Challenge UP 2019: Multimodal Fall Detection

Challenge UP 2019: Multimodal Fall Detection

Update:  The IIDB team, led by Patricia Endo and represented by Pierangelo Rosati, were awarded the 3rd place prize in the final in July 2019.  Congratulations to the team on a fantastic achievement!

Congratulations to the team from IIDB, led by Patricia Endo, who have made it to the final of ‘Challenge UP 2019: Multimodal Fall Detection’.  This deep learning competition focuses on the recognition of a range of five falls and six daily activities, performed by 12 subjects, summarising information from wearable sensors, ambient sensors and vision devices.

The goal of this competition is to detect/classify the activities and types of falls performed in a set of routines of human daily living activities.

Falls are frequent, especially among old people, and it is a major health problem according to World Health Organisation. Fall detectors can alleviate this problem and can reduce the time in which a person who suffered a fall receives assistance.  Recently, there has been an increase in fall detection system development based mainly in sensor and/or context approaches; however, public datasets are scarce.

For this reason a public multimodal dataset for fall detection has been built to benefit researchers in the fields of wearable computing, ambient intelligence, and vision.

Pierangelo Rosati will represent the IIDB team at this final stage, in Budapest from July 14^th – 19^th and we wish them every success with the challenge.