Skip to main content
SpringerLink
Log in

A cloud-assisted smart monitoring system for sports activities using SVM and CNN

  • Data analytics and machine learning
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The emergence of data-driven analytics and intelligent monitoring systems is radically transforming the world of competitive sports. However, robust real-time activity recognition remains elusive for rapid-paced sports like table tennis due to the complexity of strokes and quick maneuvering. This paper tries to overcome this challenge through an innovative cloud-supported system integrating the Internet of Things (IoT), machine learning, and wearable sensors for automated analysis of table tennis gameplay. We strategically set up a multi-camera IoT system around a table tennis court to compile a rich labeled image dataset encapsulating over 15,000 frames. To improve model generalization, the images captured various playing styles, lighting conditions, and camera angles. We developed tailored SVM and CNN architectures optimized for table tennis activity classification. The models were trained on GPU-accelerated platforms using the curated dataset. After hyperparameter tuning and cross-validation, the CNN model achieved 96.2% accuracy, a precision value of 96.3%, a recall value of 95.2%, and an F1-score of 96.3% in classifying standard strokes and serves. Additionally, the models exhibited impressive processing speeds of 22–34 fps, enabling real-time utilization. Our proposed model outperformed the latest models in this field with higher accuracy, recall values, F1-score, and precision. While expanding the dataset diversity and testing variations of deep network architectures could further enhance performance, this paper demonstrates a crucial leap toward helping real-time analytics in table tennis using an AI-powered computer vision approach. The hybrid system combining IoT, wearables, and machine learning establishes a framework to transform data into actionable and timely insights for table tennis players and coaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data availability

Inquiries about data availability should be addressed to the authors.

References

  • Agyeman MO, Guerrero AF and Vien QT (2022) A review of classification techniques for arrhythmia patterns using convolutional neural networks and Internet of Things (IoT) devices. IEEE Access

  • Alam T, Gupta R (2022) Federated learning and its role in the privacy preservation of IoT devices. Future Internet 14(9):246

    Article  Google Scholar 

  • Ali M, Yin B, Bilal H et al (2023) Advanced efficient strategy for detection of dark objects based on spiking network with multi-box detection. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-16852-2

    Article  Google Scholar 

  • Ali M, Yin B, Kumar A, Sheikh AM et al (2020) Reduction of multiplications in convolutional neural networks. In: 2020 39th Chinese Control Conference (CCC) (pp 7406–7411). IEEE. https://doi.org/10.23919/CCC50068.2020.9188843

  • Almaraz-Rivera JG, Perez-Diaz JA, Cantoral-Ceballos JA (2022) Transport and application layer DDoS attacks detection to IoT devices by using machine learning and deep learning models. Sensors 22(9):3367

    Article  Google Scholar 

  • Aslam MS, Dai X, Hou J, Li Q, Ullah R, Ni Z, Liu Y (2020) Reliable control design for composite-driven scheme based on delay networked T-S fuzzy system. Int J Robust Nonlinear Control 30(4):1622–1642

    Article  MathSciNet  Google Scholar 

  • Awotunde JB, Ayoade OB, Ajamu GJ, AbdulRaheem M and Oladipo ID (2022) Internet of things and cloud activity monitoring systems for elderly healthcare. In: Internet of things for human-centered design: application to elderly healthcare. Singapore: Springer Nature Singapore, pp 181–207

  • Ben Amor A, Jebri S, Abid M and Meddeb A (2023) A secure lightweight mutual authentication scheme in social industrial IoT environment. J Supercomput 79:13578–13600

  • Carone S, Pappalettera G, Casavola C, De Carolis S, Soria L (2023) A support vector machine-based approach for bolt loosening monitoring in industrial customized vehicles. Sensors 23(11):5345

    Article  Google Scholar 

  • Chao G, Gang W (2021) Sports training teaching device based on big data and cloud computing. J Healthcare Eng. https://doi.org/10.1155/2021/7339486

    Article  Google Scholar 

  • Chen Z (2019) Observer-based dissipative output feedback control for network T-S fuzzy systems under time delays with mismatch premise. Nonlinear Dyn 95:2923–2941

    Article  Google Scholar 

  • Dou H, Liu Y, Chen S et al (2023) A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways. Soft Comput 27:16373–16388. https://doi.org/10.1007/s00500-023-09164-y

    Article  Google Scholar 

  • Emamian M, Eskandari A, Aghaei M, Nedaei A, Sizkouhi AM, Milimonfared J (2022) Cloud computing and IoT based intelligent monitoring system for photovoltaic plants using machine learning techniques. Energies 15(9):3014

    Article  Google Scholar 

  • Feng X, Zhu X, Han QL, Zhou W, Wen S, Xiang Y (2022) Detecting vulnerability on IoT device firmware: a survey. IEEE/CAA J Automatica Sinica 10(1):25–41

    Article  Google Scholar 

  • Hannan A, Shafiq MZ, Hussain F, Pires IM (2021) A portable smart fitness suite for real-time exercise monitoring and posture correction. Sensors 21(19):6692

    Article  Google Scholar 

  • Heidari A, Navimipour NJ, Jamali MAJ, Akbarpour S (2023) A hybrid approach for latency and battery lifetime optimization in IoT devices through offloading and CNN learning. Sustain Comput Inform Syst 39:100899

    Google Scholar 

  • Iqbal MJ, Farhan M, Ullah F, Srivastava G, Jabbar S (2023) Intelligent multimedia content delivery in 5G/6G networks: a reinforcement learning approach. Trans Emerg Telecommun Technol. https://doi.org/10.1002/ett.4842

    Article  Google Scholar 

  • Krishnaraj N (2022) Implementation of a human activity monitoring system through IoT sensor and Blynk cloud platform. J Inf Technol Digit World 4(2):105–113

    Article  Google Scholar 

  • Li C (2022) Sports-assisted education based on a support vector machine and genetic algorithm. Math Prob Eng. https://doi.org/10.1155/2022/5194937

    Article  Google Scholar 

  • Li Q, Kumar P, Alazab M (2022) IoT-assisted physical education training network virtualization and resource management using a deep reinforcement learning system. Complex & Intelligent Systems, pp 1–14

    Google Scholar 

  • Maaloul K, Brahim L, Abdelhamid NM (2023) Real-time human activity recognition from smart phone using linear support vector machines. TELKOMNIKA (telecommunication Computing Electronics and Control) 21(3):574–583

    Article  Google Scholar 

  • Malekloo A, Ozer E, AlHamaydeh M, Girolami M (2022) Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Struct Health Monit 21(4):1906–1955

    Article  Google Scholar 

  • Manickam P, Mariappan SA, Murugesan SM, Hansda S, Kaushik A, Shinde R, Thipperudraswamy SP (2022) Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors 12(8):562

    Article  Google Scholar 

  • Meena JS, Choi SB, Jung SB, Kim JW (2023) Electronic textiles: new age of wearable technology for healthcare and fitness solutions. Mater Today Bio 19:100565

    Article  Google Scholar 

  • Muhammad, Qaisar I, Majid A, Shamrooz S (2023) Adaptive event-triggered robust H∞ control for Takagi-Sugeno fuzzy networked Markov jump systems with time-varying delay. Asian J Control 25(1):213–228

    Article  MathSciNet  Google Scholar 

  • Oikonomou G, Duquennoy S, Elsts A, Eriksson J, Tanaka Y, Tsiftes N (2022) The Contiki-NG open source operating system for next generation IoT devices. SoftwareX 18:101089

    Article  Google Scholar 

  • Ortega FB, Leskošek B, Gil-Cosano JJ, Mäestu J, Tomkinson GR, Ruiz JR, Mäestu E, Starc G, Milanovic I, Tammelin TH, Sorić M (2023) European fitness landscape for children and adolescents: updated reference values, fitness maps and country rankings based on nearly 8 million test results from 34 countries gathered by the FitBack network. Br J Sports Med 57(5):299–310

    Article  Google Scholar 

  • Parameswari V, Pushpalatha S (2020) Human activity recognition using SVM and deep learning. Eur J Mol Clin Med 7(4):1984–1990

    Google Scholar 

  • Pattewar G, Mahamuni N, Nikam H, Loka O, Patil R (2022) Management of IoT devices security using blockchain—a review. Sentiment Anal Deep Learn 2021:735–743

    Article  Google Scholar 

  • Rjoub G, Wahab OA, Bentahar J, Bataineh A (2022) Trust-driven reinforcement selection strategy for federated learning on IoT devices. Computing. https://doi.org/10.1007/s00607-022-01078-1

    Article  Google Scholar 

  • Segura-Garcia SJ, Garcia-Pineda M, Tamarit-Tronch M, Cibrian RM, Salvador-Palmer R (2018) Cost-effective eHealth system based on a multi-sensor system-on-chip platform and data fusion in cloud for sport activity monitoring. Electronics 7(9):183

    Article  Google Scholar 

  • Shamrooz M, Li Q, Hou J (2021) Fault detection for asynchronous T-S fuzzy networked Markov jump systems with new event-triggered scheme. IET Control Theory Appl 15(11):1461–1473

    Article  MathSciNet  Google Scholar 

  • Shrivastava A, Haripriya D, Borole YD, Nanoty A, Singh C, Chauhan D (2022) High performance FPGA based secured hardware model for IoT devices. Int J Syst Assur Eng Manage 13(S1):736–741

    Article  Google Scholar 

  • Thouti S, Venu N, Rinku DR, Arora A, Rajeswaran N (2022) Investigation on identify the multiple issues in IoT devices using Convolutional Neural Network. Measur Sens 24:100509

    Article  Google Scholar 

  • Ullah R, Dai X, Sheng A (2020) Event-triggered scheme for fault detection and isolation of non-linear system with time-varying delay. IET Control Theory Appl 14(16):2429–2438

    Article  MathSciNet  Google Scholar 

  • Wang S, Li H, Chen J, Wang J, Deng Y (2022) DAG blockchain-based lightweight authentication and authorization scheme for IoT devices. J Inf Secur Appl 66:103134

    Google Scholar 

  • Wang L, Zhai Q, Yin B et al (2019) Second-order convolutional network for crowd counting. In: Proceeding of SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980T. https://doi.org/10.1117/12.2540362

  • Wu X, Zhou J, Zheng M, Chen S, Wang D, Anajemba J, Zhang G, Abdelhaq M, Alsaqour R, Uddin M (2022) Cloud-based deep learning-assisted system for diagnosis of sports injuries. J Cloud Comput 11(1):1–18

    Article  Google Scholar 

  • Wu Q, Li X, Wang K et al (2023) Regional feature fusion for on-road detection of objects using camera and 3D-LiDAR in high-speed autonomous vehicles. Soft Comput 27:18195–18213. https://doi.org/10.1007/s00500-023-09278-3

    Article  Google Scholar 

  • Xu H, Sun Z, Cao Y et al (2023) A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things. Soft Comput. https://doi.org/10.1007/s00500-023-09037-4

    Article  Google Scholar 

  • Yao W, Guo Y, Wu Y and Guo J (2017) Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties. In: 2017 36th Chinese Control Conference (CCC) (pp 4192–4197). IEEE. https://doi.org/10.23919/ChiCC.2017.8028015

  • Yin B, Khan J, Wang L, Zhang J and Kumar A (2019) Real-time lane detection and tracking for advanced driver assistance systems. In: 2019 Chinese Control Conference (CCC) (pp 6772–6777). IEEE. https://doi.org/10.23919/ChiCC.2019.8866334

  • Zhang Y, Hu Y, Jiang N, Yetisen AK (2022) Wearable artificial intelligence biosensor networks. Biosens Bioelectron 219:114825

    Article  Google Scholar 

Download references

Funding

No funding was provided for the completion of this study.

Author information

Authors and Affiliations

  1. School of Electronic Information Engineering, Henan Institute of Technology, 453003, Xinxiang, Henan, China

    Kang Chang

  2. School of Physical Education, Huaibei Normal University, Huaibei, 235000, Anhui, China

    Peng Sun

  3. Department of Computer Science, Comsats University Islamabad (CUI), Sahiwal Campus, Pakistan

    Muhammad Usman Ali

Authors
  1. Kang Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Usman Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Sun.

Ethics declarations

Conflict of interest

No conflict of interest has been declared by the authors.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, K., Sun, P. & Ali, M.U. A cloud-assisted smart monitoring system for sports activities using SVM and CNN. Soft Comput 28, 339–362 (2024). https://doi.org/10.1007/s00500-023-09404-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-09404-1

Keywords

Search

Navigation