Assistive Technology for Fall Detection Development of Integrated Wearable Sensor to Smart Home System
Fall detection is an assistive technology for elderly people that helps in emergency situations. This work presents the development of a wearable device to detect falls connected to a ultra low power wireless network. The device is connected to a smart home system to trigger alarms when events are detected. The fall detection is done by a threshold algorithm based on data fusion from inertial sensors. The wearable sensor is based on EnOcean protocol, which includes a wireless connection with a smart home system, according to the KNX standard, through the Home Assistant platform. The tests were performed in a prototype and the results include the evaluation of fall and nonfall movements in two different body characteristics. The results revealed sensitivity and specificity of up to 96% and 100%, respectively.
F. Baig, S. Beg, M. F. Khan, and S. J. Nawaz, “A Method to Control Home Appliances Based on Writing Commands Over the Air,” Journal of Control, Automation and Electrical Systems, vol. 26, no. 4, pp. 421–429, 2015. [Online]. Available: http://dx.doi.org/10.1007/s40313-015-0184-4
H. Wang, D. Zhang, Y. Wang, J. Ma, Y. Wang, and S. Li, “Rt-fall: A real-time and contactless fall detection system with commodity wifi devices,” IEEE Transactions on Mobile Computing, vol. 16, no. 2, pp. 511–526, 2016.
P. Rashidi and A. Mihailidis, “A survey on ambient-assisted living tools for older adults,” IEEE journal of biomedical and health informatics, vol. 17, no. 3, pp. 579–590, 2012.
S. Blackman, C. Matlo, C. Bobrovitskiy, A. Waldoch, M. L. Fang, P. Jackson, A. Mihailidis, L. Nygård, A. Astell, and A. Sixsmith, “Ambient assisted living technologies for aging well: a scoping review,” Journal of Intelligent Systems, vol. 25, no. 1, pp. 55–69, 2016.
A. Ramachandran and A. Karuppiah, “A survey on recent advances in wearable fall detection systems,” BioMed Research International, vol. 2020, 2020.
F. Darko, S. Denis, and Ž. Mario, “Human movement detection based on acceleration measurements and k-NN classification,” EUROCON 2007 - The International Conference on Computer as a Tool, pp. 589–594, 2007.
J. He, M. Zhou, X. Wang, and Y. Han, “A wearable method for autonomous fall detection based on Kalman filter and k-NN algorithm,” Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016, pp. 420–423, 2016.
Q. Li, J. A. Stankovic, M. A. Hanson, A. T. Barth, J. Lach, G. Zhou et al., “Accurate, fast fall detection using gyroscopes and accelerometerderived posture information.” in BSN, vol. 9, 2009, pp. 138–143.
F. Wu, H. Zhao, Y. Zhao, and H. Zhong, “Development of a wearablesensor- based fall detection system,” International journal of telemedicine and applications, vol. 2015, p. 2, 2015.
S. Abdelhedi, R. Bourguiba, J. Mouine, and M. Baklouti, “Development of a two-threshold-based fall detection algorithm for elderly health monitoring,” in 2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS). IEEE, 2016, pp. 1–5.
H. W. Guo, Y. T. Hsieh, Y. S. Huang, J. C. Chien, K. Haraikawa, and J. S. Shieh, “A threshold-based algorithm of fall detection using a wearable device with tri-axial accelerometer and gyroscope,” in 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2015, pp. 54–57.
G. N. Swathi and M. Amarnadh, “Threshold Based Fall Detection and PredictionMethod Using Tri-Axial Accelerometer,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 4, pp. 29–33, 2014.
L. Tong, Q. Song, Y. Ge, and M. Liu, “Hmm-based human fall detection and prediction method using tri-axial accelerometer,” IEEE Sensors Journal, vol. 13, no. 5, pp. 1849–1856, 2013.
D. Lim, C. Park, N. H. Kim, S.-H. Kim, and Y. S. Yu, “Fall-detection algorithm using 3-axis acceleration: combination with simple threshold and hidden markov model,” Journal of Applied Mathematics, vol. 2014, 2014.
N. Noury, A. Fleury, P. Rumeau, A. Bourke, G. Ó. Laighin, V. Rialli, and J. Lundy, “Fall detection – Principles and Methods,” International Conference of the Engineering in Medicine and Biology Society (EMBS), vol. 29, 2007.
EnOcean. (2020) Enocean: Ultra-low power management. [Online]. Available: https://www.enocean.com/en/technology/energy-harvesting-wireless/
P. Rawat, K. D. Singh, H. Chaouchi, and J. M. Bonnin, “Wireless sensor networks: a survey on recent developments and potential synergies,” The Journal of supercomputing, vol. 68, no. 1, pp. 1–48, 2014.
L. Parsons, R. Ross, and K. Robert, “A survey on wireless sensor network technologies in pest management applications,” SN Applied Sciences, vol. 2, no. 1, p. 28, 2020.
KNX-Association. (2020) Serial data transmission and knx protocol documentation. [Online]. Available: [link]
S. A. Navarro-Tuch, M. R. Bustamante-Bello, A. Molina, J. Izquierdo-Reyes, R. Avila-Vazquez, J. L. Pablos-Hach, and Y. Gutiérrez-Martínez, “Inhabitable space control for the creation of healthy interactive spaces through emotional domotics,” International Journal on Interactive Design and Manufacturing (IJIDeM), vol. 12, no. 4, pp. 1337–1347, 2018.
Raspberry Pi Foundation, “Raspberry Pi 3 Model B+ 1Gb,” 2015, Accessed Oct 10 2019. [Online]. Available: [link]
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.