Development of a smart wearable device for fall and slip detection and warning for the elderly people
Main Article Content
Abstract
In the elderly, due to the degeneration of the muscles along with visual impairment, mobility becomes more difficult than in other ages. Upon moving, the elderly can be susceptible to several factors such as slips or obstacles which can cause unintended fall events and can lead to different degrees of injury, from minor trauma to more severe and even life-threatening injuries. In this work, the characteristics of movement and falling in the elderly are first studied, thereby finding thresholds for determining fall events for motion parameters to detect ahead of time the fall event. Therefore, necessary warnings can be promptly delivered to the users or caregivers, otherwise distress signal can be sent wirelessly to request assistance if the elderly is unable to stand up. This will help minimize the negative impact on the elderly caused by the fall event. The paper proposes to base the research on motion and fall features of the old people to build a wearable device which combines gyroscope accelerometer, a self-developed fall sensor and heart rate sensor for fall detection and warning. The device also employs parameter thresholds to detect forward and backward fall events as well as to provide accurate information about other familiar activities such as standing and sitting, lean forward, backward, left or right. The threshold-based method we used in determining the body states correctly identified the states: 93.33% steady state, 86.67% fallible state and 96.67% slip state. Moreover, the device can also achieve 90% accurate information about the user's heart rate.
Keywords
elderly, fall, slip, detection, warning, sensor, wearable device
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
[1] Sharif, S. I., Al-Harbi, A. B., Al-Shihabi, A. M., Al-Daour, D. S. and Sharif, R. S., Falls in the elderly: assessment of prevalence and risk factors. Pharmacy Practice (Granada), 16(3), 2018. https://doi.org/10.18549/PharmPract.2018.03.1206
[2] Takashima, K., Wada, K., Tra, T. T. and Smith, D.R., A review of Vietnam’s healthcare reform through the Direction of Healthcare Activities (DOHA). Environmental Health and Preventive Medicine, 2017, 22(1), pp.1-7. https://doi.org/10.1186/s12199-017-0682-z
[3] Hoang, D. K., Le, N. M., Vo‐Thi, U. P., Nguyen, H.G., Ho‐Pham, L. T. and Nguyen, T. V., Mechanography assessment of fall risk in older adults: the Vietnam Osteoporosis Study. Journal of Cachexia, Sarcopenia and Muscle, 2021. https://doi.org/10.1002/jcsm.12751
[4] Mubashir, M., Shao, L. and Seed, L., A survey on fall detection: Principles and approaches, Neurocomputing, 100, 2013, pp.144-152. https://doi.org/10.1016/j.neucom.2011.09.037
[5] Kadhum, A.A., Al-Libawy, H. and Hussein, E.A., May. An accurate fall detection system for the elderly people using smartphone inertial sensors. In Journal of Physics: Conference Series (Vol. 1530, No. 1, p. 012102), 2020. https://doi.org/10.1088/1742-6596/1530/1/012102
[6] Aguiar, B., Rocha, T., Silva, J. and Sousa, I., June. Accelerometer-based fall detection for smartphones. In 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA) 2014, pp. 1-6. IEEE. https://doi.org/10.1109/MeMeA.2014.6860110
[7] Wang, F.T., Chan, H. L., Hsu, M. H., Lin, C. K., Chao, P. K. and Chang, Y. J., Threshold-based fall detection using a hybrid of tri-axial accelerometer and gyroscope. Physiological Measurement, 39 (10), 2018, p.105002. https://doi.org/10.1088/1361-6579/aae0eb
[8] Lin, C. L., Chiu, W. C., Chu, T. C., Ho, Y. H., Chen, F. H., Hsu, C. C., Hsieh, P. H., Chen, C. H., Lin, C. C. K., Sung, P. S. and Chen, P. T., Innovative head-mounted system based on inertial sensors and magnetometer for detecting falling movements. Sensors, 20(20), 2020, p.5774. https://doi.org/10.3390/s20205774
[9] Trkov, M., Chen, K., Yi, J. and Liu, T., Inertial sensorbased slip detection in human walking. IEEE Transactions on Automation Science and Engineering, 16(3), 2019, pp.1399-1411. https://doi.org/10.1109/TASE.2018.2884723
[10] Mubashir, M., Shao, L. and Seed, L., A survey on fall detection: Principles and approaches. Neurocomputing, 100, 2013, pp.144-152 https://doi.org/10.1016/j.neucom.2011.09.037
[11] Sixsmith, A. and Johnson, N., A smart sensor to detect the falls of the elderly. IEEE Pervasive computing, 3(2), 2004, pp.42-47. https://doi.org/10.1109/MPRV.2004.1316817
[12] Jefiza, A., Pramunanto, E., Boedinoegroho, H. and Purnomo, M. H., September. Fall detection based on accelerometer and gyroscope using back propagation. In 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017, pp. 1-6. IEEE. https://doi.org/10.1109/EECSI.2017.8239149
[13] Ren, L. and Peng, Y., Research of fall detection and fall prevention technologies: A systematic review. IEEE Access, 7, 2019, pp. 77702-77722. https://doi.org/10.1109/ACCESS.2019.2922708
[14] Harari, Y., Shawen, N., Mummidisetty, C. K. et al. A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls. J NeuroEngineering Rehabil 18, 124, 2021. https://doi.org/10.1186/s12984-021- 00918-z
[15] Wayan Wiprayoga Wisesa, I., & Mahardika, G., Fall detection algorithm based on accelerometer and gyroscope sensor data using Recurrent Neural Networks. IOP Conference Series: Earth and Environmental Science, 258, 012035, 2019. https://doi.org/10.1088/1755-1315/258/1/012035
[16] Y. Shao, X. Wang, W. Song, S. Ilyas, H. Guo, and W. S. Chang, Feasibility of Using Floor Vibration to Detect Human Falls, International Journal of Environmental Research and Public Health, vol. 18, no. 1, p. 200, Dec. 2020 https://doi.org/10.3390/ijerph18010200
[2] Takashima, K., Wada, K., Tra, T. T. and Smith, D.R., A review of Vietnam’s healthcare reform through the Direction of Healthcare Activities (DOHA). Environmental Health and Preventive Medicine, 2017, 22(1), pp.1-7. https://doi.org/10.1186/s12199-017-0682-z
[3] Hoang, D. K., Le, N. M., Vo‐Thi, U. P., Nguyen, H.G., Ho‐Pham, L. T. and Nguyen, T. V., Mechanography assessment of fall risk in older adults: the Vietnam Osteoporosis Study. Journal of Cachexia, Sarcopenia and Muscle, 2021. https://doi.org/10.1002/jcsm.12751
[4] Mubashir, M., Shao, L. and Seed, L., A survey on fall detection: Principles and approaches, Neurocomputing, 100, 2013, pp.144-152. https://doi.org/10.1016/j.neucom.2011.09.037
[5] Kadhum, A.A., Al-Libawy, H. and Hussein, E.A., May. An accurate fall detection system for the elderly people using smartphone inertial sensors. In Journal of Physics: Conference Series (Vol. 1530, No. 1, p. 012102), 2020. https://doi.org/10.1088/1742-6596/1530/1/012102
[6] Aguiar, B., Rocha, T., Silva, J. and Sousa, I., June. Accelerometer-based fall detection for smartphones. In 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA) 2014, pp. 1-6. IEEE. https://doi.org/10.1109/MeMeA.2014.6860110
[7] Wang, F.T., Chan, H. L., Hsu, M. H., Lin, C. K., Chao, P. K. and Chang, Y. J., Threshold-based fall detection using a hybrid of tri-axial accelerometer and gyroscope. Physiological Measurement, 39 (10), 2018, p.105002. https://doi.org/10.1088/1361-6579/aae0eb
[8] Lin, C. L., Chiu, W. C., Chu, T. C., Ho, Y. H., Chen, F. H., Hsu, C. C., Hsieh, P. H., Chen, C. H., Lin, C. C. K., Sung, P. S. and Chen, P. T., Innovative head-mounted system based on inertial sensors and magnetometer for detecting falling movements. Sensors, 20(20), 2020, p.5774. https://doi.org/10.3390/s20205774
[9] Trkov, M., Chen, K., Yi, J. and Liu, T., Inertial sensorbased slip detection in human walking. IEEE Transactions on Automation Science and Engineering, 16(3), 2019, pp.1399-1411. https://doi.org/10.1109/TASE.2018.2884723
[10] Mubashir, M., Shao, L. and Seed, L., A survey on fall detection: Principles and approaches. Neurocomputing, 100, 2013, pp.144-152 https://doi.org/10.1016/j.neucom.2011.09.037
[11] Sixsmith, A. and Johnson, N., A smart sensor to detect the falls of the elderly. IEEE Pervasive computing, 3(2), 2004, pp.42-47. https://doi.org/10.1109/MPRV.2004.1316817
[12] Jefiza, A., Pramunanto, E., Boedinoegroho, H. and Purnomo, M. H., September. Fall detection based on accelerometer and gyroscope using back propagation. In 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017, pp. 1-6. IEEE. https://doi.org/10.1109/EECSI.2017.8239149
[13] Ren, L. and Peng, Y., Research of fall detection and fall prevention technologies: A systematic review. IEEE Access, 7, 2019, pp. 77702-77722. https://doi.org/10.1109/ACCESS.2019.2922708
[14] Harari, Y., Shawen, N., Mummidisetty, C. K. et al. A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls. J NeuroEngineering Rehabil 18, 124, 2021. https://doi.org/10.1186/s12984-021- 00918-z
[15] Wayan Wiprayoga Wisesa, I., & Mahardika, G., Fall detection algorithm based on accelerometer and gyroscope sensor data using Recurrent Neural Networks. IOP Conference Series: Earth and Environmental Science, 258, 012035, 2019. https://doi.org/10.1088/1755-1315/258/1/012035
[16] Y. Shao, X. Wang, W. Song, S. Ilyas, H. Guo, and W. S. Chang, Feasibility of Using Floor Vibration to Detect Human Falls, International Journal of Environmental Research and Public Health, vol. 18, no. 1, p. 200, Dec. 2020 https://doi.org/10.3390/ijerph18010200