References
[1]. Alvarez, F. J., Urena, J., Mazo, M., Hernández, A.,
García, J. J., & Donato, P. (2004, June). Ultrasonic sensor
system for detecting falling objects on railways. In
Intelligent Vehicles Symposium, 2004 IEEE (pp. 866-871).
IEEE.
[2]. Anderson, D., Keller, J. M., Skubic, M., Chen, X., & He,
Z. (2006, August). Recognizing falls from silhouettes. In
Engineering in Medicine and Biology Society, 2006.
th EMBS'06. 28 Annual International Conference of the IEEE
(pp. 6388-6391). IEEE.
[3]. Dai, J., Bai, X., Yang, Z., Shen, Z., & Xuan, D. (2010,
March). PerFallD: A pervasive fall detection system using
mobile phones. In Per vasive Computing and
Communications Workshops (PERCOM Workshops), 2010
8th IEEE International Conference on (pp. 292-297). IEEE.
[4]. Dinh, A., Teng, D., Chen, L., Ko, S. B., Shi, Y., Basran, J.,
& Del Bello-Hass, V. (2008, August). Data acquisition
system using six degree-of-freedom inertia sensor and
Zigbee wireless link for fall detection and prevention. In
Engineering in Medicine and Biology Society, 2008. EMBS
th 2008. 30 Annual International Conference of the IEEE
(pp. 2353-2356). IEEE.
[5]. Hsu, Y. L., Wang, J. S., Lin, Y. C., Chen, S. M., Tsai, Y. J., Chu, C. L., & Chang, C. W. (2013, March). A wearable
inertial-sensing-based body sensor network for shoulder
range of motion assessment. In Orange Technologies
(ICOT), 2013 International Conference On (pp. 328-331).
IEEE.
[6]. Jay, C., Karric, K., Dennis, C., Jerry, L., & Ruzena, B.
(2005). Wearable sensors for reliable fall detection. In
Engineering in Medicine and Biology Society, 2005. IEEEth
EMBS 2005. 27 Annual International Conference of the
(pp. 3551-3554).
[7]. Kepski, M., & Kwolek, B. (2015, September).
Embedded system for fall detection using body-worn
accelerometer and depth sensor. In Intelligent Data
Acquisition and Advanced Computing Systems:
th Technology and Applications (IDAACS), 2015 IEEE 8
International Conference on (Vol. 2, pp. 755-759). IEEE.
[8]. Kianoush, S., Savazzi, S., Vicentini, F., Rampa, V., &
Giussani, M. (2017). Device-free RF human body fall
detection and localization in industrial workplaces. IEEE
Internet of Things Journal, 4(2), 351-362.
[9]. Li, Q., Stankovic, J. A., Hanson, M. A., Barth, A. T., Lach,
J., & Zhou, G. (2009, June). Accurate, fast fall detection
using gyroscopes and accelerometer-derived posture
information. In Wearable and Implantable Body Sensor
Networks, 2009. BSN 2009. Sixth International Workshop
on (pp. 138-143). IEEE.
[10]. Mahabalagiri, A., Ozcan, K., & Velipasalar, S. (2013,
October). A robust edge-based optical flow method for
elderly activity classification with wearable smart cameras. In Distributed Smart Cameras (ICDSC), 2013
Seventh International Conference on (pp. 1-6). IEEE.
[11]. Pattamaset, S., Charoenpong, T., Charoenpong, P.,
& Chianrabutra, C. (2017, February). Human fall
detection by using the body vector. In Knowledge and
t h Smart Technology (KST), 2017 9 International
Conference on (pp. 162-165). IEEE.
[12]. Pierleoni, P., Belli, A., Palma, L., Pernini, L., & Valenti,
S. (2014, October). A versatile ankle-mounted fall
detection device based on attitude heading systems. In
Biomedical Circuits and Systems Conference (BioCAS),
2014 IEEE (pp. 153-156). IEEE.
[13]. Purwar, A., Jeong, D. U., & Chung, W. Y. (2007,
October). Activity monitoring from real-time triaxial
accelerometer data using sensor network. In Control,
Automation and Systems, 2007. ICCAS'07. International
Conference on (pp. 2402-2406). IEEE.
[14]. Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau,
J. (2011). Robust video surveillance for fall detection
based on human shape deformation. IEEE Transactions
on Circuits and Systems for Video Technology, 21(5), 611-
622.
[15]. Vo, Q. V., Lee, G., & Choi, D. (2012, February). Fall
detection based on movement and smart phone
technology. In Computing and Communication
Technologies, Research, Innovation, and Vision for the
Future (RIVF), 2012 IEEE RIVF International Conference on
(pp. 1-4). IEEE.
