IRO Journals
Journal of Innovative Image Processing

Wearable Gyroscope-Based Motion Tracking and Hand Gesture Recognition Using ANOVA for Feature Discrimination

Open Access
Published: 24 September, 2026
Pages: 1393-1412
  • Neela Harish , Neela Harish
    Assistant Professor, Department of Biomedical Engineering, Easwari Engineering College, Ramapuram, Chennai, India
    Assistant Professor, Department of Biomedical Engineering, Easwari Engineering College, Ramapuram, Chennai, India
  • Deepa S. , Deepa S.
    Assistant Professor, Department of Mathematics, Easwari Engineering College, Ramapuram, Chennai, India
    Assistant Professor, Department of Mathematics, Easwari Engineering College, Ramapuram, Chennai, India
  • Uma Loganathan , Uma Loganathan
    Assistant Professor, Department of Mathematics, Easwari Engineering College, Ramapuram, Chennai, India
    Assistant Professor, Department of Mathematics, Easwari Engineering College, Ramapuram, Chennai, India
  • Kavitha J. , Kavitha J.
    Assistant Professor, Department of Mathematics, Easwari Engineering College, Ramapuram, Chennai, India
    Assistant Professor, Department of Mathematics, Easwari Engineering College, Ramapuram, Chennai, India
  • Praveen Kumar Praveen Kumar
    Associate Professor, Department of Electronics and Communication Engineering, Easwari Engineering College, Ramapuram, Chennai, India
    Associate Professor, Department of Electronics and Communication Engineering, Easwari Engineering College, Ramapuram, Chennai, India
view PDF
view PDF

How to Cite

Harish, Neela, Deepa S., Uma Loganathan, Kavitha J., and Praveen Kumar. 2026. “Wearable Gyroscope-Based Motion Tracking and Hand Gesture Recognition Using ANOVA for Feature Discrimination”. Journal of Innovative Image Processing 8 (4): 1393-1412. https://doi.org/10.36548/jiip.2026.4.004.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

Gyroscope-based Gesture Recognition
Wearable Sensors
Inertial Sensing
Artificial Neural Network
ANOVA
Rehabilitation Monitoring
Human–Computer Interaction

Abstract

Hand gesture recognition has been identified as an important field of research in the healthcare sector, specifically for applications in rehabilitation, assistive communication, and human computer interaction. Upper limb motion tracking provides useful information when determining the recovery of movement in patients suffering from conditions such as stroke, Parkinson’s disease, spinal cord injuries, and others. Existing methods utilize camera-based systems or EMG sensors; however, their effectiveness may be hindered by various factors such as lighting conditions, privacy issues, electrode positioning, and computational difficulties. In this paper, a wearable hand gesture recognition system employing an MPU6500 gyroscope to detect tri-axial angular velocity corresponding to five different pre-defined hand gestures: Center, Up, Down, Tilt Left, and Tilt Right. The detected signals are then filtered through a low-pass filter and normalized before statistical features are obtained in the form of mean, variance, root mean square (RMS), and signal magnitude vector (SMV). Statistical significance of the features is assessed through one-way ANOVA while the Artificial Neural Network (ANN) model is used for classifying the gestures. The proposed device was tested on signals obtained from 19 subjects aged between 14 and 16 years. Experimental results have shown that the classification accuracy was 94.2%, while the precision was 93.6%, the recall was 92.8%, and the F1-Score was 93.1%. It can be concluded from the above results that the system employed is a portable system for gesture recognition, thus making it ideal for applications in rehabilitation monitoring, assistive communication, and wearables.

References

  1. Ozioko, Oliver, and Ravinder Dahiya. ”Smart Tactile Gloves for Haptic Interaction, Communication, and Rehabilitation.” Advanced Intelligent Systems 4, no. 2 (2022): 2100091.
  2. Tam, Simon, Mounir Boukadoum, Alexandre Campeau-Lecours, and Benoit Gosselin. ”A Fully Embedded Adaptive Real-Time Hand Gesture Classifier Leveraging HD-sEMG and Deep Learning.” IEEE transactions on biomedical circuits and systems 14, no. 2 (2019): 232-243.
  3. Piyathilaka, Lasitha, Jung-Hoon Sul, Sanura Dunu Arachchige, Amal Jayawardena, and Diluka Moratuwage. ”Advances in EMG Signal Processing and Pattern Recognition: Techniques, Challenges, And Emerging Applications.” Electronics 15, no. 3 (2026): 590.
  4. Garcia-de-Villa, Sara, David Casillas-Pérez, Ana Jiménez-Martín, and Juan Jesús García-Domínguez. ”Inertial Sensors for Human Motion Analysis: A Comprehensive Review.” IEEE Transactions on Instrumentation and Measurement 72 (2023): 1-39.
  5. Liang, Bangyi, Ning Li, Gongxin Li, Jie Huang, Zhuoheng Yu, and Xingang Zhao. ”Sensing Technologies for Hand Gesture Recognition in Human–Robot Interaction: A Review.” IEEE Sensors Journal 26, no. 2 (2025): 1501-1519.
  6. Sarowar, Md Selim, Nur E. Jannatul Farjana, Md Asraful Islam Khan, Md Abdul Mutalib, Syful Islam, and Mohaiminul Islam. ”Hand Gesture Recognition Systems: A Review of Methods, Datasets, and Emerging Trends.” International Journal of Computer Applications 975 (2025): 8887.
  7. Pyun, Kyung Rok, Kangkyu Kwon, Myung Jin Yoo, Kyun Kyu Kim, Dohyeon Gong, Woon-Hong Yeo, Seungyong Han, and Seung Hwan Ko. ”Machine-learned Wearable Sensors for Real-Time Hand-Motion Recognition: Toward Practical Applications.” National science review 11, no. 2 (2024): nwad298.
  8. Alqudah, Hamzah. Modelling, Regulating and Controlling Cardiovascular Responses by using Wearable Sensors. University of Technology Sydney (Australia), 2019.
  9. Hurtado-Perez, Andres Emilio, Manuel Toledano-Ayala, Irving A. Cruz-Albarran, Alejandra Lopez-Zúñiga, Jesús Adrián Moreno-Perez, Alejandra Álvarez-López, Juvenal Rodriguez-Resendiz, and Carlos A. Perez-Ramirez. ”Use of Technologies For The Acquisition and Processing Strategies for Motion Data Analysis.” Biomimetics 10, no. 5 (2025): 339.
  10. Abbaspour, Sara, Autumn Naber, Max Ortiz-Catalan, Hamid GholamHosseini, and Maria Lindén. ”Real-time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements.” Sensors 21, no. 16 (2021): 5677.
  11. Zhao, Hongyang, Shuangquan Wang, Gang Zhou, and Daqing Zhang. ”Ultigesture: A Wristband-Based Platform for Continuous Gesture Control in Healthcare.” Smart Health 11 (2019): 45-65.
  12. Heir, Fereshteh Manafzadeh, Hossein Najafzadeh, and Sarvenaz Erfani. ”A Hybrid CNN and Reinforcement Learning Framework for Speaker Identification Using Mel-Spectrogram and Continuous Wavelet Transform Features.” Scientific Reports 16, no. 1 (2026): 5954.
  13. Noh, Donghyeon, Hojin Yoon, and Donghun Lee. ”A Decade of Progress in Human Motion Recognition: A Comprehensive Survey from 2010 to 2020.” IEEE Access 12 (2024): 5684-5707.
  14. Hashi, Abdirahman Osman, Siti Zaiton Mohd Hashim, and Azurah Bte Asamah. ”A Systematic Review of Hand Gesture Recognition: An Update from 2018 to 2024.” IEEE Access 12 (2024): 143599-143626.
  15. Liu, Wentao, Yuxin Zhang, Mingyu Zhang, Xue Chen, Shihao Sun, and Guizhi Xu. ”A Real-Time Wrist Action Reconstruction System Design Based on BP Neural Network Model to Predict Multiple FES Parameters.” IEEE Sensors Journal 25, no. 14 (2025): 27353-27366.
  16. Jiang, Shuo, Peiqi Kang, Xinyu Song, Benny PL Lo, and Peter B. Shull. ”Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey.” IEEE Reviews in Biomedical Engineering 15 (2021): 85-102.
  17. Tchantchane, Rayane, Hao Zhou, Shen Zhang, and Gursel Alici. ”A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors.” Advanced intelligent systems 5, no. 10 (2023): 2300207.
  18. Sardadvar, Fatemeh, Valentin Kennel, Athina Tome, Nurcennet Kaynak, Alexa Straus, Rok Kos, Felix Schmidt, Alexander Heinrich Nave, and Bert Arnrich. ”A Technical Insight into Sensor-S Study: Effect of Wearable Sensors on Patient Engagement and Motivation in Post-stroke Rehabilitation.” In International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence, Cham: Springer Nature Switzerland, 2025, 404-412.
  19. Stan, Ionel Eduard, Daniela D’Auria, and Paolo Napoletano. ”A Systematic Literature Review of Innovations, Challenges, And Future Directions in Telemonitoring and Wearable Health Technologies.” IEEE Journal of Biomedical and Health Informatics 30, no. 3 (2025): 2630-2645.
  20. Siddiqui, Uzma Abid, Farman Ullah, Asif Iqbal, Ajmal Khan, Rehmat Ullah, Sheroz Paracha, Hassan Shahzad, and Kyung-Sup Kwak. ”Wearable-sensors-based Platform for Gesture Recognition of Autism Spectrum Disorder Children Using Machine Learning Algorithms.” Sensors 21, no. 10 (2021): 3319.
  21. Yoo, Seohyun, Eunbae Jeon, Joonseo Hyeon, and Jaehyuk Cho. ”Adaptive Ensemble Techniques Leveraging BERT based Models for Multilingual Hate Speech Detection in Korean and English.” Scientific Reports 15, no. 1 (2025): 19844.
  22. Hu, Jingcheng, Yinmin Zhang, Qi Han, Daxin Jiang, Xiangyu Zhang, and Heung-Yeung Shum. ”Open-reasoner-zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model.” Advances in Neural Information Processing Systems 38 (2026): 162239-162262.