Low-cost Machine Learning-Based Sensor-based Activity Recognition for Patients with Financial Difficulties

Year : 2025 | Volume : 12 | Issue : 03 | Page : 7 17
    By

    Harsh Kumar,

  • Zubair Fayaz,

  • Khalil Ahmed,

  • Gagandeep Singh,

  1. Research Scholar, Department of Computer Science, Akal University, Punjab, India
  2. Research Scholar, Department of Computer Science, Akal University, Punjab, India
  3. Research Scholar, Department of Computer Science, Baba Ghulam Shah Badshah University, Rajouri, Jammu & Kashmir, India
  4. Research Scholar, Department of Computer Science, Akal University, Punjab, India

Abstract

Elderly and schizophrenic patients are compelled to obtain treatment at home due to a lack of resources, putting them at risk for patient neglect and other health issues. This is particularly troublesome for prescription yoga or fitness programs, which are hard for doctors to keep an eye on all the time. We have looked into an automated method that tracks patients’ everyday behaviors using machine learning- based techniques in order to solve this issue. Our approach collects patient activity records, including walking, standing, sitting, and laying down and upstairs, using accelerometers and gyroscope devices. With applications in sports, healthcare, child monitoring, child writing monitoring, and yoga feedback systems, we use a combination of IoT and machine learning approaches to track patient activities. The architecture of our system is a significant advancement, especially for patients with modest incomes who cannot afford hospital stays. To accurately measure daily activity, we have classified patient activity using a support tensor machine-based technique.

Keywords: Machine Learning, Sensor Categorization, Human Activity Recognition, IoT, Physical Sensors

[This article belongs to Recent Trends in Sensor Research & Technology ]

How to cite this article:
Harsh Kumar, Zubair Fayaz, Khalil Ahmed, Gagandeep Singh. Low-cost Machine Learning-Based Sensor-based Activity Recognition for Patients with Financial Difficulties. Recent Trends in Sensor Research & Technology. 2025; 12(03):7-17.
How to cite this URL:
Harsh Kumar, Zubair Fayaz, Khalil Ahmed, Gagandeep Singh. Low-cost Machine Learning-Based Sensor-based Activity Recognition for Patients with Financial Difficulties. Recent Trends in Sensor Research & Technology. 2025; 12(03):7-17. Available from: https://journals.stmjournals.com/rtsrt/article=2025/view=235186


References

  1. Zheng G. A novel attention-based convolution neural network for human activity recognition. IEEE Sensors Journal. 2021 Oct 22;21(23):27015-25.
  2.  Tan DY, Ting HY, Lau SB. A review on badminton motion analysis. In2016 International Conference on Robotics, Automation and Sciences (ICORAS) 2016 Nov 5 (pp. 1-4). IEEE
  3. Yadav SK, Tiwari K, Pandey HM, Akbar SA. A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions. Knowledge-Based Systems. 2021 Jul 8;223:106970.
  4. Liu T, Liu H, Li YF, Chen Z, Zhang Z, Liu S. Flexible FTIR spectral imaging enhancement for industrial robot infrared vision sensing. IEEE Transactions on Industrial Informatics. 2019 Aug 12;16(1):544-54.
  5. Liu T, Wang J, Yang B, Wang X. NGDNet: Nonuniform Gaussian-label distribution learning for infrared head pose estimation and on-task behavior understanding in the classroom. Neurocomputing. 2021 May 14;436:210-20.
  6. Mliki H, Bouhlel F, Hammami M. Human activity recognition from UAV-captured video sequences. Pattern Recognition. 2020 Apr 1;100:107140.
  7. Ahad MA, Antar AD, Ahmed M. IoT sensor-based activity recognition. IoT Sensor-Based Activity Recognition. 2020;2.
  8. Mehta A, Vaddadi SK, Sharma V, Kala P. A phase-wise analysis of machine learning based human activity recognition using inertial sensors. In2020 IEEE 17th India Council International conference (INDICON) 2020 Dec 10 (pp. 1-7). IEEE.
  9. Gjoreski M, Gjoreski H, Luštrek M, Gams M. How accurately can your wrist device recognize daily activities and detect falls?. Sensors. 2016 Jun 1;16(6):800.
  10. Gupta A, Gupta HP. Yogahelp: Leveraging motion sensors for learning correct execution of yoga with feedback. IEEE Transactions on Artificial Intelligence. 2021 Jul 9;2(4):362-71..
  11. Ahmad N, Ghazilla RA, Khairi NM, Kasi V. Reviews on various inertial measurement unit (IMU) sensor applications. International Journal of Signal Processing Systems. 2013 Dec;1(2):256-62..
  12. Li S, Xu LD, Zhao S. The internet of things: a survey. Information systems frontiers. 2015 Apr;17(2):243-59.
  13. He W, Yan G, Da Xu L. Developing vehicular data cloud services in the IoT environment. IEEE transactions on industrial informatics. 2014 Jan 10;10(2):1587-95..
  14. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015 Jul 17;349(6245):255-60..
  15. Zantalis F, Koulouras G, Karabetsos S, Kandris D. A review of machine learning and IoT in smart transportation. Future Internet. 2019 Apr 10;11(4):94.
  16. Ma Z, Yang LT, Lin M, Zhang Q, Dai C. Weighted support tensor machines for human activity recognition with smartphone sensors. IEEE Transactions on Industrial Informatics. 2021 Feb 23.
  17. Airaksinen M, Raitio T, Story B, Alku P. Quasi closed phase glottal inverse filtering analysis with weighted linear prediction. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2013 Dec 11;22(3):596-607.
Regular Issue Subscription Original Research
Volume 12
Issue 03
Received 28/05/2025
Accepted 02/07/2025
Published 30/12/2025
Publication Time 216 Days
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