Elderly Fall Detection and Fall Direction Detection via Various Machine Learning Algorithms Using Wearable Sensors
The world population is aging rapidly. Some of the elderly live alone and it is observed that the elderly who live with their families frequently have to stay at home alone, especially during the working hours of adult members of the family. Falling while alone at home often results in fatal injurie...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hitit University
2021-09-01
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| Series: | Hittite Journal of Science and Engineering |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1551147 |
| _version_ | 1797662766080720896 |
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| author | Yılmaz Güven Sıtkı Kocaoğlu |
| author_facet | Yılmaz Güven Sıtkı Kocaoğlu |
| author_sort | Yılmaz Güven |
| collection | DOAJ |
| description | The world population is aging rapidly. Some of the elderly live alone and it is observed that the elderly who live with their families frequently have to stay at home alone, especially during the working hours of adult members of the family. Falling while alone at home often results in fatal injuries and even death in elderly individuals. Fall detection systems detect falls and provide emergency healthcare services quickly. In this study, a two-step fall detection and fall direction detection system has been developed by using a public dataset and by testing 5 different machine learning algorithms comparatively. If a fall is detected in the first stage, the second stage is started and the direction of the fall is determined. In this way, the fall direction of the elderly individual can be determined for use in future researches, and a system that enables necessary measures such as opening an airbag in the direction of the fall is developed. Thus, a gradual fall detection and fall direction detection system has been developed by determining the best classifying algorithms. As a result, it has been determined that Ensemble Subspace k-NN classifier performs a little more successful classification compared to other classifiers. The classification via the test data corresponding to 30% of the total data, which was never used during the training phase, has been performed with 99.4% accuracy, and then 97.2% success has been achieved in determining the direction of falling. |
| first_indexed | 2024-03-11T19:03:56Z |
| format | Article |
| id | doaj.art-ff22c1f298764a408850e448f91b9ff9 |
| institution | Directory Open Access Journal |
| issn | 2148-4171 |
| language | English |
| last_indexed | 2024-03-11T19:03:56Z |
| publishDate | 2021-09-01 |
| publisher | Hitit University |
| record_format | Article |
| series | Hittite Journal of Science and Engineering |
| spelling | doaj.art-ff22c1f298764a408850e448f91b9ff92023-10-10T11:17:26ZengHitit UniversityHittite Journal of Science and Engineering2148-41712021-09-018319720510.17350/HJSE19030000230150Elderly Fall Detection and Fall Direction Detection via Various Machine Learning Algorithms Using Wearable SensorsYılmaz Güven0Sıtkı Kocaoğlu1KIRKLARELİ ÜNİVERSİTESİKIRKLARELİ ÜNİVERSİTESİThe world population is aging rapidly. Some of the elderly live alone and it is observed that the elderly who live with their families frequently have to stay at home alone, especially during the working hours of adult members of the family. Falling while alone at home often results in fatal injuries and even death in elderly individuals. Fall detection systems detect falls and provide emergency healthcare services quickly. In this study, a two-step fall detection and fall direction detection system has been developed by using a public dataset and by testing 5 different machine learning algorithms comparatively. If a fall is detected in the first stage, the second stage is started and the direction of the fall is determined. In this way, the fall direction of the elderly individual can be determined for use in future researches, and a system that enables necessary measures such as opening an airbag in the direction of the fall is developed. Thus, a gradual fall detection and fall direction detection system has been developed by determining the best classifying algorithms. As a result, it has been determined that Ensemble Subspace k-NN classifier performs a little more successful classification compared to other classifiers. The classification via the test data corresponding to 30% of the total data, which was never used during the training phase, has been performed with 99.4% accuracy, and then 97.2% success has been achieved in determining the direction of falling.https://dergipark.org.tr/tr/download/article-file/1551147fall detectionfalling direction detectionmachine learningwearable sensorsaccelerometers |
| spellingShingle | Yılmaz Güven Sıtkı Kocaoğlu Elderly Fall Detection and Fall Direction Detection via Various Machine Learning Algorithms Using Wearable Sensors Hittite Journal of Science and Engineering fall detection falling direction detection machine learning wearable sensors accelerometers |
| title | Elderly Fall Detection and Fall Direction Detection via Various Machine Learning Algorithms Using Wearable Sensors |
| title_full | Elderly Fall Detection and Fall Direction Detection via Various Machine Learning Algorithms Using Wearable Sensors |
| title_fullStr | Elderly Fall Detection and Fall Direction Detection via Various Machine Learning Algorithms Using Wearable Sensors |
| title_full_unstemmed | Elderly Fall Detection and Fall Direction Detection via Various Machine Learning Algorithms Using Wearable Sensors |
| title_short | Elderly Fall Detection and Fall Direction Detection via Various Machine Learning Algorithms Using Wearable Sensors |
| title_sort | elderly fall detection and fall direction detection via various machine learning algorithms using wearable sensors |
| topic | fall detection falling direction detection machine learning wearable sensors accelerometers |
| url | https://dergipark.org.tr/tr/download/article-file/1551147 |
| work_keys_str_mv | AT yılmazguven elderlyfalldetectionandfalldirectiondetectionviavariousmachinelearningalgorithmsusingwearablesensors AT sıtkıkocaoglu elderlyfalldetectionandfalldirectiondetectionviavariousmachinelearningalgorithmsusingwearablesensors |