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A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices

wearables, such as smartwatches and fitness trackers, are becoming increasingly popular as tools for measuring and managing health and fitness. However, most wearables on the market today only provide data on a single health metric, such as steps taken or heart rate.

A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices and weight management would be a significant step forward in the accuracy of fitness tracking. This dataset would include data from multiple wearable devices, as well as other health data, such as weight and body composition.

With this dataset, researchers would be able to develop algorithms that could more accurately estimate energy expenditure. This would be a valuable tool for people who are trying to lose weight or manage their weight, as well as for athletes who are trying to optimize their training.

While there are some challenges to collecting this type of dataset, such as making sure that the data is accurate and consistent across devices, the benefits of a more accurate energy expenditure estimation would be worth the effort.

In the past few years, an increasing number of wearable devices that track human movement and physiological signals have been developed and commercialized. These devices have the potential to provide objective and real-time estimates of human energy expenditure (EE), a key metric in physical activity and health research. However, the accuracy of EE estimation from wearable devices remains limited, in part due to the lack of publicly available datasets that can be used to benchmark and validate device performance. To address this need, we have developed a multidevice and multimodal dataset for EE estimation. The dataset includes data from 14 subjects who wore up to 7 different wearable devices during 6 different activities (e.g., walking, running, and biking). The data were collected in a controlled laboratory setting and contain ground truth EE measurements from indirect calorimetry. We provide detailed descriptions of the dataset, including information on the devices used, the activities performed, and the data collection protocol. The dataset is freely available to the research community and can be accessed at https://xxx.

The accuracy of EE estimation from wearable devices remains limited, in part due to the lack of publicly available datasets that can be used to benchmark and validate device performance. To address this need, we have developed a multidevice and multimodal dataset for EE estimation. The dataset includes data from 14 subjects who wore up to 7 different wearable devices during 6 different activities (e.g., walking, running, and biking). The data were collected in a controlled laboratory setting and contain ground truth EE measurements from indirect calorimetry. We provide detailed descriptions of the dataset, including information on the devices used, the activities performed, and the data collection protocol. The dataset is freely available to the research community and can be accessed at https://xxx.

Wearable devices have the potential to provide objective and real-time estimates of human energy expenditure (EE), a key metric in physical activity and health research. However, the accuracy of EE estimation from wearable devices remains limited. To address this need, we have developed a multidevice and multimodal dataset for EE estimation.

The dataset includes data from 14 subjects who wore up to 7 different wearable devices during 6 different activities (e.g., walking, running, and biking). The data were collected in a controlled laboratory setting and contain ground truth EE measurements from indirect calorimetry.

We provide detailed descriptions of the dataset, including information on the devices used, the activities performed, and the data collection protocol. The dataset is freely available to the research community and can be accessed at https://xxx.

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