Towards non-laboratory prediction of relative physical activity intensities from multimodal wearable sensor data

@article{Chowdhury2017TowardsNP,
  title={Towards non-laboratory prediction of relative physical activity intensities from multimodal wearable sensor data},
  author={Alok Kumar Chowdhury and Dian Wirawan Tjondronegoro and Jinglan Zhang and Puspa Setia Pratiwi and Stewart G. Trost},
  journal={2017 IEEE Life Sciences Conference (LSC)},
  year={2017},
  pages={230-233}
}
This paper explored a non-laboratory approach to effectively predict relative physical activity intensities using regression algorithms on multimodal physiological data. 22 participants completed 5 to 7 physical activity sessions where each session consisted of 5 activity trials ranging from sedentary to vigorous. During the trials, participant's heart rate (HR), r-r interval (RR), electrodermal activity (Eda), and body temperature (Temp) were recorded using wearable sensors. Immediately after… CONTINUE READING

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