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Raising the Bar(Ometer): Identifying a User’s Stair and Lift Usage Through Wearable Sensor Data Analysis

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Sensor-Based Activity Recognition and Artificial Intelligence (iWOAR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15357))

  • 36 Accesses

Abstract

Many users are confronted multiple times daily with the choice of whether to take the stairs or the elevator. Whereas taking the stairs could be beneficial for cardiovascular health and wellness, taking the elevator might be more convenient but it also consumes energy. By precisely tracking and boosting users’ stairs and elevator usage through their wearable, users might gain health insights and motivation, encouraging a healthy lifestyle and lowering the risk of sedentary-related health problems. This research describes a new exploratory dataset, to examine the patterns and behaviors related to using stairs and lifts. We collected data from 20 participants while climbing and descending stairs and taking a lift in a variety of scenarios. The aim is to provide insights and demonstrate the practicality of using wearable sensor data for such a scenario. Our collected dataset was used to train and test a Random Forest machine learning model, and the results show that our method is highly accurate at classifying stair and lift operations with an accuracy of 87.61% and a multi-class weighted F1-score of 87.56% over 8-second time windows. Furthermore, we investigate the effect of various types of sensors and data attributes on the model’s performance. Our findings show that combining inertial and pressure sensors yields a viable solution for real-time activity detection.

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Notes

  1. 1.

    Both the code and data (raw and resampled form) for the project are publicly accessible on GitHub: https://github.com/iiMox/project_work_stairs_lift_detection.git.

References

  1. Jovin, I.: Fitbit floors climbed not accurate? here’s what to do. (2019). https://gadgetsandwearables.com/2019/07/28/fitbit-floor-count-too-high/. Accessed 26 June 2024

  2. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L., et al.: A public domain dataset for human activity recognition using smartphones. In: Esann, vol. 3, p. 3 (2013)

    Google Scholar 

  3. Atlas: Cardiovascular benefits of stair climbing: Fitness explained. https://atlasbars.com/blogs/fitness-explained/cardiovascular-benefits-of-stair-climbing-fitness-explained. Accessed 26 June 2024

  4. Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L., Amirat, Y.: Physical human activity recognition using wearable sensors. Sensors 15(12), 31314–31338 (2015)

    Article  Google Scholar 

  5. Banos, O., et al.: mHealthDroid: a novel framework for agile development of mobile health applications. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) IWAAL 2014. LNCS, vol. 8868, pp. 91–98. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13105-4_14

    Chapter  MATH  Google Scholar 

  6. Captain calculator: Calories burned on stairs | calculator & formula (2020). https://captaincalculator.com/health/calorie/calories-burned-stairs-calculator/. Accessed 26 June 2024

  7. Delahoz, Y.S., Labrador, M.A.: Survey on fall detection and fall prevention using wearable and external sensors. Sensors 14(10), 19806–19842 (2014)

    Article  Google Scholar 

  8. Kim, Y., Oh, J.H.: Recent progress in pressure sensors for wearable electronics: from design to applications. Appl. Sci. 10(18), 6403 (2020)

    Article  MATH  Google Scholar 

  9. Kritzler, M., Bäckman, M., Tenfält, A., Michahelles, F.: Wearable technology as a solution for workplace safety. In: Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia, pp. 213–217 (2015)

    Google Scholar 

  10. Mattmann, C., Amft, O., Harms, H., Troster, G., Clemens, F.: Recognizing upper body postures using textile strain sensors. In: 2007 11th IEEE International Symposium on Wearable Computers, pp. 29–36. IEEE (2007)

    Google Scholar 

  11. Neves, P., Stachyra, M., Rodrigues, J.: Application of wireless sensor networks to healthcare promotion. J. Commun. Softw. Syst. 4(3), 181–190 (2008)

    Article  MATH  Google Scholar 

  12. Blackmer, N.: Here’s how many stairs you should climb a day for a healthy heart (2023), https://www.verywellhealth.com/daily-stairs-for-a-healthy-heart-8349369. Accessed 26 June 2024

  13. Ohtaki, Y., Susumago, M., Suzuki, A., Sagawa, K., Nagatomi, R., Inooka, H.: Automatic classification of ambulatory movements and evaluation of energy consumptions utilizing accelerometers and a barometer. Microsyst. Technol. 11, 1034–1040 (2005)

    Article  Google Scholar 

  14. Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M.: A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 9, 1–17 (2012)

    Article  MATH  Google Scholar 

  15. Ramasamy Ramamurthy, S., Roy, N.: Recent trends in machine learning for human activity recognition-a survey. Wiley Interdisc. Rev. Data Mining Knowl. Discov. 8(4), e1254 (2018)

    Article  Google Scholar 

  16. Randell, C., Muller, H.: Context awareness by analysing accelerometer data. In: Digest of Papers. Fourth International Symposium on Wearable Computers, pp. 175–176. IEEE (2000)

    Google Scholar 

  17. Reiss, A., Stricker, D.: Introducing a new benchmarked dataset for activity monitoring. In: 2012 16th International Symposium on Wearable Computers, pp. 108–109 (2012). https://doi.org/10.1109/ISWC.2012.13

  18. Rozalynn S. Frazier, C.P.T.: Do you have a sedentary lifestyle? Here are 8 signs and solutions (2024). https://www.realsimple.com/health/fitness-exercise/sedentary-lifestyle-signs. Accessed 26 June 2024

  19. TheTechyLife: Which fitbits count stairs (2024). https://thetechylife.com/which-fitbits-count-stairs/. Accessed 20 June 2024

  20. Thompson, W.R.: Worldwide survey of fitness trends for 2020. ACSM’s Health Fitness J. 23(6), 10–18 (2019)

    Article  MATH  Google Scholar 

  21. Nielson, T.: [fix] fitbit not counting floors climbed or tracking incorrectly (2023). https://wearholic.com/fitbit-not-counting-floors/. Accessed 26 June 2024

  22. Weiss, G.: Wisdm smartphone and smartwatch activity and biometrics dataset data set. UCI Machine Learning Repository (2019). https://archive.ics. uci. edu/ml/datasets/WISDM+ Smartphone+ and+ Smartwatch+ Activity+ and+ Biometrics+ Dataset+. Accessed 18 May 2022

  23. Weiss, G.: WISDM Smartphone and Smartwatch Activity and Biometrics Dataset. UCI Machine Learning Repository (2019)

    Google Scholar 

  24. Wu, M., Luo, J.: Wearable technology applications in healthcare: a literature review. Online J. Nurs. Inform 23(3) (2019)

    Google Scholar 

  25. Zhang, S., et al.: Deep learning in human activity recognition with wearable sensors: a review on advances. Sensors 22(4), 1476 (2022)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 425868829 and is part of Priority Program SPP2199 Scalable Interaction Paradigms for Pervasive Computing Environments.

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Authors and Affiliations

  1. University of Siegen, 57076, Siegen, Germany

    Hrishikesh Balkrishna Karande, Ravikiran Arasur Thippeswamy Shivalingappa, Abdelhafid Nassim Yaici, Iman Haghbin, Niravkumar Bavadiya, Robin Burchard & Kristof Van Laerhoven

Authors
  1. Hrishikesh Balkrishna Karande
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  2. Ravikiran Arasur Thippeswamy Shivalingappa
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  3. Abdelhafid Nassim Yaici
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  4. Iman Haghbin
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  5. Niravkumar Bavadiya
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  6. Robin Burchard
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  7. Kristof Van Laerhoven
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Corresponding author

Correspondence to Robin Burchard .

Editor information

Editors and Affiliations

  1. Hasso Plattner Institute, Potsdam, Germany

    Orhan Konak

  2. Hasso Plattner Institute, Potsdam, Germany

    Bert Arnrich

  3. Fraunhofer Institute for Computer Graphics Research IGD, Rostock, Baden-Württemberg, Germany

    Gerald Bieber

  4. Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt, Hessen, Germany

    Arjan Kuijper

  5. University of Lübeck, Lübeck, Germany

    Sebastian Fudickar

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The authors have no competing interests to declare that are relevant to the content of this article.

Appendices

A Confusion Matrix for 4-Seconds Time-Window

Fig. 7.
A confusion matrix for a classification model with six categories: Null, lift down, lift up, stairs down, stairs up. The matrix shows true labels on the y-axis and predicted labels on the x-axis. Key data points include high accuracy for the Null category with 3340 correct predictions, and significant misclassifications such as 216 Null predicted as stairs down. The lift down category has 304 correct predictions, while stairs up has 1153 correct predictions. Other categories show varying levels of accuracy and misclassification.

Confusion Matrix for 4-second windows.

Full size image

B Evaluation Metrics for 20 Participants in 8-Seconds Time-Window

The following table presents evaluation metrics and also suitable hyper-parameter values for random forest found by GridSearchCV for each participant in 8-second windows.

Table 3. The model evaluation metrics (Acc for Accuracy, Est.s for Estimators) for all 20 participants for 8-second time windows along with hyperparameter values.
Full size table

C Evaluation Metrics for 20 Participants in 4-Seconds Time-Window

The following table presents evaluation metrics and also suitable hyper-parameter values for random forest found by GridSearchCV for each participant in 4-second windows.

Table 4. The model evaluation metrics (Acc for Accuracy, Est.s for Estimators) for all 20 participants for 4-second time windows along with hyperparameter values.
Full size table

D Total Time Duration for Each Class in Minutes

Table 5. Duration of data collected for each class in minutes
Full size table

In total 152.95 min of data was collected for the lift classes and 99.08 min of data was collected for the stairs classes.

E Feature-Scores for 26 Features Averaged over 20 Participants in 8-Seconds and 4-Seconds Time-Window

Table 6. Feature scores for all features extracted over 8-seconds and 4-seconds window
Full size table

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Karande, H.B. et al. (2025). Raising the Bar(Ometer): Identifying a User’s Stair and Lift Usage Through Wearable Sensor Data Analysis. In: Konak, O., Arnrich, B., Bieber, G., Kuijper, A., Fudickar, S. (eds) Sensor-Based Activity Recognition and Artificial Intelligence. iWOAR 2024. Lecture Notes in Computer Science, vol 15357. Springer, Cham. https://doi.org/10.1007/978-3-031-80856-2_14

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  • DOI: https://doi.org/10.1007/978-3-031-80856-2_14

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