Fall Risk Prediction System Using Wearable Sensors and LSTM - Based Machine Learning

References

[1] F. Bagala, A. Cappello, A. Chiari, J. Klenk, C. W. Becker and W. Zijlstra, "Evaluation of accelerometer- based fall detection algorithms on real-world falls," Gait & Posture, vol. 33, no. 3, pp. 556–560, Mar. 2011.

[2] A. Bourke et al., "Evaluation of waist-mounted tri- axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities," Journal of Biomechanics, vol. 43, no. 15, pp. 3051–3057, 2010.

[3] M. Sucerquia, J. D. López and J. F. Vargas-Bonilla, "SisFall: A Fall and Movement Dataset," Sensors, vol. 17, no. 1, pp. 198, Jan. 2017.

[4] G. Özdemir and F. Barshan, "Detecting falls with wearable sensors using machine learning techniques," Sensors, vol. 14, no. 6, pp. 10691–10708, 2014.

[5] T. M. Khan, M. Y. Ma, S. Khan and M. Q. Mehmood, "Smart wearable system for fall detection and posture recognition using deep learning," IEEE Access, vol. 9, pp. 117239–117252, 2021.

[6] C. Chen, J. K. Aggarwal, J. L. Wachs and M. Chen, "Vision-based fall detection," IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 5, pp. 702–712, May 2011.

[7] S. Wang, J. Li, Y. Wang and Z. Zhang, "A wearable fall detection system based on LSTM algorithm and edge computing," 2020 IEEE Global Communications Conference (GLOBECOM), Taipei, Taiwan, 2020, pp. 1–6.

[8] J. J. M. Lee and C. W. C. Woo, "Elderly fall detection based on wearable sensors using LSTM recurrent neural networks," 2021 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2021, pp. 1–4.

[9] Y. Li, J. Zhang, H. Liu and Z. Wang, "Fall detection system for elderly using wearable sensors and LSTM recurrent neural networks," 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, South Korea, 2020, pp. 567–572.

[10] M. Alqahtani, M. Alshammari, M. A. Khan and R. Islam, "Fall detection and prevention system for elderly using wearable sensors: A review," IEEE Access, vol. 10, pp. 78245–78266, 2022.

[11] M. Mubashir, L. Shao and L. Seed, "A survey on fall detection: Principles and approaches," Neurocomputing, vol. 100, pp. 144–152, 2013.

[12] D. N. Tripathi and R. M. Tripathi, "Elderly fall detection using wearable sensors: A review," 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India, 2021, pp. 276–281.

[13] J. Rougier, A. Meunier, A. St-Arnaud and J. Rousseau, "Robust video surveillance for fall detection based on human shape deformation," IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 5, pp. 611–622, 2011.

[14] B. Li, X. Zhao, M. Liu and X. Meng, "Research on fall detection method based on wearable devices and edge computing," 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), Shenyang, China, 2020, pp. 156–159.

[15] P. Casilari, J. A. Santoyo-Ramón and J. M. Cano- García, "Analysis of Android device-based solutions for fall detection," Sensors, vol. 15, no. 8, pp. 17827– 17894, 2015.

[16] A. F. da Silva, L. P. R. Almeida and M. A. L. Marques, "Real-time mobile fall detection system based on artificial neural networks," 2020 IEEE Symposium on Computers and Communications (ISCC), Rennes, France, 2020, pp. 1–6.

[17] D. D. Salgado and M. Vargas, "Wearable fall detection systems for elderly: A review," 2019 IEEE Colombian Conference on Communications and Computing (COLCOM), Medellín, Colombia, 2019, pp. 1–6.

[18] A. Cippitelli, F. Fioranelli, E. Gambi and S. Spinsante, "Radar and RGB-depth sensors for fall detection: A review," IEEE Sensors Journal, vol. 17, no. 12, pp. 3585–3604, 2017.

[19] R. De Miguel, J. L. Sevillano, C. Casteleiro-Roca and A. Fernández-Costa, "Fall detection and alert system using Raspberry Pi and computer vision techniques," Sensors, vol. 22, no. 5, pp. 1792, 2022.

[20] L. Wang, Y. Guo and H. Liu, "A smartphone-based fall detection algorithm for the elderly using a fuzzy logic approach," Applied Sciences, vol. 11, no. 3, pp. 1096, 2021

@article{1708547,
      author = {Arshiya Fatima, Shaistha Khanum, Siddhi Singh Rathor, Syed Rayyan Ahmed, Taufiq Ahmed I},
      title = {Fall Risk Prediction System Using Wearable Sensors and LSTM - Based Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {1270-1277},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708547.pdf},
      abstract = {Falls represent a significant health hazard, particularly among the elderly and patients with mobility impairments. Timely prediction of fall risk can play a vital role in reducing injuries, hospitalization, and related healthcare costs. This paper proposes a cost-effective and intelligent fall risk prediction system that combines wearable sensors with machine learning to assess the likelihood of falls in real time. The system architecture includes an ESP- 32 microcontroller, an accelerometer sensor (MPU6050), a force pressure sensor, resistors for signal conditioning, a buzzer for immediate alerts, and a common power supply for mobility. These components are embedded into a wearable module that continuously collects motion and pressure data. The collected data is transmitted to a remote server where it is preprocessed and analyzed using a Long Short- Term Memory (LSTM) model trained to detect irregular movement patterns that typically precede a fall. The system demonstrated robust performance, achieving a PSNR value of 10.3 dB and an SSIM score of 0.4, validating its ability to preserve structural features in time-series sensor signals. Performance testing revealed that even with limited training (three epochs due to hardware constraints), the model was capable of identifying motion anomalies associated with fall risk. A buzzer-based local alert mechanism provides instant feedback to the user or nearby caregiver. The usability of the system was positively received, especially for its lightweight design and intuitive functionality. The device holds strong potential for further development, including mobile application integration for cloud-based visualization, enhanced sensor fusion (e.g., gyroscope, ECG), and edge-AI implementation for faster local processing. This research contributes toward creating an affordable, scalable, and effective fall risk prediction solution tailored for home-based elderly care, post-operative rehabilitation, and assistive living facilities.},
      keywords = {Fall Risk Prediction, LSTM, Wearable Sensors, ESP-32, Accelerometer, Force Pressure Sensor, IoT in Healthcare, MIT App Inventor, Real-Time Monitoring, Elderly Care},
      month = {May},
  }