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

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Silva, J (Author) Other; Gomes, D (Author) Other; Sousa, I (Author) Other; Jaime S Cardoso (Author) FEUP

Journal

Title: IEEE Sensors Journal

Vol. 20

Pages: 5465-5472

ISSN: 1530-437X

Publisher: IEEE

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-00S-10Y

DOI: 10.1109/jsen.2020.2970994

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8