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We need help in care because the percentage of the global population over 60 is projected to double in 2050. Several specific problems related to aging will need specific attention, falls are one of those. Sensing in devices for fall recognition is vulnerable to external conditions. An extended dataset is key to the development of fall detection systems to run robustness and non-regression tests in real life conditions. 18 “time of flight” “sensors were used to collect a dataset of depth map images at the bedside in two different geriatric hospital wards of the Brussels region. From this large amount of data, we have analysed and labelled manually 15% of the content and extracted most relevant parts for evaluation. Patients are inactive most part of the day (61% laid and 15% sit). The analysed dataset contains 9 falls, 6 of them unknown by the nurse. The movement-tracking algorithm shows a sensitivity of 72% and specificity of 97%. Not to be aware of each fall is an important loss of clinical information. There is a gap in fall prevention. Building a comprehensive dataset for further machine learning development and using an automated fall detection system may allow a better quality of care.

At the bedside, what you know and what you don’t know: A 3D dataset for ageing studies