Abstract
In this article, we first present a comprehensive review of methods for inferring or predicting Energy Expenditure (EE). Signals used to estimate EE are classified and compared; the methods to build the relationship between signals and EE are classified and compared. Based on this literature review and analysis, we conclude that the generalized regression approach along with combining multiple signals is promising. Subsequently, we propose a new approach to estimate EE based on the XGBoosting method along with two types of signals (in time series), namely electrymography (EMG) and ground force (GF). The result of the experiment shows that the proposed approach has an excellent performance in terms of accuracy, efficiency, and enabler of real-time or online monitoring, and the new concept makes sense to further improve the performance of the new approach.
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Zhang, X., Cao, S., Zhang, H., Chen, J., Gupta, M.M., Zhang, W. (2022). Energy Expenditure Prediction Methods: Review and New Developments. In: Li, X. (eds) Advances in Intelligent Automation and Soft Computing. IASC 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-81007-8_133
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DOI: https://doi.org/10.1007/978-3-030-81007-8_133
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