A comparison between skeleton and bounding box models for falling direction recognition

Lalita Narupiyakul; Nitikorn Srisrisawang

doi:10.1117/12.2300760

19 December 2017 A comparison between skeleton and bounding box models for falling direction recognition

Lalita Narupiyakul, Nitikorn Srisrisawang

Proceedings Volume 10613, 2017 International Conference on Robotics and Machine Vision; 1061305 (2017) https://doi.org/10.1117/12.2300760
Event: Second International Conference on Robotics and Machine Vision, 2017, Kitakyushu, Japan

Abstract

Falling is an injury that can lead to a serious medical condition in every range of the age of people. However, in the case of elderly, the risk of serious injury is much higher. Due to the fact that one way of preventing serious injury is to treat the fallen person as soon as possible, several works attempted to implement different algorithms to recognize the fall. Our work compares the performance of two models based on features extraction: (i) Body joint data (Skeleton Data) which are the joint’s positions in 3 axes and (ii) Bounding box (Box-size Data) covering all body joints. Machine learning algorithms that were chosen are Decision Tree (DT), Naïve Bayes (NB), K-nearest neighbors (KNN), Linear discriminant analysis (LDA), Voting Classification (VC), and Gradient boosting (GB). The results illustrate that the models trained with Skeleton data are performed far better than those trained with Box-size data (with an average accuracy of 94-81% and 80-75%, respectively). KNN shows the best performance in both Body joint model and Bounding box model. In conclusion, KNN with Body joint model performs the best among the others.

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Lalita Narupiyakul and Nitikorn Srisrisawang "A comparison between skeleton and bounding box models for falling direction recognition", Proc. SPIE 10613, 2017 International Conference on Robotics and Machine Vision, 1061305 (19 December 2017); https://doi.org/10.1117/12.2300760

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KEYWORDS

Data modeling

Performance modeling

Detection and tracking algorithms

Feature extraction

Data acquisition

Injuries

Machine learning

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