On-line fall detection using wrist devices

November 18, 2022

More than 37 million falls that require medical attention occur every year, mainly affecting the elderly. In addition to the natural consequences of a fall, most aged adults with a history of falling are likely to develop a fear of falling, leading to a decrease in their mobility level and impacting their overall quality of life. Previous wrist-based datasets revealed limitations such as unrealistic recording set-ups, lack of proper documentation and, most importantly, the absence of Elderly People's movements. Therefore, this work proposes a new wrist-based dataset to tackle this problem. With this dataset, an exhaustive research is carried out with the low computational FS-1 feature set (Maximum, Minimum, Mean and Variance) with various Machine Learning Methods. This work presents an accelerometer-only Fall Detector streaming data at 50Hz, using the low computational FS-1 feature set to train a 3NN algorithm with Euclidean Distance, having a window size of 9 seconds. This work had battery and memory limitations in mind. It also developed a Learning Version that boosts the Fall Detector's performance over time, achieving over four days without a single False Positive and False Negative.

Authors in the community:

• 

  J

  João José Tacanho Marques

  ist189473

Supervisors of this institution:

• 

  M

  Manuel Fernando Cabido Peres Lopes

  ist40208

electrical-engineering-electronic-engineering-information-engineering - Electrical engineering, electronic engineering, information engineering

eng - English

Instituto Superior Técnico