AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks
Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in excepti...
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| Materiálatiipa: | Artihkal |
| Giella: | English |
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MDPI AG
2021-03-01
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| Ráidu: | Sensors |
| Fáttát: | |
| Liŋkkat: | https://www.mdpi.com/1424-8220/21/5/1889 |
| _version_ | 1827601532278800384 |
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| author | Francisco Luna-Perejón Luis Muñoz-Saavedra Javier Civit-Masot Anton Civit Manuel Domínguez-Morales |
| author_facet | Francisco Luna-Perejón Luis Muñoz-Saavedra Javier Civit-Masot Anton Civit Manuel Domínguez-Morales |
| author_sort | Francisco Luna-Perejón |
| collection | DOAJ |
| description | Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system’s feasibility. |
| first_indexed | 2024-03-09T04:59:24Z |
| format | Article |
| id | doaj.art-ba3cb78fd30f4c9b9f04960960cbd7c6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:59:24Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ba3cb78fd30f4c9b9f04960960cbd7c62023-12-03T13:02:18ZengMDPI AGSensors1424-82202021-03-01215188910.3390/s21051889AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural NetworksFrancisco Luna-Perejón0Luis Muñoz-Saavedra1Javier Civit-Masot2Anton Civit3Manuel Domínguez-Morales4Architecture and Computer Technology Department, ETSII-EPS, University of Seville, 41004 Sevilla, SpainArchitecture and Computer Technology Department, ETSII-EPS, University of Seville, 41004 Sevilla, SpainArchitecture and Computer Technology Department, ETSII-EPS, University of Seville, 41004 Sevilla, SpainArchitecture and Computer Technology Department, ETSII-EPS, University of Seville, 41004 Sevilla, SpainArchitecture and Computer Technology Department, ETSII-EPS, University of Seville, 41004 Sevilla, SpainFalls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system’s feasibility.https://www.mdpi.com/1424-8220/21/5/1889accelerometerdeep learningembedded systemfall detectionwearablerecurrent neural networks |
| spellingShingle | Francisco Luna-Perejón Luis Muñoz-Saavedra Javier Civit-Masot Anton Civit Manuel Domínguez-Morales AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks Sensors accelerometer deep learning embedded system fall detection wearable recurrent neural networks |
| title | AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks |
| title_full | AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks |
| title_fullStr | AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks |
| title_full_unstemmed | AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks |
| title_short | AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks |
| title_sort | ankfall falls falling risks and daily life activities dataset with an ankle placed accelerometer and training using recurrent neural networks |
| topic | accelerometer deep learning embedded system fall detection wearable recurrent neural networks |
| url | https://www.mdpi.com/1424-8220/21/5/1889 |
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