Online Fall Detection Using Wrist Devices
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 o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/3/1146 |
| _version_ | 1827759504255614976 |
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| author | João Marques Plinio Moreno |
| author_facet | João Marques Plinio Moreno |
| author_sort | João Marques |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T09:26:22Z |
| format | Article |
| id | doaj.art-ad7a24871f964c79842f66e8c1c7febc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:26:22Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ad7a24871f964c79842f66e8c1c7febc2023-11-16T17:56:50ZengMDPI AGSensors1424-82202023-01-01233114610.3390/s23031146Online Fall Detection Using Wrist DevicesJoão Marques0Plinio Moreno1Instituto Superior Técnico, Unviersidade de Lisboa, 1049-001 Lisboa, PortugalInstituto Superior Técnico, Unviersidade de Lisboa, 1049-001 Lisboa, PortugalMore 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.https://www.mdpi.com/1424-8220/23/3/1146fall detectionwrist-based datasetwrist-based solutionmachine learning methodsbattery/memory limitationslearning version |
| spellingShingle | João Marques Plinio Moreno Online Fall Detection Using Wrist Devices Sensors fall detection wrist-based dataset wrist-based solution machine learning methods battery/memory limitations learning version |
| title | Online Fall Detection Using Wrist Devices |
| title_full | Online Fall Detection Using Wrist Devices |
| title_fullStr | Online Fall Detection Using Wrist Devices |
| title_full_unstemmed | Online Fall Detection Using Wrist Devices |
| title_short | Online Fall Detection Using Wrist Devices |
| title_sort | online fall detection using wrist devices |
| topic | fall detection wrist-based dataset wrist-based solution machine learning methods battery/memory limitations learning version |
| url | https://www.mdpi.com/1424-8220/23/3/1146 |
| work_keys_str_mv | AT joaomarques onlinefalldetectionusingwristdevices AT pliniomoreno onlinefalldetectionusingwristdevices |