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2024 | OriginalPaper | Buchkapitel

Machine Learning Models for Human Activity Recognition: A Comparative Study

verfasst von : Anshul Sheoran, Ritu Boora, Manisha Jangra

Erschienen in: Advances in Data-Driven Computing and Intelligent Systems

Verlag: Springer Nature Singapore

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Abstract

With the advancements in machine learning, human activity recognition has found its applications in several emerging areas such as robotics healthcare, surveillance, smart environment etc. This paper aims to study and evaluate the performance of some popularly used machine learning algorithms in classifying human activities. We have selected K-NN, SVM and XGBoost methods in this study and the performance of the methods has been evaluated for 19 different activities which were performed by eight random persons. The required data was recorded using 5 MTx 3-DOF orientation trackers. The raw data was processed before feature extraction and then fed as input to the machine learning models. On performance comparison of these methods, it has been found that the SVM method when implemented with a polynomial kernel, outperforms the other state-of-the-art methods. It classified the different activities with an accuracy of 96.9%.

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Liu X, Liu L, Simske SJ, Liu J (2016) Human daily activity recognition for healthcare using wearable and visual sensing data. In: IEEE international conference on healthcare informatics, ICHI 2016, pp 24–31. https://doi.org/10.1109/ICHI.2016.100

Jalal A, Kamal S, Kim D (2014) A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments. Sensors 14:11735–11759. https://doi.org/10.3390/s140711735CrossRef

Xu H, Pan Y, Li J, Nie L, Xu X (2019) Activity recognition method for home-based elderly care service based on random forest and activity similarity. IEEE Access 7:16217–16225. https://doi.org/10.1109/ACCESS.2019.2894184CrossRef

Gayathri KS, Elias S, Ravindran B (2015) Hierarchical activity recognition for dementia care using Markov Logic Network. Pers Ubiquitous Comput 19:271–285. https://doi.org/10.1007/s00779-014-0827-7CrossRef

Direkoglu C, O’Conner NE (2012) Team activity recognition in sports. In: ECCV 2012. Lecture notes in computer science, vol 7578, pp 69–83

Vishwakarma S, Agrawal A (2013) A survey on activity recognition and behavior understanding in video surveillance. Visual Comput 29:983–1009. https://doi.org/10.1007/s00371-012-0752-6CrossRef

Turaga P, Chellappa R, Subrahmanian VS, Udrea O (2008) Machine recognition of human activities: A survey. IEEE Trans Circuits Syst Video Technol 18:1473–1488. https://doi.org/10.1109/TCSVT.2008.2005594CrossRef

Uddin S, Haque I, Lu H, Moni MA, Gide E (2022) Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Sci Rep 12:1–11. https://doi.org/10.1038/s41598-022-10358-xCrossRef

Patil SS, Dinesha HA (2022) URL redirection attack mitigation in social communication platform using data imbalance aware machine learning algorithm. Indian J Sci Technol 15:481–488. https://doi.org/10.17485/IJST/v15i11.1813

10.

Li M, Fu X, Li D (2020) Diabetes prediction based on XGBoost algorithm. In: IOP conference series: materials science and engineering. Institute of Physics Publishing. https://doi.org/10.1088/1757-899X/768/7/072093

11.

Sri Chandrahas N, Choudhary BS, Vishnu Teja M, Venkataramayya MS, Krishna Prasad NSR (2022) XG boost algorithm to simultaneous prediction of rock fragmentation and induced ground vibration using unique blast data. Appl Sci (Switzerland) 12. https://doi.org/10.3390/app12105269

12.

Deng F, Jovanov E, Song H, Shi W, Zhang Y, Xu W (2023) WiLDAR: WiFi signal-based lightweight deep learning model for human activity recognition. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2023.3294004CrossRef

13.

Altun K, Barshan B (2010) Human activity recognition using inertial/magnetic sensor units. In: HBU 2010. Lecture notes in computer science. Springer, Berlin, vol 6219, pp 38–51. https://doi.org/10.1007/978-3-642-14715-9_5

14.

Altun K, Barshan B, Tunçel O (2010) Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognit 43:3605–3620. https://doi.org/10.1016/j.patcog.2010.04.019CrossRef

15.

Barshan B, Yüksek MC (2013) Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. Comput J 57:1649–1667. https://doi.org/10.1093/comjnl/bxt075CrossRef

16.

Chowdhary CL, Acharjya DP (2020) Segmentation and feature extraction in medical imaging: a systematic review. Procedia Comput Sci 167:26–36. https://doi.org/10.1016/j.procs.2020.03.179

17.

Hakak S, Alazab M, Khan S, Gadekallu TR, Maddikunta PKR, Khan WZ (2021) An ensemble machine learning approach through effective feature extraction to classify fake news. Futur Gener Comput Syst 117:47–58. https://doi.org/10.1016/j.future.2020.11.022CrossRef

18.

Zhang S, Cheng D, Deng Z, Zong M, Deng X (2018) A novel kNN algorithm with data-driven k parameter computation. Pattern Recognit Lett 109:44–54. https://doi.org/10.1016/j.patrec.2017.09.036CrossRef

19.

Bansal M, Goyal A, Choudhary A (2022) A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decis Anal J 3. https://doi.org/10.1016/j.dajour.2022.100071

20.

Kurani A, Doshi P, Vakharia A, Shah M (2023) A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Annal Data Sci 10:183–208. https://doi.org/10.1007/s40745-021-00344-xCrossRef

21.

Armaghani DJ, Asteris PG, Askarian B, Hasanipanah M, Tarinejad R, Huynh VV (2020) Examining hybrid and single SVM models with different kernels to predict rock brittleness. Sustainability (Switzerland) 12:1–17. https://doi.org/10.3390/su12062229CrossRef

22.

Qiu Y, Zhou J, Khandelwal M, Yang H, Yang P, Li C (2022) Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration. Eng Comput 38:4145–4162. https://doi.org/10.1007/s00366-021-01393-9CrossRef

Titel: Machine Learning Models for Human Activity Recognition: A Comparative Study
verfasst von: Anshul Sheoran
Ritu Boora
Manisha Jangra
Verlag: Springer Nature Singapore
Buch: Advances in Data-Driven Computing and Intelligent Systems
Print ISBN: 978-981-9995-20-2

Electronic ISBN: 978-981-9995-21-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-9521-9_16

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