Models that are Not Available

This is a list of novel methods for predicting energy expenditure of activity intensity that have been developed but are not yet included in the repository because the model/code/etc. was not linked to the original published paper.

If you want to be included in the repository or have a suggestion for a model to be included, please contact us!

• Albinali, F., Intille, S., Haskell, W., & Rosenberger, M. (2010). Using wearable activity type detection to improve physical activity energy expenditure estimation. Proceedings of the 12th ACM International Conference on Ubiquitous Computing.

• Altini, M., Casale, P., Penders, J., & Amft, O. (2015). Personalized cardiorespiratory fitness and energy expenditure estimation using hierarchical Bayesian models. Journal of Biomedical Informatics, 56, 195-204.

• Altini, M., Casale, P., Penders, J. F., & Amft, O. (2015). Personalization of energy expenditure estimation in free living using topic models. IEEE Journal of Biomedical and Health Informatics, 19(5), 1577-1586.

• Altini, M., Penders, J., & Amft, O. (2012). Energy expenditure estimation using wearable sensors: A new methodology for activity-specific models. Proceedings of the Conference on Wireless Health.

• Altini, M., Penders, J., & Amft, O. (2013). Body weight-normalized energy expenditure estimation using combined activity and allometric scaling clustering. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

• Altini, M., Penders, J., & Amft, O. (2013). Personalizing energy expenditure estimation using a cardiorespiratory fitness predicate. 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops.

• Altini, M., Penders, J., & Amft, O. (2015). Estimating oxygen uptake during nonsteady-state activities and transitions using wearable sensors. IEEE Journal of Biomedical and Health Informatics, 20(2), 469-475.

• Altini, M., Penders, J., Vullers, R., & Amft, O. (2013). Combining wearable accelerometer and physiological data for activity and energy expenditure estimation. Proceedings of the 4th Conference on Wireless Health.

• Altini, M., Penders, J., Vullers, R., & Amft, O. (2014). Estimating energy expenditure using body-worn accelerometers: a comparison of methods, sensors number and positioning. IEEE Journal of Biomedical and Health Informatics, 19(1), 219-226.

• Altini, M., Penders, J., Vullers, R., & Amft, O. (2014). Personalizing energy expenditure estimation using physiological signals normalization during activities of daily living. Physiological Measurement, 35(9), 1797.

• Amaral, K. M., Chen, P., Crouter, S., & Ding, W. (2017). Bag-of-words method applied to accelerometer measurements for the purpose of classification and energy estimation. arXiv preprint arXiv:1704.01574.

• Anastasopoulou, P., Tansella, M., Stumpp, J., Shammas, L., & Hey, S. (2012). Classification of human physical activity and energy expenditure estimation by accelerometry and barometry. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

• Atallah, L., Leong, J., Lo, B., & Yang, G.-Z. (2011). Energy expenditure prediction using a miniaturized ear-worn sensor. Medicine and Science in Sports and Exercise, 43(7), 1369-1377.

• Bai, C., Wanigatunga, A. A., Saldana, S., Casanova, R., Manini, T. M., & Mardini, M. T. (2022). Are machine learning models on wrist accelerometry robust against differences in physical performance among older adults?. Sensors, 22(8), 3061.

• Beltrame, T., Amelard, R., Wong, A., & Hughson, R. L. (2017). Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living. Scientific Reports, 7(1), 1-8.

• Bonomi, A. G., Plasqui, G., Goris, A. H., & Westerterp, K. R. (2009). Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer. Journal of Applied Physiology, 107(3), 655-661.

• Bouarfa, L., Atallah, L., Kwasnicki, R. M., Pettitt, C., Frost, G., & Yang, G.-Z. (2013). Predicting free-living energy expenditure using a miniaturized ear-worn sensor: An evaluation against doubly labeled water. IEEE Transactions on Biomedical Engineering, 61(2), 566-575.

• Bourke, A. K., Massé, F., Arami, A., Aminian, K., Healy, M., Nelson, J., ODwyer, C., & Coote, S. (2014). Energy expenditure estimation using accelerometry and heart rate for multiple sclerosis and healthy older adults. 2014 11th International Conference on Wearable and Implantable Body Sensor Networks Workshops.

• Carneiro, S., Silva, J., Aguiar, B., Rocha, T., Sousa, I., Montanha, T., & Ribeiro, J. (2015). Accelerometer-based methods for energy expenditure using the smartphone. 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings.

• Catal, C., & Akbulut, A. (2018). Automatic energy expenditure measurement for health science. Computer Methods and Programs in Biomedicine, 157, 31-37.

• Chen, K. Y., Acra, S. A., Majchrzak, K., Donahue, C. L., Baker, L., Clemens, L., Sun, M., & Buchowski, M. S. (2003). Predicting energy expenditure of physical activity using hip-and wrist-worn accelerometers. Diabetes Technology & Therapeutics, 5(6), 1023-1033.

• Chen, S., Lach, J., Amft, O., Altini, M., & Penders, J. (2013). Unsupervised activity clustering to estimate energy expenditure with a single body sensor. 2013 IEEE International Conference on Body Sensor Networks.

• Choi, J. H., Lee, J., Hwang, H. T., Kim, J. P., Park, J. C., & Shin, K. (2006). Estimation of activity energy expenditure: accelerometer approach. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference,

• Chowdhury, A. K., Tjondronegoro, D., Zhang, J., Hagenbuchner, M., Cliff, D., & Trost, S. G. (2018). Deep learning for energy expenditure prediction in pre-school children. IEEE Conference on Biomedical and Health Informatics.

• Cvetković, B., Milić, R., & Luštrek, M. (2015). Estimating energy expenditure with multiple models using different wearable sensors. IEEE Journal of Biomedical and Health Informatics, 20(4), 1081-1087.

• Cvetković, B., Szeklicki, R., Janko, V., Lutomski, P., & Luštrek, M. (2018). Real-time activity monitoring with a wristband and a smartphone. Information Fusion, 43, 77-93.

• De Bois, M., Amroun, H., & Ammi, M. (2018). Energy expenditure estimation through daily activity recognition using a smart-phone. 2018 IEEE 4th World Forum on Internet of Things.

• Delgado-Gonzalo, R., Celka, P., Renevey, P., Dasen, S., Solà, J., Bertschi, M., & Lemay, M. (2015). Physical activity profiling: activity-specific step counting and energy expenditure models using 3D wrist acceleration. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

• Dong, B., Biswas, S., Montoye, A., & Pfeiffer, K. (2013). Comparing metabolic energy expenditure estimation using wearable multi-sensor network and single accelerometer. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

• Dong, B., Montoye, A., Biswas, S., & Pfeiffer, K. (2014). Metabolic energy expenditure estimation using a position-agnostic wearable sensor system. 2014 IEEE Healthcare Innovation Conference.

• Duclos, M., Fleury, G., Lacomme, P., Phan, R., Ren, L., & Rousset, S. (2016). An acceleration vector variance based method for energy expenditure estimation in real-life environment with a smartphone/smartwatch integration. Expert Systems with Applications, 63, 435-449.

• Ellis, K., Kerr, J., Godbole, S., Lanckriet, G., Wing, D., & Marshall, S. (2014). A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers. Physiological Measurement, 35(11), 2191.

• Farrahi, V., Niemelä, M., Tjurin, P., Kangas, M., Korpelainen, R., & Jämsä, T. (2019). Evaluating and enhancing the generalization performance of machine learning models for physical activity intensity prediction from raw acceleration data. IEEE Journal of Biomedical and Health Informatics, 24(1), 27-38.

• Freedson, P. S., Lyden, K., Kozey-Keadle, S., & Staudenmayer, J. (2011). Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample. Journal of Applied Physiology, 111(6), 1804-1812.

• García-García, F., García-Sáez, G., Chausa, P., Martínez-Sarriegui, I., Benito, P. J., Gómez, E. J., & Hernando, M. E. (2011). Statistical machine learning for automatic assessment of physical activity intensity using multi-axial accelerometry and heart rate. Conference on Artificial Intelligence in Medicine in Europe.

• Garnotel, M., Bastian, T., Romero-Ugalde, H.-M., Maire, A., Dugas, J., Zahariev, A., Doron, M., Jallon, P., Charpentier, G., & Franc, S. (2018). Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry. Journal of Applied Physiology, 124(3), 780-790.

• Gjoreski, H., Kaluža, B., Gams, M., Milić, R., & Luštrek, M. (2013). Ensembles of multiple sensors for human energy expenditure estimation. Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing.

• Gjoreski, H., Kaluža, B., Gams, M., Milić, R., & Luštrek, M. (2015). Context-based ensemble method for human energy expenditure estimation. Applied Soft Computing, 37, 960-970.

• Guidoux, R., Duclos, M., Fleury, G., Lacomme, P., Lamaudière, N., Manenq, P.-H., Paris, L., Ren, L., & Rousset, S. (2014). A smartphone-driven methodology for estimating physical activities and energy expenditure in free living conditions. Journal of Biomedical Informatics, 52, 271-278.

• Guidoux, R., Duclos, M., Fleury, G., Lacomme, P., Lamaudière, N., Saboul, D., Ren, L., & Rousset, S. (2017). The eMouveRecherche application competes with research devices to evaluate energy expenditure, physical activity and still time in free-living conditions. Journal of Biomedical Informatics, 69, 128-134.

• Haapalainen, E., Laurinen, P., Siirtola, P., Roning, J., Kinnunen, H., & Jurvelin, H. (2008). Exercise energy expenditure estimation based on acceleration data using the linear mixed model. 2008 IEEE International Conference on Information Reuse and Integration.

• Hamid, A., Duncan, M. J., Eyre, E. L., & Jing, Y. (2021). Predicting children’s energy expenditure during physical activity using deep learning and wearable sensor data. European Journal of Sport Science, 21(6), 918-926.

• Härtel, S., Gnam, J. P., Löffler, S., & Bös, K. (2011). Estimation of energy expenditure using accelerometers and activity-based energy models-validation of a new device. European Review of Aging and Physical Activity, 8(2), 109-114.

• Hiremath, S. V., Intille, S. S., Kelleher, A., Cooper, R. A., & Ding, D. (2016). Estimation of energy expenditure for wheelchair users using a physical activity monitoring system. Archives of Physical Medicine and Rehabilitation, 97(7), 1146-1153. e1141.

• Ingraham, K. A., Ferris, D. P., & Remy, C. D. (2017). Using wearable physiological sensors to predict energy expenditure. 2017 International Conference on Rehabilitation Robotics (ICORR).

• John, D., Liu, S., Sasaki, J., Howe, C., Staudenmayer, J., Gao, R., & Freedson, P. S. (2011). Calibrating a novel multi-sensor physical activity measurement system. Physiological Measurement, 32(9), 1473.

• Kate, R. J., Swartz, A. M., Welch, W. A., & Strath, S. J. (2016). Comparative evaluation of features and techniques for identifying activity type and estimating energy cost from accelerometer data. Physiological Measurement, 37(3), 360.

• Kawahara, Y., Ryu, N., & Asami, T. (2009). Monitoring daily energy expenditure using a 3-axis accelerometer with a low-power microprocessor. International Journal on Human-Computer Interaction, 1(5), 145-154.

• Kheirkhahan, M., Mehta, S., Nath, M., Wanigatunga, A. A., Corbett, D. B., Manini, T. M., & Ranka, S. (2017). A bag-of-words approach for assessing activities of daily living using wrist accelerometer data. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

• Lazar, D., Begum, M., Murshed, M. M., Nelson, B., Bock, J. M., Imboden, M., Kaminsky, L., & Montoye, A. (2020). Statistical Learning Methods to Predict Activity Intensity from Body-Worn Accelerometers. Journal of Biomedical Analytics, 3(1), 27-50.

• Lee, J.-M., Kim, Y.-W., Kwon, Y.-S., Derrick, T. R., & Welk, G. J. (2015). Feasibility of calibrating smartphone to access physical activity. The Korean Journal of Measurement and Evaluation in Physical Education and Sport Science, 17(3), 1-10.

• Lee, M.-W., Khan, A. M., & Kim, T.-S. (2011). A single tri-axial accelerometer-based real-time personal life log system capable of human activity recognition and exercise information generation. Personal and Ubiquitous Computing, 15(8), 887-898.

• Lester, J., Hartung, C., Pina, L., Libby, R., Borriello, G., & Duncan, G. (2009). Validated caloric expenditure estimation using a single body-worn sensor. Proceedings of the 11th International Conference on Ubiquitous Computing.

• Li, M., Kwak, K.-C., & Kim, Y. T. (2016). Estimation of energy expenditure using a patch-type sensor module with an incremental radial basis function neural network. Sensors, 16(10), 1566.

• Lin, C.-W., Yang, Y.-T. C., Wang, J.-S., & Yang, Y.-C. (2012). A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation. IEEE Transactions on Information Technology in Biomedicine, 16(5), 991-998.

• Lin, S.-Y., Lai, Y.-C., Hsia, C.-C., Su, P.-F., & Chang, C.-H. (2016). Validation of energy expenditure prediction models using real-time shoe-based motion detectors. IEEE Transactions on Biomedical Engineering, 64(9), 2152-2162.

• Lin, W.-Y., Verma, V. K., Lee, M.-Y., & Lai, C.-S. (2018). Activity monitoring with a wrist-worn, accelerometer-based device. Micromachines, 9(9), 450.

• Liu, C.-T., & Chan, C.-T. (2016). Exercise performance measurement with smartphone embedded sensor for well-being management. International Journal of Environmental Research and Public Health, 13(10), 1001.

• Liu, K. C., Liu, C. T., Chen, C. W., Lin, C. C., & Chan, C. T. (2014). Accelerometry-based motion pattern analysis for physical activity recognition and activity level assessment. Applied Mechanics and Materials, 479, 818-822.

• Liu, S., Gao, R. X., John, D., Staudenmayer, J., & Freedson, P. S. (2011). SVM-based multi-sensor fusion for free-living physical activity assessment. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

• Liu, S., Gao, R. X., John, D., Staudenmayer, J. W., & Freedson, P. S. (2011). Multisensor data fusion for physical activity assessment. IEEE Transactions on Biomedical Engineering, 59(3), 687-696.

• Lu, K., Yang, L., Seoane, F., Abtahi, F., Forsman, M., & Lindecrantz, K. (2018). Fusion of heart rate, respiration and motion measurements from a wearable sensor system to enhance energy expenditure estimation. Sensors, 18(9), 3092.

• Luštrek, M., Cvetković, B., & Kozina, S. (2012). Energy expenditure estimation with wearable accelerometers. 2012 IEEE international symposium on circuits and systems (ISCAS).

• McGregor, S. J., Busa, M. A., Yaggie, J. A., & Bollt, E. M. (2009). High resolution MEMS accelerometers to estimate VO 2 and compare running mechanics between highly trained inter-collegiate and untrained runners. PloS One, 4(10), e7355.

• Miyatake, M., Nakamura, N., Nagata, T., Yuuki, A., Yomo, H., Kawabata, T., & Hara, S. (2016). VO2 estimation using 6-axis motion sensing data. 2016 10th International Symposium on Medical Information and Communication Technology (ISMICT).

• Mo, L., Liu, S., Gao, R. X., John, D., Staudenmayer, J. W., & Freedson, P. S. (2012). Wireless design of a multisensor system for physical activity monitoring. IEEE Transactions on Biomedical Engineering, 59(11), 3230-3237.

• Montoye, A. H., Dong, B., Biswas, S., & Pfeiffer, K. A. (2014). Use of a wireless network of accelerometers for improved measurement of human energy expenditure. Electronics, 3(2), 205-220.

• Montoye, A. H., Begum, M., Henning, Z., & Pfeiffer, K. A. (2017). Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data. Physiological Measurement, 38(2), 343.

• Montoye, A. H., Dong, B., Biswas, S., & Pfeiffer, K. A. (2016). Validation of a wireless accelerometer network for energy expenditure measurement. Journal of Sports Sciences, 34(21), 2130-2139.

• Mukaino, M., Ogasawara, T., Matsuura, H., Aoshima, Y., Suzuki, T., Furuzawa, S., ... & Otaka, Y. (2022). Validity of trunk acceleration measurement with a chest-worn monitor for assessment of physical activity intensity. BMC Sports Science, Medicine and Rehabilitation, 14(1), 1-11.

• Nagata, T., Nakamura, N., Miyatake, M., Yuuki, A., Yomo, H., Kawabata, T., & Hara, S. (2016). VO 2 estimation using 6-axis motion sensor with sports activity classification. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

• Nakamura, N., Nagata, T., Miyatake, M., Yuuki, A., Yomo, H., Kawabata, T., & Hara, S. (2016). Applying neural network to VO 2 estimation using 6-axis motion sensing data. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

• Nakanishi, M., Izumi, S., Nagayoshi, S., Kawaguchi, H., Yoshimoto, M., Shiga, T., Ando, T., Nakae, S., Usui, C., & Aoyama, T. (2018). Estimating metabolic equivalents for activities in daily life using acceleration and heart rate in wearable devices. Biomedical Engineering Online, 17(1), 1-18.

• Nnamoko, N., Cabrera-Diego, L. A., Campbell, D., Sanders, G., Fairclough, S. J., & Korkontzelos, I. (2021). Personalised Accelerometer Cut-point Prediction for Older Adults’ Movement Behaviours using a Machine Learning approach. Computer Methods and Programs in Biomedicine, 208, 106165.

• O’Driscoll, R., Turicchi, J., Hopkins, M., Horgan, G. W., Finlayson, G., & Stubbs, J. R. (2020). Improving energy expenditure estimates from wearable devices: A machine learning approach. Journal of Sports Sciences, 38(13), 1496-1505.

• Pande, A., Zeng, Y., Das, A. K., Mohapatra, P., Miyamoto, S., Seto, E., Henricson, E. K., & Han, J. J. (2013). Energy expenditure estimation with smartphone body sensors. Proceedings of the 8th International Conference on Body Area Networks.

• Pande, A., Zhu, J., Das, A. K., Zeng, Y., Mohapatra, P., & Han, J. J. (2015). Using smartphone sensors for improving energy expenditure estimation. IEEE Journal of Translational Engineering in Health and Medicine, 3, 1-12.

• Park, H., Dong, S.-Y., Lee, M., & Youn, I. (2017). The role of heart-rate variability parameters in activity recognition and energy-expenditure estimation using wearable sensors. Sensors, 17(7), 1698.

• Pires, I. M., Felizardo, V., Pombo, N., Drobics, M., Garcia, N. M., & Flórez-Revuelta, F. (2018). Validation of a method for the estimation of energy expenditure during physical activity using a mobile device accelerometer. Journal of Ambient Intelligence and Smart Environments, 10(4), 315-326.

• Popp, W. L., Schneider, S., Bär, J., Bösch, P., Spengler, C. M., Gassert, R., & Curt, A. (2019). Wearable sensors in ambulatory individuals with a spinal cord injury: from energy expenditure estimation to activity recommendations. Frontiers in Neurology, 10, 1092.

• Rosenberger, M. E., Haskell, W. L., Albinali, F., Mota, S., Nawyn, J., & Intille, S. (2013). Estimating activity and sedentary behavior from an accelerometer on the hip or wrist. Medicine and Science in Sports and Exercise, 45(5), 964.

• Rothney, M. P., Neumann, M., Béziat, A., & Chen, K. Y. (2007). An artificial neural network model of energy expenditure using nonintegrated acceleration signals. Journal of Applied Physiology, 103(4), 1419-1427.

• Rousset, S., Guidoux, R., Paris, L., Farigon, N., Miolanne, M., Lahaye, C., Duclos, M., Boirie, Y., & Saboul, D. (2017). A novel smartphone accelerometer application for low-intensity activity and energy expenditure estimations in overweight and obese adults. Journal of Medical Systems, 41(8), 1-10.

• Ruch, N., Joss, F., Jimmy, G., Melzer, K., Hänggi, J., & Mäder, U. (2013). Neural network versus activity-specific prediction equations for energy expenditure estimation in children. Journal of Applied Physiology, 115(9), 1229-1236.

• Rumo, M., Amft, O., Tröster, G., & Mäder, U. (2011). A stepwise validation of a wearable system for estimating energy expenditure in field-based research. Physiological Measurement, 32(12), 1983.

• Satizábal, H. F., Grillon, A., Picasso, G., Upegui, A., Millet, G., & Perez-Uribe, A. (2017). ActiDote–A wireless sensor-based system for self-tracking activity levels among manual wheelchair users. EAI Endorsed Transactions on Pervasive Health and Technology, 3(11).

• Sazonov, E., Hegde, N., Browning, R. C., Melanson, E. L., & Sazonova, N. A. (2015). Posture and activity recognition and energy expenditure estimation in a wearable platform. IEEE Journal of Biomedical and Health Informatics, 19(4), 1339-1346.

• Staudenmayer, J., Pober, D., Crouter, S., Bassett, D., & Freedson, P. (2009). An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. Journal of Applied Physiology.

• Strath, S. J., Brage, S., & Ekelund, U. (2005). Integration of physiological and accelerometer data to improve physical activity assessment. Medicine and Science in Sports and Exercise, 37(11 Suppl), S563-571.

• Strath, S. J., Kate, R. J., Keenan, K. G., Welch, W. A., & Swartz, A. M. (2015). Ngram time series model to predict activity type and energy cost from wrist, hip and ankle accelerometers: implications of age. Physiological Measurement, 36(11), 2335.

• Su, S. W., Celler, B. G., Savkin, A. V., Nguyen, H. T., Cheng, T. M., Guo, Y., & Wang, L. (2009). Transient and steady state estimation of human oxygen uptake based on noninvasive portable sensor measurements. Medical & Biological Engineering & Computing, 47(10), 1111-1117.

• Tao, L., Burghardt, T., Mirmehdi, M., Damen, D., Cooper, A., Camplani, M., Hannuna, S., Paiement, A., & Craddock, I. (2017). Energy expenditure estimation using visual and inertial sensors. IET Computer Vision, 12(1), 36-47.

• Tjurin, P., Niemelä, M., Huusko, M., Ahola, R., Kangas, M., & Jämsä, T. (2017). Classification of physical activities and sedentary behavior using raw data of 3D hip acceleration. European Medical and Biological Engineering Conference.

• Trost, S. G., Wong, W.-K., Pfeiffer, K. A., & Zheng, Y. (2012). Artificial neural networks to predict activity type and energy expenditure in youth. Medicine and Science in Sports and Exercise, 44(9), 1801.

• Trost, S., Cliff, D., Ahmadi, M. N., Van Tuc, N., & Hagenbuchner, M. (2018). Sensor-enabled activity class recognition in preschoolers: Hip versus wrist data. Medicine and Science in Sports and Exercise, 50(3), 634-641.

• Van Hees, V. T., Van Lummel, R. C., & Westerterp, K. R. (2009). Estimating activity‐related energy expenditure under sedentary conditions using a tri‐axial seismic accelerometer. Obesity, 17(6), 1287-1292.

• Vathsangam, H., Emken, A., Schroeder, E. T., Spruijt-Metz, D., & Sukhatme, G. S. (2011). An experimental study in determining energy expenditure from treadmill walking using hip-worn inertial sensors. IEEE Transactions on Bio-medical Engineering, 58(10), 2804.

• Vathsangam, H., Emken, A., Schroeder, E. T., Spruijt-Metz, D., & Sukhatme, G. S. (2011). Towards a generalized regression model for on-body energy prediction from treadmill walking. 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

• Vathsangam, H., Emken, B. A., Schroeder, E. T., Spruijt-Metz, D., & Sukhatme, G. S. (2010). Energy estimation of treadmill walking using on-body accelerometers and gyroscopes. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

• Vathsangam, H., Schroeder, E. T., & Sukhatme, G. S. (2014). Hierarchical approaches to estimate energy expenditure using phone-based accelerometers. IEEE Journal of Biomedical and Health Informatics, 18(4), 1242-1252.

• Vathsangam, H., Zhang, M., Tarashansky, A., Sawchuk, A. A., & Sukhatme, G. S. (2013). Towards practical energy expenditure estimation with mobile phones. 2013 Asilomar Conference on Signals, Systems and Computers.

• Voleno, M., Redmond, S. J., Cerutti, S., & Lovell, N. H. (2010). Energy expenditure estimation using triaxial accelerometry and barometric pressure measurement. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

• Wang, J., Redmond, S. J., Voleno, M., Narayanan, M. R., Wang, N., Cerutti, S., & Lovell, N. H. (2012). Energy expenditure estimation during normal ambulation using triaxial accelerometry and barometric pressure. Physiological Measurement, 33(11), 1811.

• Wang, Q., Lohit, S., Toledo, M. J., Buman, M. P., & Turaga, P. (2016). A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

• Widianto, A., Sugiarto, T., Lin, Y.-J., Lee, Y.-H., & Hsu, W.-C. Physical activity intensity classification using a convolutional neural network and wearable accelerometer. The 16th International Conference on Automation Technology.

• Zakeri, I., Adolph, A. L., Puyau, M. R., Vohra, F. A., & Butte, N. F. (2008). Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry. Journal of Applied Physiology, 104(6), 1665-1673.

• Zhu, J., Pande, A., Mohapatra, P., & Han, J. J. (2015). Using deep learning for energy expenditure estimation with wearable sensors. 2015 17th International Conference on E-health Networking, Application & Services.