Quantitative Dehydration Estimation

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If you are using sweat-related data, please cite:

Ring, M., Lohmueller, C., Rauh, M., and Eskofier, B. M. (2015). On sweat analysis for quantitative estimation of dehydration during physical exercise. Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Milan, Italy, pp. 7011–7014.

If you are using bioimpedance-related data, please cite:

Ring, M., Lohmueller, C., Rauh, M., Mester, J., and Eskofier, B. M. (2016), A temperature-based bioimpedance correction for water loss estimation during sports. IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 6, pp. 1477–1484.

If you are using saliva-related data, please cite:

Ring, M., Lohmueller, C., Rauh, M., Mester, J., and Eskofier, B. M. (2016), Salivary markers for quantitative dehydration estimation during physical exercise, IEEE Journal of Biomedical and Health Informatics, in press.

Please also include the standard citation for PhysioNet:

Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13).


Quantitative estimation of dehydration (total body water loss) using bioimpedance measurements, temperature measurements, salivary samples, and sweat samples.

Data Collection

The task is to estimate total body water (TBW) loss using bioimpedance measurements, temperature measurements, salivary samples, and sweat samples. TBW loss was induced by 120 minutes of physical exercise without fluid intake. Physical exercise consisted of running on an indoor treadmill and was partitioned into 8 intervals of 15 minutes. Every 15-minute running interval was followed by an 8-minute resting break in which bioimpedance, temperature, sweat, and saliva were collected / measured. TBW loss was assumed to be equal to the change in body weight, which was measured using a high-precision scale (+- 5 g accuracy).


The subject data and measurements are contained in the dehydration_estimation.csv file. The attributes in the data set are:

  1. subject id (10 subjects in total)
  2. subject age [years]
  3. subject height [cm]
  4. running speed on treadmill [km/h]
  5. running interval (8 running intervals in total, 0 denotes baseline measurements before the first interval, 1 denotes measurements after the first running interval, 2 denotes measurements after the second running interval, ...)
  6. body weight measured using Kern DE 150K2D [kg] (difference in body weight is assumed to be equal to total body water loss)
  7. body weight measured using InBody 720 [kg]
  8. total body water estimated using InBody 720 [l]
  9. bioimpedance of right arm at 1000kHz [Ohm]
  10. bioimpedance of left arm at 1000kHz [Ohm]
  11. bioimpedance of trunk at 1000kHz [Ohm]
  12. bioimpedance of right leg at 1000kHz [Ohm]
  13. bioimpedance of left leg at 1000kHz [Ohm]
  14. temperature ear [degree C]
  15. temperature left hand [degree C]
  16. temperature right hand [degree C]
  17. temperature left foot [degree C]
  18. temperature right foot [degree C]
  19. temperature chest [degree C]
  20. temperature back [degree C]
  21. temperature upper arm [degree C]
  22. temperature lower arm [degree C]
  23. temperature upper leg [degree C]
  24. temperature lower leg [degree C]
  25. sweat chloride [mmol/l]
  26. sweat osmolality [mmol/kg]
  27. salivary amylase [units/l]
  28. salivary chloride [mmol/l]
  29. salivary cortisol [ng/ml]
  30. salivary cortisone [ng/ml]
  31. salivary osmolality [mmol/kg]
  32. salivary potassium [mmol/l]
  33. salivary protein concentration [mg/l]


Matthias Ring or Bjoern M. Eskofier
Machine Learning and Data Analytics Lab
Department of Computer Science
Friedrich-Alexander University Erlangen-Nuremberg

Additional References

Ring, M., Lohmueller, C., Rauh, M., and Eskofier, B. M. (2014). A two-stage regression using bioimpedance and temperature for hydration assessment during sports. In: Proceedings of the 22nd International Conference on Pattern Recognition. Stockholm, Sweden, pp. 4519-4524.

Icon  Name                       Last modified      Size  Description
[PARENTDIR] Parent Directory - [   ] MD5SUMS 2018-08-30 16:37 146 [   ] SHA1SUMS 2018-08-30 16:37 170 [   ] SHA256SUMS 2018-08-30 16:37 242 [TXT] dehydration_estimation.csv 2017-07-19 13:43 14K

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Updated Friday, 28 October 2016 at 16:58 EDT

PhysioNet is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number 2R01GM104987-09.