Abstract
Daily sufficient fluid intake is one of the important conditions of human health. Only an automatic monitoring of a sufficient fluid intake volume fulfills practicality requirements for people especially dependent on such intake, e.g. during specific medical treatment or older people experiencing a diminished sensation of natural thirst. Programmable smartwatch apps can realize such an automatic fluid intake volume monitoring from the morning till night time with sufficient precision impeding dehydration. We present an innovative five aspects approach for a comprehensive analysis of the application area. First, how much fluid volume shall be orally ingested by an individual person? Second, in which way can fluid intake acts reliably be detected from gestures? Third, how can the ingested total volume be automatically estimated? Fourth, in which way can the daily fluid volume already ingested so far be indicated to a smartwatch wearer at a glance and in an intuitive way? Fifth and at last, when and in which situations do advices to the smartwatch wearer to drink a beverage right now meet his/her open mind and eyes? For the necessary machine learning, data mining, and recognition respectively classification process of physiologic activities the applied statistics and artificial intelligence methods will be presented, analyzed and evaluated from a practical perspective.
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Notes
- 1.
The experiments where conducted with n = 7 persons in the age group of interest: 50 to 72 years. The test persons agreed, after acquiring ground truth data in phase 1 of the experiment over the course of either a full day or full week, to use the smartwatch in phase 2 of the experiment also for a full day or a full week. Each ingested beverage during the test period had to be time-stamped and recorded, measured or weighed with kitchen scales in order to provide the necessary ground truth. Test persons included as well ambidextrous persons as persons with a predominant, preferred hand.
- 2.
Ingested fluids included: cold table and mineral water, soft drinks like apple juice, different flavors of hot tea, various kinds of hot coffee and chocolate. Alcoholics were not permitted in the experiments.
References
Hall, J.E., Guyton, A.C.: Textbook on Medical Physiology, 14th edn. Elsevier Publishing Inc., Philadelphia, PA, USA (2020)
Köhnke, K.: Water balance and the nutritional importance of water and beverages. Ernährungsumschau 58(2), 88–94 (2011). (in German). https://www.ernaehrungs-umschau.de/fileadmin/Ernaehrungs-Umschau/pdfs/pdf_2011/02_11/EU02_2011_088_095.qxd.pdf
Volkert, D., Beck, A.M., Cederholm, T., Cruz-Jentoft, A., Goisser, S., et al.: ESPEN guideline on clinical nutrition and hydration in geriatrics. Clin. Nutrition 38, 10–47 (2018). (Elsevier). https://doi.org/10.1016/j.clnu.2018.05.024
Mascot, O., Miranda, J., Santamaría, A.L., Pueyo, E.P., Pascual, A., Butigué, T.: Fluid intake recommendation considering the physiological adaptions of adults over 65 years: a critical review. Nutrients 12(11), 1–14 (2020). (MDPI). https://doi.org/10.3390/nu12113383
Hodgkinson, B., Evans, D., Wood, J.: Maintaining oral hydration in older adults: a systematic review. Int. J. Nurs. Pract. 9(3), S19–S278 (2003). (J. Wiley & Sons). https://doi.org/10.1046/j.1440-172X.2003.00425.x
Saker, P., Farrell, M.J., Egan, G.F., McKinley, M.J., Denton, D.A.: Overdrinking, swallowing inhibition, and regional brain responses prior to swallowing. In: Proceedings of the National Academy of Sciences of the United States of America (PNAS), vol. 113, no. 43, October 10, 2016, pp. 12274–12379 (2016). https://doi.org/10.1073/pnas.1613929113
Yamada.Y. et al.: Variation in human water turnover associated with environmental and lifestyle factors. Science 378(6622), 909–915 (2022). https://doi.org/10.1126/science.abm8668
Cohen, R., Fernie, G., Fekr, A.R.: Fluid intake monitoring systems for the elderly: a review of the literature. Nutrients 13(6), 1–28 (2021). (MDPI). https://doi.org/10.3390/nu13062092
Watson, P.E., Watson, I.D. Batt, R.D.: Total body water volume for adult males and females estimated from simple anthropometric measurements. Am. J. Clin. Nutrition 33(1), 27–39 (1980). https://doi.org/10.1093/ajcn/33.1.27
Katz, S.: Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living. J. Am. Geriatr. Soc. 31(12), 721–727 (1983)
Weiss, G.M., Timko J., Gallagher, C. Yoneda, K., Schreiber A.: Smartwatch-based activity recognition: a machine learning approach. In: IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 426–429. Las Vegas, USA (2016). https://doi.org/10.1109/BHI.2016.7455925
Amft, O., Bannach, D., Pirkl, G., Kreil, M., Lukowicz, P.: Towards wearable sensing-based assessment of fluid intake. In: 8th IEEE International Conference on Pervasive Computing and Communications Workshop (PERCOM) 2010, pp. 298–303 (2010). https://doi.org/10.1109/PERCOMW.2010.5470653
Suryadevara, N.K., Mukhopadhyvay, S.C.: Determining wellness through an ambient assisted living environment. IEEE Intell. Syst. 29(3), 30–37 (2014). https://doi.org/10.1109/MIS.2014.16
Wellnitz, A., Wolff, J.P., Haubelt, C., Kirste, T.: Fluid intake recognition using inertial sensors. In: ACM 6th International Workshop on Sensor-based Activity Recognition and Interaction (IOWAR 2019), Berlin, Germany, article no. 4, pp. 1–7 (2019). https://doi.org/10.1145/3361684.3361688
Chun, K.S., Sanders, A.B., Adaimi, R., Streeper, N., Conroy, D.E., Thomaz, E.: Towards a generalizable method for detecting fluid intake with wrist-mounted sensors and adaptive segmentation. In: ACM International Conference on Intelligent User Interfaces (IUI 2019), pp. 80–85 (2019).https://doi.org/10.1145/3301275.3302315
Baldauf, R.: Mobile Sensor-Based Drinking Detection. FOM University, Research Paper (2015). [in German]
Lutze, R., Waldhör, K.: A smartwatch software architecture for health hazard handling for elderly people. In: IEEE International Conference on HealthCare Informatics (ICHI) 2015, pp. 356–361, Dallas, USA (2015). https://doi.org/10.1109/ICHI.2015.50
Waldhör, K., Baldauf, R.: Recognizing Trinking ADLs in Real Time using Smartwatches and Data Mining, Rapid Miner Wisdom/Europe Conference, Ljubljana, Slovenia (2015). https://www.researchgate.net/publication/301772482_Recognizing_Drinking_ADLs_in_Real_Time_using_Smartwatches_and_Data_Mining
Lutze, R., Baldauf, R., Waldhör, K.: Dehydration prevention and effective support for the elderly by the use of smartwatches. In: 17th IEEE International Conference on E-Health Networking, Application & Services (HealthCom), 14–17 Oct 2015, Boston, USA (2015). https://doi.org/10.1109/HealthCom.2015.7454534
Waldhör, K., Lutze, R.: Smartwatch based tumble recognition – a data mining model comparison study. In: 18th IEEE Int. Conference on E-Health, Networking, Application & Services (HealthCom), 14.-16.9.2016, Munich, Germany (2016). https://doi.org/10.1109/HealthCom.2016.7749464
Lutze, R., Waldhör, K.: The application architecture of smartwatch apps – analysis, principles of design and organization. In: Mayr, H.C., Pinzger, M. (Hrsg.) INFORMATIK 2016. LNI, vol. P259, ISBN 978-3-88579-653-4, ISSN 1617–5468, pp. 1865–1878. Springer, Bonn (2016). https://cs.emis.de/LNI/Proceedings/Proceedings259/1865.pdf
Lutze, R., Waldhör, K.: Integration of stationary and wearable support services for an actively assisted living of elderly people: capabilities, achievements, limitations, prospects—a case study. In: Wichert, R., Mand, B. (eds.) Ambient Assisted Living. ATSC, pp. 3–26. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52322-4_1
Lutze, R., Waldhör, K.: Personal health assistance for elderly people via smartwatch based motion analysis. In: IEEE International Conference on Healthcare Informatics (ICHI), 23–26 Aug 2017, pp. 124–133. Park City, UT, USA (2017). https://doi.org/10.1109/ICHI.2017.79
Lutze, R., Waldhör, K.: Model based dialogue control for smartwatches. In: Kurosu, M. (ed.) HCI 2017. LNCS, vol. 10272, pp. 225–239. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58077-7_18
Lutze, R., Waldhör, K.: Improving dialogue design and control for smartwatches by reinforcement learning based behavioral acceptance patterns. In: Kurosu, M. (ed.) HCII 2020. LNCS, vol. 12183, pp. 75–85. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49065-2_6
Lutze, R., Waldhör, K.: Practical suitability of emotion recognition from physiological signals by mainstream smartwatches. In: In: Kurosu, M. (eds.) Human-Computer Interaction. Technological Innovation. HCII 2022, LNCS, vol. 13303, Proceedings Part II, pp. 362–375, Springer (2022). https://doi.org/10.1007/978-3-031-05409-9_28
Hamatani, T., Elhamshary, M., Uchiyama, A., Higashino, T.: FluidMeter: gauging the human daily fluid intake using smartwatches. In: ACM Proceedings on Interactive, Mobile, Wearable, Ubiquitous Technologies (IMWUT), vol. 2(3), article no. 113, 1–15 (2018). https://doi.org/10.1145/3264923
Huang, H.-Y., Hsieh, C.-Y., Liu, K.-C., Hsu, S.J.-P., Chan, C.-T.: Fluid intake monitoring system using a wearable inertial sensor for fluid intake management. Sensors 20(22), 1–17 (2020). https://doi.org/10.3390/s20226682
Lutze, R.: Practicality of automatic monitoring sufficient fluid intake for older people. In: IEEE 10th International Conference on Healthcare Informatics (ICHI), June 11–14, pp. 330–336. Rochester, MN, USA (2022). https://doi.org/10.1109/ICHI54592.2022.00054
Aggarwal, C.C.: Neural Network and Deep Learning – A Textbook. Springer International Publishing 2018, Springer, Cham, Switzerland
NN: Accelerate. Developer Information, Apple Inc. https://developer.apple.com/documentation/accelerate. Retrieved 6 Jan 2023
NN: Basic Neural Network Subroutines (BNNS). Developer Information, Apple Inc. https://developer.apple.com/documentation/accelerate/bnns. Retrieved 6 Jan 2023
NN: Keras Deep Learning Framework. https://keras.io/. Retrieved 6 Jan 2023
NN: Core ML – Integrate Machine Learning Models Into Your App. Developer Information, Apple Inc. https://developer.apple.com/documentation/coreml. Retrieved 6 Jan 2023
NN: Guide to Background Work. Android Developers. Google Inc. https://developer.android.com/guide/background. Retrieved 6 Jan 2023
NN: Working with the watchOS App Life Cycle. Developer Information, Apple Inc. https://developer.apple.com/documentation/watchkit/wkextensiondelegate/working_with_the_watchos_app_life_cycle. Retrieved 6 Jan 2023
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Lutze, R., Waldhör, K. (2023). Practicality Aspects of Automatic Fluid Intake Monitoring via Smartwatches. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2023. Lecture Notes in Computer Science, vol 14014. Springer, Cham. https://doi.org/10.1007/978-3-031-35572-1_5
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