Gradient Descent Machine Learning with Equivalency Testing for Non-Subject Dependent Applications in Human Activity Recognition
DOI:
https://doi.org/10.4108/eetcasa.v8i24.1996Keywords:
Human activity recognition, digital sensor, telemetry, gradient boosting, gradient descent, machine learning, classification, statistical equivalence testingAbstract
INTRODUCTION: A solution to subject-independent HAR prediction through machine learning classification algorithms using statistical equivalency for comparative analysis between independent groups with non-subject training dependencies.
OBJECTIVES: To indicate that the multinomial predictive classification model that was trained and optimized on the one-subject control group is at least partially extensible to multiple independent experiment groups for at least one activity class.
METHODS: Gradient boosted machine multinomial classification algorithm is trained on a single individual with the classifier trained on all activity classes as a multinomial classification problem.
RESULTS: Levene-Wellek-Welch (LWW) Statistic calculated as 0.021, with a Critical Value for LWW of 0.026, using an alpha of 0.05.
CONCLUSION: Confirmed falsifiability that incorporates reproducible methods into the quasi-experiment design applied to the field of machine learning for human activity recognition.
References
Weiss GM. Wisdm smartphone and smartwatch activity and biometrics dataset. UCI Machine Learning Repository: WISDM Smartphone and Smartwatch Activity and Biometrics Dataset Data Set. 2019 Sep;7:133190-202. DOI: https://doi.org/10.1109/ACCESS.2019.2940729
Amezzane I, Fakhri Y, El Aroussi M, Bakhouya M. Towards an efficient implementation of human activity recognition for mobile devices. EAI Endorsed Transactions on Context-aware Systems and Applications. 2018 Mar 14;4(13). DOI: https://doi.org/10.4108/eai.14-3-2018.154340
Voicu RA, Dobre C, Bajenaru L, Ciobanu RI. Human physical activity recognition using smartphone sensors. Sensors. 2019 Jan;19(3):458. DOI: https://doi.org/10.3390/s19030458
Shoaib M, Bosch S, Incel OD, Scholten H, Havinga PJ. Fusion of smartphone motion sensors for physical activity recognition. Sensors. 2014 Jun;14(6):10146-76. DOI: https://doi.org/10.3390/s140610146
Zhang M, Chen S, Zhao X, Yang Z. Research on construction workers’ activity recognition based on smartphone. Sensors. 2018 Aug;18(8):2667. DOI: https://doi.org/10.3390/s18082667
Kang J, Lee J, Eom DS. Smartphone-based traveled distance estimation using individual walking patterns for indoor localization. Sensors. 2018 Sep;18(9):3149. DOI: https://doi.org/10.3390/s18093149
Benages Pardo L, Buldain Perez D, Orrite Uruñuela C. Detection of tennis activities with wearable sensors. Sensors. 2019 Jan;19(22):5004. DOI: https://doi.org/10.3390/s19225004
San Buenaventura CV, Tiglao NM, Atienza RO. Deep Learning for Smartphone-Based Human Activity Recognition Using Multi-sensor Fusion. InInternational Wireless Internet Conference 2018 Oct 15 (pp. 65-75). Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-06158-6_7
Chen K, Zhang D, Yao L, Guo B, Yu Z, Liu Y. Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities. ACM Computing Surveys (CSUR). 2021 May 22;54(4):1-40. DOI: https://doi.org/10.1145/3447744
Irfan S, Anjum N, Masood N, Khattak AS, Ramzan N. A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities. Sensors. 2021 Jan;21(24):8227. DOI: https://doi.org/10.3390/s21248227
Solorio-Fernández S, Carrasco-Ochoa JA, Martínez-Trinidad JF. A survey on feature selection methods for mixed data. Artificial Intelligence Review. 2021 Sep 29:1-26.
Wellek S. Testing statistical hypotheses of equivalence. Chapman and Hall/CRC; 2002 Nov 12. DOI: https://doi.org/10.1201/9781420035964
Natekin A, Knoll A. Gradient boosting machines, a tutorial. Frontiers in neurorobotics. 2013 Dec 4;7:21. DOI: https://doi.org/10.3389/fnbot.2013.00021
Jabbari Y, Cribbie R. Negligible interaction test for continuous predictors. Journal of Applied Statistics. 2021 Feb 20:1-5. DOI: https://doi.org/10.31234/osf.io/py5bf
Cribbie, R., Udi, A., Beribiski, N., Chalmers, P., Counsell, A., Farmus, L., Gutierrez, N., Ng, V. (2022). Negligible: A Collection of Functions for Negligible Effect / Equivalence Testing. CRAN - Package negligible (r-project.org). Accessed 2 Mar 2022.
Aiello, S., Kraljevic, T., & Maj, P. (2015). Package ‘h2o’. dim, 2, 12.
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
McAlexander, R. J., & Mentch, L. (2020). Predictive inference with random forests: A new perspective on classical analyses. Research & Politics, 7(1), 2053168020905487. DOI: https://doi.org/10.1177/2053168020905487
Zhang, Z., & Yuan, K.-H. (2018). Practical Statistical Power Analysis Using Webpower and R (Eds). Granger, IN: ISDSA Press.
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