M. J., Coster, A. C. F., Lovell, N. H., & Celler, B. G. (2004) · Physiological Measurement · doi:10.1088/0967-3334/25/2/r01 · Cited by 828 · see citing works
Measuring Physical Activity and Energy Expenditure
Kemper, H. C. G., & Saris, W. H. M. (1996) · Cited by 553 · see citing works
Metabolic equivalent: one size does not fit all
Byrne, N. M., Hills, A. P., Hunter, G. R., Weinsier, R. L., & Schutz, Y. (2005) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00023.2004 · Cited by 527 · see citing works
A novel method for using accelerometer data to predict energy expenditure
Crouter, S. E., Clowers, K. G., & Bassett, D. R. (2005) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00818.2005 · Cited by 455 · see citing works
Lee, I.-M., & Shiroma, E. J. (2014) · British Journal of Sports Medicine · doi:10.1136/bjsports-2013-093154 · Cited by 415 · cited 1× · see citing works
“However, since these proposals focus on the EE of a particular PA or are based on the use of a single accelerometer [8,21,22], the results obtained are fully conditional on the technology used or by the PA performed which means that in some cases EE has a higher margin of error.”
BackgroundMeasuring Physical Activity and Energy Expenditure
Sallis, J. F. (1996) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-199609000-00022 · Cited by 396 · see citing works
Actigraph GT3X: Validation and Determination of Physical Activity Intensity Cut Points
Santos‐Lozano, A., Santín-Medeiros, F., Cardon, G., Torres‐Luque, G., Bailón, R., Bergmeir, C., Ruiz, J. R., Lucía, A., & Garatachea, N. (2013) · International Journal of Sports Medicine · doi:10.1055/s-0033-1337945 · Cited by 388 · see citing works
Classifying household and locomotive activities using a triaxial accelerometer
Oshima, Y., Kawaguchi, K., Tanaka, S., Ohkawara, K., Hikihara, Y., Ishikawa‐Takata, K., & Tabata, I. (2010) · Gait & Posture · doi:10.1016/j.gaitpost.2010.01.005 · Cited by 372 · see citing works
Murphy, S. L. (2008) · Preventive Medicine · doi:10.1016/j.ypmed.2008.12.001 · Cited by 326 · see citing works
Endurance training does not enhance total energy expenditure in healthy elderly persons
Goran, M. I., & Poehlman, É. T. (1992) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1992.263.5.e950 · Cited by 276 · see citing works
Detection of Type, Duration, and Intensity of Physical Activity Using an Accelerometer
BONOMI, A. G., Goris, A. H. C., Yin, B., & Westerterp, K. R. (2009) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3181a24536 · Cited by 247 · see citing works
Estimating Activity and Sedentary Behavior from an Accelerometer on the Hip or Wrist
Rosenberger, M. E., Haskell, W. L., Albinali, F., Mota, S., Nawyn, J., & Intille, S. S. (2013) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31827f0d9c · Cited by 242 · see citing works
Energy expenditure: components and evaluation methods.
Volp, A. C. P., de Oliveira, F. C. E., Alves, R. D. M., Esteves, E. A., & Bressan, J. (2011) · PubMed · doi:10.1590/s0212-16112011000300002 · Cited by 167 · see citing works
Measuring physical activity and energy expenditure
Montoye, H. J. (1996) · Cited by 157 · see citing works
Freedson, P. S., Lyden, K. A., Keadle, S. K., & STAUDENMAYER, J. W. (2011) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00309.2011 · Cited by 137 · see citing works
Using wearable activity type detection to improve physical activity energy expenditure estimation
Albinali, F., Intille, S. S., Haskell, W. L., & Rosenberger, M. E. (2010) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/1864349.1864396 · Cited by 132 · see citing works
Comparison of Two Methods of Measuring Physical Activity in South African Older Adults
Kolbe-Alexander, T. L., Lambert, E. V., Harkins, J., & Ekelund, U. (2006) · Journal of Aging and Physical Activity · doi:10.1123/japa.14.1.98 · Cited by 107 · see citing works
Physical activity and energy expenditure measurements using accelerometers in older adults.
Garatachea, N., Luque, G. T., & González‐Gallego, J. (2010) · PubMed · Cited by 87 · see citing works
Energy requirements and energy expenditure of elderly men
Calloway, D. H., & Zanni, E. E. (1980) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/33.10.2088 · Cited by 83 · see citing works
Area under a Curve: Trapezoidal and Simpson’s Rules
Tallarida, R. J., & Murray, R. B. (1987) · Springer eBooks · doi:10.1007/978-1-4612-4974-0_26 · Cited by 60 · see citing works
A method to compare new and traditional accelerometry data in physical activity monitoring
van Hees, V. T., Pias, M. R., Taherian, S., Ekelund, U., & Brage, S. (2010) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom.2010.5534986 · Cited by 43 · see citing works
Zappa: An Open Mobile Platform to Build Cloud-Based m-Health Systems
Ruíz-Zafra, Á., Benghazi, K., Noguera, M., & Garrido, J. L. G. (2013) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-00566-9_12 · Cited by 28 · see citing works
Self-citationBourke, A. K., Prescher, S., Koehler, F., Cionca, V., Tavares, C., Gomis, S., Garcia, V., & Nelson, J. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6345916 · Cited by 24 · see citing works
Metabolic Equivalent: One Size Does Not Fit All
Shephard, R. J. (2006) · Yearbook of Sports Medicine · doi:10.1016/s0162-0908(08)70337-4 · Cited by 12 · see citing works
Energy Expenditure Analysis: A Comparative Research of Based on Mobile Accelerometers
Ruíz-Zafra, Á., Orantes-González, E., Noguera, M., Benghazi, K., & Heredia‐Jimenez, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13105-4_7 · Cited by 4 · see citing works
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Alzheimer’s Disease stage identification using deep learning models
Bringas, S., Salomón, S., Duque, R., Lage, C., & Montaña, J. L. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103514 · Cited by 111 · see citing works
Detection of Alzheimer’s Disease Based on Cloud-Based Deep Learning Paradigm
Dayananda, P., Nagaraju, S. C., Niranjanamurthy, M., Raisinghani, M. S., Bhatia, S., Alkhaldi, N. A., & Malibari, A. (2023) · Diagnostics · doi:10.3390/diagnostics13162687 · Cited by 26 · see citing works
Subway rail transit monitoring by built-in sensor platform of smartphone
Cong, J., Gao, M., Wang, Y., Chen, R., & Wang, P. (2020) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1900242 · Cited by 21 · see citing works
Benítez-Guijarro, A., Ruíz-Zafra, Á., Callejas, Z., Medina-Medina, N., Benghazi, K., & Noguera, M. (2018) · Sensors · doi:10.3390/s19010108 · Cited by 9 · see citing works
Self-citationKim, H.-E., Park, D.-H., An, C.-H., Choi, M.-Y., Kim, D., & Hong, Y.-S. (2025) · Sensors · doi:10.3390/s25165047 · Cited by 7 · see citing works
Sánchez-Sánchez, M. L., Ruescas-Nicolau, M.-A., Arnal‐Gómez, A., Iosa, M., Pérez‐Alenda, S., & Cortés‐Amador, S. (2024) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-024-01346-5 · Cited by 5 · see citing works
Collazo, C., González‐Santos, J., Gonzalez-Bernal, J. J., & Cubo, E. (2020) · Atención Primaria Práctica · doi:10.1016/j.appr.2020.100064 · Cited by 5 · see citing works
PyBodyTrack: A python library for multi-algorithm motion quantification and tracking in videos
Ruiz-Zafra, A., Pigueiras-del-Real, J., Heredia-Jimenez, J., Shah, S. T. H., Shah, S. A. H., & Gontard, L. C. (2025) · SoftwareX · doi:10.1016/j.softx.2025.102272 · Cited by 1 · see citing works
Self-citationMobile Health System for Evaluation of Breast Cancer Patients During Treatment and Recovery Phases
Ollero, J., Moral-Muñoz, J. A., Rojas, I., & Baños, O. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56154-7_58 · Cited by 1 · see citing works
A Mapping Review of Physical Activity Recordings Derived From Smartphone Accelerometers
Stålesen, J. H., Westergren, T., Hansen, B. H., & Berntsen, S. (2020) · Journal of Physical Activity and Health · doi:10.1123/jpah.2020-0041 · Cited by 1 · see citing works
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Malviya, R., Kumar, S., Solanke, A. S., Goyal, P., & Chauhan, K. (2025) · Wiley eBooks · doi:10.1002/9781394347537.ch7
- Authors
- Ruiz-Zafra, A., Orantes-González, E., Noguera, M., Benghazi, K., & Heredia-Jimenez, J.
- Affiliations
- University of Granada, Avda. del Hospicio, Granada 18071, Spain
- Universidad de Granada (ES)
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2015-07-27
- Volume
- 15
- Issue
- 8
- Pages
- 18270-18286
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC4570320
- OpenAlex
- W1833273439
- Cited by
- 13
- References
- 32
- Citations by year
- 2025: 3 · 2024: 1 · 2023: 1 · 2022: 1 · 2020: 4 · 2018: 2 · 2017: 1
- FWCI
- 0.87
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Institutional Repository of the University of Granada (University of Granada) (submittedVersion, cc-by-nc-nd)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministerio de Educación, Cultura y Deporte (FPU13, FPU13/00162)
- Ministerio de Economía y Competitividad
- Universidad de Granada
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- energy expenditure estimation, smartphones, inertial measurement unit, accelerometer, metabolic equivalent of task, physical activity monitoring, wearable sensors, device comparison
- MeSH terms
- Smartphone, Adult, Energy Metabolism, Humans, Motor Activity, Young Adult, Accelerometry
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