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Automated Development of Custom Fall Detectors: Position, Model and Rate Impact in Performance

: Silva, J.; Gomes, D.; Sousa, I.; Cardoso, J.S.


IEEE Sensors Journal 20 (2020), Nr.10, S.5465-5472
ISSN: 1530-437X
ISSN: 1558-1748
Fraunhofer AICOS ()

The past years have witnessed a boost in fall detection-related research works, disclosing an extensive number of methodologies built upon similar principles but addressing particular use-cases. These use-cases frequently motivate algorithm fine-tuning, making the modelling stage a time and effort consuming process. This work contributes towards understanding the impact of several of the most frequent requirements for wearable-based fall detection solutions in their performance (usage positions, learning model, rate). We introduce a new machine learning pipeline, trained with a proprietary dataset, with a customisable modelling stage which enabled the assessment of performance over each combination of custom parameters. Finally, we benchmark a model deployed by our framework using the UMAFall dataset, achieving state-of-the-art results with an F1-score of 84.6% for the classification of the entire dataset, which included an unseen usage position (ankle), considering a sampling rate of 10 Hz and a Random Forest classifier.