Total views : 182

Fall Detection Algorithm based on Peaks of Voltage Measurements from the Accelerometer


  • Department of Electronic Engineering, Daegu University, Gyeongsan, Korea
  • Department of Communication Engineering, Daegu University, Gyeongsan, Korea


According to the Korea Census Bureau, the number of elderly people above the age 65 will reach more than 10 million in 2026. It means that more than 21% of the population in KOREA will reach over 65 years old. It becomes extremely important to take care of the elderly people for the case of health emergency; such as fall or heart attack. In this paper, a simple fall detection system that detects an actual fall direction is implemented. In order to detect the fall situation, 3-axis acceleration sensor (MMA7331) is used in the system. The fall detection algorithm that can classify fall directions such as front, back, left and right fall is proposed. The direction of the fall is decided by examining the acceleration peaks of X and Y directions of the sensor. It is shown that the proposed algorithm successfully detects the front and back fall direction with probability of 96% and 98%, respectively.


Accelerometer, Acceleration Peak, Fall Detection.

Full Text:

 |  (PDF views: 146)


  • Zeng, Y. Environment-Based Design (EBD). ASME Conference Proceedings; 2011. p. 237–50. DOI: 10.1115/ DETC2011-48263.
  • Joint communication on communication on accreditation of healthcare organization (US): Joint commission international accreditation standards for hospitals. Third Edition. Illinois: The Institute; 2007
  • Baker S, Harvey A. Fall injuries in the elderly. Clinics in Geriatric Medicine. 2011; 1:501–12.
  • Statistics Korea; 2007.
  • Karantonis DM, Narayanan MR, Mathie M, Lovell NH, Celler BG. Implementation of a real-time human movement classifer using triaxial accelerometer for ambulatory monitoring. IEEE Transaction on Information Technology in Biomedicine. 2006; 10(1):156–67.
  • Chung W, Purwar A, Sharma A. Frequency domain approach for activity classification using accelerometer.IEEE EMBS Conference; 2008. p. 1120–3.
  • Gupta P, Dallas T. Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Transaction on Biomedical Engineering. 2014; 61(6):1780– 6.
  • Dumitrache M, Pasca S. Fall detection algorithm based on triaxial accelerometer data. E-Health and Bioengineering Conference(EHB); 2013. p. 1–4.
  • Kau L, Chen C. A smart phone-based pocket fall accident detection, positioning, and rescue system. IEEE Journal of Biomedical and Health Information. 2015; 19(1):44–56.
  • Hou Y, et al. Triaxial accelerometer-based real time fall event detection. International Conference on Information Society (i-Society); 2012. p. 386–90.
  • Bianchi F, Redmond SJ, Narayanan MR, Cerutti S, Lovell NH. Barometric pressure and triaxial accelerometry-based falls event detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2010; 18(6):619–27.
  • Hsu-Tang Kung, Ou C-Y, Li S-D, Lin C-H, Chen H-J, Hsu Y-L, Chang M-H. Efficient movement detection for human actions using triaxial accelerometer. Digest of Technical Papers International Conference on Consumer Electronics; 2010. p. 113–14
  • Cheng S. An intelligent detection system using triaxial accelerometer integrated by active RFID. International Conference on Machine Learning and Cybernetics (ICMLC). 2014; 2:517–22.
  • Pierleoni P, Belli A, Palma L, Pellegrini M, Pernini L, Valenti S. A high reliability wearable device for elderly fall detection. IEEE Sensors Journal. 2015; 15(8):4544–53.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.