Sistem Deteksi Orang Jatuh Dengan Menggunakan Sensor Kamera Kinect Dengan Metode AdaBoost

Satria Perwira(1*), Muhammad Idham Ananta Timur(2), Agus Harjoko(3)

(1) Prodi Elektronika dan Instrumentasi, DIKE, FMIPA, UGM, Yogyakarta
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA, UGM, Yogyakarta
(3) Departemen Ilmu Komputer dan Elektronika, FMIPA, UGM, Yogyakarta
(*) Corresponding Author


Fall cases of elderly people aged 65 or above put their health at risk because it could lead to hip bone fracture, concussion, even death. Immediate help is needed if fall happened which is why an automatic and unobtrusive fall detection system is needed. There are three approaches in fall detection system; wearable, ambience, and vision-based. Wearable approach has the drawback of its obtrusive nature while ambience approach is prone to high false positive value. Vision-based approach is chosen because its unobtrusive nature and low false positive value. This study uses Kinect camera because of its ability on extracting skeletal data.
The methods that are used in the fall detection system are AdaBoost method and joint velocity thresholding method. Thresholding method is used as a comparison to AdaBoost method. Both methods use skeletal data from the subject recorded by the Kinect camera. AdaBoost method compares the skeletal data with model that was made before while thresholding method compares the joint velocity value with the threshold value. System test is done using training data, test data, and real-time data. The average accuracy obtained from the system test with AdaBoost method is 91.75% and with thresholding method is 68.22%.


Fall detection; Kinect; AdaBoost

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G. Bergen and M. R. Stevens, “Falls Prevention Awareness Day — Falls and Fall Injuries Among Adults Aged ≥ 65 Years — United States , 2014,” vol. 65, no. 37, 2016.

J. Chen, K. Kwong, D. Chang, J. Luk, and R. Bajcsy, “Wearable Sensors for Reliable Fall Detection,” Clin. Ter., vol. 85, no. 2, pp. 179–204, 2005.

M. Alwan, P. J. Rajendran, S. Kelli, D. Mack, S. Dalali, and M. W. I, “A Smart and Passive Floor-Vibration Based Fall Detector for Elderly,” pp. 3–7, 2006.

L. Jamhoury, “Understanding Kinect V2 Joints and Coordinate System,” 2018. [Online]. Available: [Accessed: 21-Nov-2018].

K. Gunadi, Liliana, and J. Tjitrokusmo, “Fall detection application using kinect,” Proc. - 2017 Int. Conf. Soft Comput. Intell. Syst. Inf. Technol. Build. Intell. Through IOT Big Data, ICSIIT 2017, vol. 2018–Janua, pp. 279–282, 2018.

Y. Nizam, M. N. H. Mohd, and M. M. A. Jamil, “Human Fall Detection from Depth Images using Position and Velocity of Subject,” Procedia Comput. Sci., vol. 105, no. Iris 2016, pp. 131–137, 2017.

Y. Angal and A. Jagtap, “Fall Detection System for Older People,” 2016 IEEE Int. Conf. Adv. Electron. Commun. Comput. Technol., pp. 262–266, 2016.

G. Sriram, M. Vivek, S. K. Roy, and P. Sharan, “Spectral Analysis of Photonic crystal based Bio-Sensor using AdaBoost Algorithm,” 2015 Int. Conf. Commun. Signal Process., pp. 1806–1810, 2015.

M. Rahman, Beginning Microsoft Kinect for Windows SDK 2.0: Motion and Depth Sensing for Natural User Interface. Montreal: Apress, 2017.

A. B. Rathod, “A Comparative Study on Distance Measuring Approches for Permutation Representations,” 2016 IEEE Int. Conf. Adv. Electron. Commun. Comput. Technol., pp. 251–255, 2016.


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