With the improvement of people’s living standards, countries around the world are facing the serious problem of aging in their own countries. Unexpected events such as falls in the elderly have a serious and inestimable impact on the health of the elderly. The purpose of this paper is to study the elderly fall detection system based on multi-feature fusion robot. This article first introduces a fall detection algorithm. Second, it improves the way of feature fusion. It uses feature weighted kernel functions to achieve the weighted fusion of acceleration and angular velocity features to build a fall recognition model based on support vector machines. Then, it analyzes the key parts of the fall recognition model. To verify the generalization performance of the algorithm. Finally, a fall detection system based on the recognition model constructed in this paper is implemented on the Android phone platform, and the practical application effect of the fall detection algorithm is verified. The experiments show that the feature fusion fall monitoring algorithm constructed in this paper has a sensitivity of 90.67%, a specificity of 92.96%, and an accuracy of 92.14%, which verifies the detection performance of the detection algorithm constructed in this paper. The robustness and stability of the drop detection software based on the Android platform.

Yuan Guo. Motion Fall Detection System for the Elderly Based on Multi-feature Fusion Robot. International Journal of Multimedia Computing (2020), Vol. 1, Issue 2: 62-78. https://doi.org/10.38007/IJMC.2020.010206.

[1] Li Y, Li G, Wang Z, et al. A Multifeature Fusion Approach for Power System Transient Stability Assessment Using PMU Data. Mathematical Problems in Engineering, 2015, 2015(6):263-267. https://doi.org/10.1155/2015/786396

[2] Kianoush S, Savazzi S, Vicentini F, et al. Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces. IEEE Internet of Things Journal, 2016, 4(2):351-362. https://doi.org/10.1109/JIOT.2016.2624800

[3] Abdiansah A, Wardoyo R. Time complexity analysis of support vector machines (SVM) in LibSVM. International Journal of Computer Applications, 2015, 128(3):975-8887. https://doi.org/10.5120/ijca2015906480

[4] Zhan T, Feng P, Hong X, et al. An automatic glioma grading method based on multi-feature extraction and fusion. Technology and health care: official journal of the European Society for Engineering and Medicine, 2017, 25(12):1-9. https://doi.org/10.3233/THC-171341

[5] Wei T, Xiao H E, Ying L U. Visual Saliency and Multi-Feature Fusion for Object Tracking. Journal of Frontiers of Computer Science and Technology, 2017, 11(3):438-449.

[6] Song W, Wang Y, Shi L, et al. SAR Target Discrimination Algorithm Based on Bag-of-words Model with Multi-feature Fusion. Journal of Electronics & Information Technology, 2017, 39(11):2705-2715.

[7] Shi L K, Zhou H, Liu W H. Multi-Feature Fusion and Visualization of Pavement Distress Images Based on Manifold Learning. Journal of Highway & Transportation Research & Development, 2017, 11(1):14-22. https://doi.org/10.1061/JHTRCQ.0000545

[8] Song C, Li Y, Ning J, et al. Study of multi-feature fusion methods for distribution fields in object tracking. Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, 2015, 42(4):1-7.

[9] L. Huang, J. Tang, C. Ling. Pattern recognition for partial discharge based on multi-feature fusion technology. Gaodianya Jishu/high Voltage Engineering, 2015, 41(3):947-955.

[10] F. Wang, Y. Cheng, S. Li. Remote sensing image fusion algorithm based on multi-feature. Journal of Northwestern Polytechnical University, 2015, 33(3):489-494.

[11] Zhong Y, Zhu Q, Zhang L. Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(11):1-16. https://doi.org/10.1109/TGRS.2015.2435801

[12] Qikai Lu, Xin Huang, Jun Li. A Novel MRF-Based Multifeature Fusion for Classification of Remote Sensing Images. IEEE Geoscience & Remote Sensing Letters, 2016, 13(4):1-5. https://doi.org/10.1109/LGRS.2016.2521418

[13] Askari M, Eslami S, Rijn M V, et al. Assessment of the quality of fall detection and management in primary care in the Netherlands based on the ACOVE quality indicators. Osteoporosis International, 2015, 27(2):569-576. https://doi.org/10.1007/s00198-015-3235-6

[14] Sabatini A M, Ligorio G, Mannini A, et al. Prior-to- and Post-Impact Fall Detection Using Inertial and Barometric Altimeter Measurements. IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society, 2015, 24(7):774-783. https://doi.org/10.1109/TNSRE.2015.2460373

[15] Shotaro, Kosuke, Jian, et al. Fall Detection and Prevention Control Using Walking-Aid Cane Robot. IEEE/ASME Transactions on Mechatronics, 2016, 21(2):625-637. https://doi.org/10.1109/TMECH.2015.2477996

[16] Chaccour K, Darazi R, Hassani A H E, et al. From Fall Detection to Fall Prevention: A Generic Classification of Fall-Related Systems. Sensors Journal, IEEE, 2017, 17(3):812-822. https://doi.org/10.1109/JSEN.2016.2628099

[17] Mitja Luštrek, Hristijan Gjoreski, Narciso González Vega. Fall Detection Using Location Sensors and Accelerometers. IEEE Pervasive Computing, 2015, 14(4):72-79. https://doi.org/10.1109/MPRV.2015.84

[18] Dieu Tien Bui, Tran Anh Tuan, Harald Klempe. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 2016, 13(2):361-378. https://doi.org/10.1007/s10346-015-0557-6

[19] Nitin Anand Shrivastava, Abbas Khosravi, Bijaya Ketan Panigrahi. Prediction Interval Estimation of Electricity Prices Using PSO-Tuned Support Vector Machines. IEEE Transactions on Industrial Informatics, 2015, 11(2):1-1. https://doi.org/10.1109/TII.2015.2389625

[20] Hu W, Zhang W, Min Y, et al. Real-time Emergency Control Decision in Power System Based on Support Vector Machines. Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2017, 37(16):4567-4576.

[21] Tetsuzo Tanino, Ryo Kawachi, Masashi Akao. Performance evaluation of multiobjective multiclass support vector machines maximizing geometric margins. Numerical Algebra Control & Optimization, 2017, 1(1):151-169. https://doi.org/10.3934/naco.2011.1.151

[22] Tan K, Zhang J, Du Q, et al. GPU Parallel Implementation of Support Vector Machines for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(10):1-10. https://doi.org/10.1109/JSTARS.2015.2453411

[23] Guo S, Deng F, Jie C, et al. Sensor Multi-fault Diagnosis With Improved Support Vector Machines. IEEE Transactions on Automation Science & Engineering, 2017, 14(2):1053-1063. https://doi.org/10.1109/TASE.2015.2487523

[24] Xiang Zhang, Yichao Wu, Lan Wang. Variable selection for support vector machines in moderately high dimensions. J R Stat Soc, 2016, 78(1):53-76. https://doi.org/10.1111/rssb.12100

[25] Singh T, Troia F D, Corrado V A, et al. Support vector machines and malware detection. Journal of Computer Virology & Hacking Techniques, 2016, 41(10):1-10. https://doi.org/10.1007/s11416-015-0252-0

[26] Juntao Li, Yimin Cao, Yadi Wang. Online Learning Algorithms for Double-Weighted Least Squares Twin Bounded Support Vector Machines. Neural Processing Letters, 2016, 45(1):1-21. https://doi.org/10.1007/s11063-017-9609-3

[27] Yang You, Jim Demmel, Rich Vuduc. Design and Implementation of a Communication-Optimal Classifier for Distributed Kernel Support Vector Machines. IEEE Transactions on Parallel & Distributed Systems, 2016, 28(4):1-1. https://doi.org/10.1109/TPDS.2016.2608823

[28] Alejandro Rosales-Perez, Salvador Garcia, Jesus A. Gonzalez. An Evolutionary Multiobjective Model and Instance Selection for Support Vector Machines with Pareto-Based Ensembles. IEEE Transactions on Evolutionary Computation, 2017, 21(6):863-877. https://doi.org/10.1109/TEVC.2017.2688863 

[29] Xiaochun Lu, Juntao Fei. Velocity Tracking Control of Wheeled Mobile Robots by Iterative Learning Control. International Journal of Advanced Robotic Systems, 2016, 13(3):1. https://doi.org/10.5772/63813

[30] Seokwon Yeom, Yong-Hyun Woo. Person-Specific Face Detection in a Scene with Optimum Composite Filtering and Colour-Shape Information. International Journal of Advanced Robotic Systems, 2013, 10(1):1. https://doi.org/10.5772/54239