Bias and Motivation of Energy Monitoring during Exercise under Scientometric Perspective

Abstract

The thermodynamics is of great value for better practical applications of Internet of Things (IoT). With the development of IoT, Wearable Energy Harvester can provide more abundant measurement methods for human engineering and human heat. However, how close the relationship is between traditional individual thermal measurement and emerging technologies remains a topic that has not been paid attention to. Therefore, this paper first sorts and summarizes the literature of human calorimetry and then shows the cross field of Wearable Energy Harvester and traditional research using graph theory. Specifically, this paper presents the evolution of the research field in the form of dual-map overlays by characterizing the network structure and measuring the structural transformation of the knowledge network. With Web of Science as the data source and CiteSpace 5.0 software as the tool to make a knowledge graph for quantitative statistics, it is found that the research hotspot of individual energy expenditure is in the physiological field. The starting point of individual thermal energy monitoring technology represented by Wearable Energy Harvester is to aid with human health. However, as time progresses, although the development of this field still focuses on the fields of chemical industry and materials, it has gradually become closely related to multiple disciplines such as the monitoring of thermal energy during high-intensity sports training.