Online Fall Detection Using Wrist Devices.
Sensors (Basel)
; 23(3)2023 Jan 19.
Article
en En
| MEDLINE
| ID: mdl-36772187
ABSTRACT
More than 37 million falls that require medical attention occur every year, mainly affecting the elderly. Besides the natural consequences of falls, 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, 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 50 Hz, using the low computational FS-1 feature set to train a 3NN algorithm with Euclidean distance, with a window size of 9 s. 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 no single false positives or false negatives over four days.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Calidad de Vida
/
Muñeca
Tipo de estudio:
Diagnostic_studies
Límite:
Adult
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Aged
/
Humans
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Middle aged
Idioma:
En
Año:
2023
Tipo del documento:
Article