Abstract
Human activity recognition by the use of smartphone-equipped sensors has gotten a lot of interest in current times because of its large variety of applications.In this regard, this study provides a comprehensive comparative analysis of shallow and deep learning models for smartphone-based HARover high granular daily human activities. Moreover, A robust architecture for smartphone-based HAR is also provided, with stages ranging from data collection to data modelling. A total of seven best performing HAR models namely Decision Tree (DT), Random Forest(RF), DeepNeural Networks (DNN), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gradient Boosting (GB) and Convolutional Neural Networks (CNN) are investigated. This research work is based on a real-world dataset of 95690 data samples collected from the smartphone sensors of 18 different subjects. The comparative study reveals that three models namely DNN, RF, and GB mostly dominated over the other models in terms of five performance metrics namely accuracy, recall, precision, F1-score, and AUC value.
Similar content being viewed by others
Notes
https://bit.ly/3z5ddO8
References
Ahmed N, Rafiq JI, Islam MR (2020) Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model. Sensors 20(1):317
Auc-roc score. https://bit.ly/3optubl, Accessed: 04 Apr 2021
Azar SM, Atigh MG, Nickabadi A, Alahi A, (2019) Convolutional relational machine for group activity recognition, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 7892–7901
Barua A, Masum AKM, Hossain ME, Bahadur EH, Alam MS (2019) A study on human activity recognition using gyroscope, accelerometer, temperature and humidity data, in 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE). pp 1–6
Bashar SK, Al Fahim A, Chon KH (2020) Smartphone based human activity recognition with feature selection and dense neural network, in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC) pp 5888–5891
Bouchabou D, Nguyen SM, Lohr C, LeDuc B, Kanellos I (2021) A survey of human activity recognition in smart homes based on IoT sensors algorithms: Taxonomies, challenges, and opportunities with deep learning, Sensors, 21(18):6037 Sep. 2021. [Online]. Available: https://doi.org/10.3390/s21186037
Branco P, Torgo L, Ribeiro RP (2017) Relevance-based evaluation metrics for multi-class imbalanced domains, in Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, pp 698–710
Bulbul E, Cetin A, Dogru IA (2018) Human activity recognition using smartphones, in 2018 2nd international symposium on multidisciplinary studies and innovative technologies (ismsit). IEEE p 1–6
Çatalbaş B, Çatalbaş B, Morgül Ö (2017) Human activity recognition with different artificial neural network based classifiers, in 2017 25th Signal Processing and Communications Applications Conference (SIU). IEEE. p 1–4
Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Computers & Electrical Engineering. 40(1):16–28
Chen Y, Shen C (2017) Performance analysis of smartphone-sensor behavior for human activity recognition. IEEE Access 5:3095–3110
Chen Z, Zhu Q, Soh YC, Zhang L (2017) Robust human activity recognition using smartphone sensors via ct-pca and online svm. IEEE Transactions on Industrial Informatics. 13(6):3070–3080
Chen Z, Jiang C, Xiang S, Ding J, Wu M, Li X (2019) Smartphone sensor-based human activity recognition using feature fusion and maximum full a posteriori. IEEE Transactions on Instrumentation and Measurement 69(7):3992–4001
Chen B, Deng W, Du J (2017) Noisy softmax: Improving the generalization ability of dcnn via postponing the early softmax saturation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 5372–5381
Das Antar A, Ahmed M, Ahad MAR (2019) Challenges in sensor-based human activity recognition and a comparative analysis of benchmark datasets: A review, in 2019 Joint 8th International Conference on Informatics, Electronics Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision Pattern Recognition (icIVPR). pp 134–139
Du Y, Lim Y, Tan Y (2019) A novel human activity recognition and prediction in smart home based on interaction. Sensors 19(20):4474
Erdaş ÇB, Güney S (2021) Human activity recognition by using different deep learning approaches for wearable sensors. Neural Processing Letters 53(3):1795–1809. https://doi.org/10.1007/s11063-021-10448-3
Fang L, Yishui S, Wei C (2016) Up and down buses activity recognition using smartphone accelerometer, in 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference p 761–765
Fan L, Wang Z, Wang H (2013) Human activity recognition model based on decision tree, in 2013 International Conference on Advanced Cloud and Big Data. IEEE, 2013 pp 64–68
Feng Z, Mo L, Li M (2015) A random forest-based ensemble method for activity recognition, in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), p 5074–5077
Ferrari A, Micucci D, Mobilio M, Napoletano P (2021) Trends in human activity recognition using smartphones, Journal of Reliable Intelligent Environments, 7(3):189–213. https://doi.org/10.1007/s40860-021-00147-0
Fridriksdottir E, Bonomi AG (2020) Accelerometer-based human activity recognition for patient monitoring using a deep neural network. Sensors 20(22):6424. https://doi.org/10.3390/s20226424
Ghate V, C SH (2021) Hybrid deep learning approaches for smartphone sensor-based human activity recognition, Multimedia Tools and Applications, 80(28-29):35 585–35 604, Feb. 2021. [Online]. Available: https://doi.org/10.1007/s11042-020-10478-4
Golestani N, Moghaddam M (2020) A comparison of machine learning classifiers for human activity recognition using magnetic induction-based motion signals, in 2020 14th European Conference on Antennas and Propagation (EuCAP).IEEE https://doi.org/10.23919/eucap48036.2020.9135215
Gopika N, ME AMK (2018) Correlation based feature selection algorithm for machine learning, in 2018 3rd international conference on communication and electronics systems (ICCES). IEEE pp 692–695
Grid-search. https://bit.ly/3fkLijJ. Accessed 04 Apr 2021
Grzeszick R, Lenk JM, Rueda FM, Fink GA, Feldhorst S, ten M Hompel, Deep neural network based human activity recognition for the order picking process, Proceedings of the 4th international Workshop on Sensor-based Activity Recognition and Interaction, 1–6
Gupta A, Gusain K, Popli B (2016) Verifying the value and veracity of extreme gradient boosted decision trees on a variety of datasets, in 2016 11th International Conference on Industrial and Information Systems (ICIIS) pp 457–462
Gusain K, Gupta A, Popli B (2018) Transition-aware human activity recognition using extreme gradient boosted decision trees. Advanced Computing and Communication Technologies. Springer 2018:41–49
Harsha NCS, Anudeep YGVS, Vikash K, Ratnam DV (2021) Performance analysis of machine learning algorithms for smartphone-based human activity recognition. Wireless Personal Communications 121(1):381–398. https://doi.org/10.1007/s11277-021-08641-7
Hassan MM, Ullah S, Hossain MS, Alelaiwi A (2020) An end-to-end deep learning model for human activity recognition from highly sparse body sensor data in internet of medical things environment, The Journal of Supercomputing, 77(3):2237–2250 https://doi.org/10.1007/s11227-020-03361-4
Hassan MM, Uddin MZ, Mohamed A, Almogren A (2018) A robust human activity recognition system using smartphone sensors and deep learning. Future Generation Computer Systems 81:307–313
Hou W, Li D, Xu C, Zhang H, Li T (2018) An advanced k nearest neighbor classification algorithm based on kd-tree, in 2018 IEEE International Conference of Safety Produce Informatization (IICSPI). IEEE pp 902–905
Huang F, Xie G (2009) Xiao R (2009) Research on ensemble learning, in. International Conference on Artificial Intelligence and Computational Intelligence 3:249–252
Imran HA, Latif U (2020) Hharnet: Taking inspiration from inception and dense networks for human activity recognition using inertial sensors, in 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET). IEEE pp 24–27
Ishimaru S, Hoshika K, Kunze K, Kise K, Dengel A (2017) Towards reading trackers in the wild: Detecting reading activities by eog glasses and deep neural networks, Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, p 704–711
Kalimuthu S, Perumal T, Yaakob R, Marlisah E, Babangida L (2021) Human activity recognition based on smart home environment and their applications, challenges, in 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, https://doi.org/10.1109/icacite51222.2021.9404753
Khan AM, Lee Y, Lee SY, Kim T (2010) Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis, in 2010 5th International Conference on Future Information Technology. pp 1–6
Kim E, Helal S, Cook D (2010) Human activity recognition and pattern discovery. IEEE Pervasive Computing 9(1):48–53
Kingma DP, Ba J, (2014) Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980
Krishnan NC, Panchanathan S (2008) Analysis of low resolution accelerometer data for continuous human activity recognition, in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. pp 3337–3340
Kwon M, You H, Kim J, Choi S (2018) Classification of various daily activities using convolution neural network and smartwatch, in 2018 IEEE International Conference on Big Data (Big Data) pp 4948–4951
Lee SM, Yoon SM, Cho H (2017) Human activity recognition from accelerometer data using convolutional neural network, in 2017 ieee international conference on big data and smart computing (bigcomp). IEEE, pp 131–134
Li H, Shrestha A, Fioranelli F, Le Kernec J, Heidari H, Pepa M, Cippitelli E, Gambi E, Spinsante S (2017) Multisensor data fusion for human activities classification and fall detection, in 2017 IEEE SENSORS pp 1–3
Liu L, Popescu M, Rantz M, Skubic M (2012) Fall detection using doppler radar and classifier fusion, in Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE https://doi.org/10.1109/bhi.2012.6211539
Liu C, Ying J, Han F, Ruan M (2018) Abnormal human activity recognition using bayes classifier and convolutional neural network. In 2018 IEEE 3rd international conference on signal and image processing (ICSIP) IEEE pp 33–37
Li X, Zhang Y, Li M, Marsic I, Yang J, Burd RS (2016) Deep neural network for rfid-based activity recognition, Proceedings of the Eighth Wireless of the Students, by the Students, and for the Students Workshop, pp. 24–26
Madeira R, Nunes L (2016) A machine learning approach for indirect human presence detection using IOT devices, in 2016 Eleventh International Conference on Digital Information Management (ICDIM) IEEE https://doi.org/10.1109/icdim.2016.7829781
Ma Y, Guo G (2014) Support vector machines applications. Springer, vol. 649
Mandong A, Munir U (2018) Smartphone based activity recognition using k-nearest neighbor algorithm, in Proceedings of the International Conference on Engineering Technologies, Konya, Turkey, pp 26–28
Masum AKM, Hossain ME, Humayra A, Islam S, Barua A, Alam GR (2019) A statistical and deep learning approach for human activity recognition. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). pp 1332–1337
Mekruksavanich S, Jitpattanakul A (2021) Lstm networks using smartphone data for sensor-based human activity recognition in smart homes. Sensors 21(5):1636
Mekruksavanich S, Jitpattanakul A (2020) Smartwatch-based human activity recognition using hybrid lstm network, in 2020 IEEE SENSORS. pp 1–4
Middya AI, Roy S, Mandal S, Talukdar R (2021) Privacy protected user identification using deep learning for smartphone-based participatory sensing applications, Neural Computing and Applications, 33(24):17 303–17 313 https://doi.org/10.1007/s00521-021-06319-6
Myung IJ, Pitt MA (1997) Applying occam’s razor in modeling cognition: A bayesian approach. Psychonomic bulletin & review. 4(1):79–95
Narkhede S (2018) Understanding auc-roc curve. Towards Data Science. 26:220–227
Paul P, George T (2015) An effective approach for human activity recognition on smartphone, in 2015 IEEE International Conference on Engineering and Technology (ICETECH), pp 1–3
Priyadarshini RK, Banu AB, Nagamani T (2019) Gradient boosted decision tree based classification for recognizing human behavior, in 2019 International Conference on Advances in Computing and Communication Engineering (ICACCE). pp 1–4
Python. https://www.python.org/ Accessed: 04 Apr 2021
Rahman A, Nahid N, Hassan I, Ahad M (2020) Nurse care activity recognition: Using random forest to handle imbalanced class problem, in Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers , pp 419–424
Rasheed MB, Javaid N, Alghamdi TA, Mukhtar S, Qasim U, Khan ZA, Raja MHB (2015) Evaluation of human activity recognition and fall detection using android phone, in 2015 IEEE 29th International Conference on Advanced Information Networking and Applications IEEE, 2015 p. 163–170
Reena JK, Parameswari R (2019) A smart health care monitor system in iot based human activities of daily living: A review. 2019 International Conference on Machine Learning. Big Data, Cloud and Parallel Computing (COMITCon), pp 446–448
Ronao CA, Cho SB (2015) Deep convolutional neural networks for human activity recognition with smartphone sensors, in Neural Information Processing. Springer International Publishing p 46–53
San-Segundo R, Blunck H, Moreno-Pimentel J, Stisen A, Gil-Martín M (2018) Robust human activity recognition using smartwatches and smartphones. Engineering Applications of Artificial Intelligence 72:190–202
Sekiguchi R, Abe K, shogo S, Kumano M, Asakura D, Okabe R, Kariya T, Kawakatsu M (2021) Phased human activity recognition based on gps, in Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, pp 396–400
Shan CY, Han PY, Yin OS (2020) Deep analysis for smartphone-based human activity recognition. In: 2020 8th International Conference on Information and Communication Technology (ICoICT). pp 1–5
Sousa Lima W, Souto E, El-Khatib K, Jalali R, Gama J (2019) Human activity recognition using inertial sensors in a smartphone: An overview. Sensors 19(14):3213
Straczkiewicz M, James P, Onnela JP (2021) A systematic review of smartphone-based human activity recognition methods for health research, npj Digital Medicine, 4(1) Oct. 2021. [Online]. Available: https://doi.org/10.1038/s41746-021-00514-4
Szandała T (2020) Review and comparison of commonly used activation functions for deep neural networks, in Bio-inspired Neurocomputing. Springer, pp 203–224
Tran DN, Phan DD (2016) Human activities recognition in android smartphone using support vector machine, in 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS) pp 64–68
Uddin MZ, Hassan MM (2019) Activity recognition for cognitive assistance using body sensors data and deep convolutional neural network. IEEE Sensors Journal. 19(19):8413–8419. https://doi.org/10.1109/jsen.2018.2871203
Ullah HA, Letchmunan S, Zia MS, Butt UM, Hassan FH, (2021) Analysis of deep neural networks for human activity recognition in videos—a systematic literature review, IEEE Access, vol 9, pp. 126 366–126 387, https://doi.org/10.1109/access.2021.3110610
Vesa AV, Vlad S, Rus R, Antal M, Pop C, Anghel I, Cioara T, Salomie I, (2020) Human activity recognition using smartphone sensors and beacon-based indoor localization for ambient assisted living systems, In: 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE p 205–212
Wang J, Chen Y, Hao S, Peng X, Hu L (2019) Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters 119:3–11. https://doi.org/10.1016/j.patrec.2018.02.010
Yalçin M, Tüfek N, Yalcin H (2018) Activity recognition of interacting people, in 2018 26th Signal Processing and Communications Applications Conference (SIU) p 1–4
Zebin T, Scully PJ, Ozanyan KB (2017) Evaluation of supervised classification algorithms for human activity recognition with inertial sensors. In 2017 IEEE SENSORS, IEEE, pp. 1–3
Acknowledgements
The research work of Asif Iqbal Middya is supported by UGC-NET Junior Research Fellowship (UGC-Ref. No.: 3684/(NET-JULY 2018)) provided by the University Grants Commission, Government of India.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no conflicts of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Middya, A.I., Kumar, S. & Roy, S. Activity recognition based on smartphone sensor data using shallow and deep learning techniques: A Comparative Study. Multimed Tools Appl 83, 9033–9066 (2024). https://doi.org/10.1007/s11042-023-15751-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-15751-w