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Martinez-Villaseñor, Lourdes
Approaching Fall Classification Using the UP-Fall Detection Dataset: Analysis and Results from an International Competition
2020
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Ponce, Hiram
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Martinez-Villaseñor, Lourdes
This chapter presents the results of the Challenge UP – Multimodal Fall Detection competition that was held during the 2019 International Joint Conference on Neural Networks (IJCNN 2019). This competition lies on the fall classification problem, and it aims to classify eleven human activities (i.e. five types of falls and six simple daily activities) using the joint information from different wearables, ambient sensors and video recordings, stored in a given dataset. After five months of competition, three winners and one honorific mention were awarded during the conference event. The machine learning model from the first place scored$$82.47\%$$ in$$F:1$$-score, outperforming the baseline of$$70.44\%$$. After analyzing the implementations from the participants, we summarized the insights and trends of fall classification. © 2020, Springer Nature Switzerland AG.