Elderly Fall Detection Using Autoencoder Based Dimensionality  Reduction and Smartwatch Based Wearable Motion Detectors

Ensar Arif Sağbaş; Serkan Ballı

Makale detayları

Elderly Fall Detection Using Autoencoder Based Dimensionality Reduction and Smartwatch Based Wearable Motion Detectors

Yazarlar:

Ensar Arif Sağbaş | Serkan Ballı

Yayıncı:

Afyon Kocatepe University

Ana Başlıklar:

Yapay Zeka

Dergi:

Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.23 Issue 5, pp.1150-1159

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001302520

pISSN:

2149-3367

Anahtar kelimeler:

Özet:

 Falling is a serious health risk that can even result in death, especially for the elderly. For this reason, it is crucial to prevent falls and, in cases where prevention is not possible, to detect and intervene as soon as possible. Smartwatches are an ideal tool for fall detection due to their constant presence, rich sensor resources, and communication capabilities. The aim of this study is to detect falls in elderly people with high accuracy using motion sensor data obtained from smartwatches. To achieve this, a dataset was created consisting of falls and daily activities. Then, the feature vector was extracted which has provided successful results in signal processing studies. Afterward, the dimensionality of the dataset was reduced using an autoencoder-based approach in order to decrease the workload on smartwatches and ensure more accurate and faster classification. The dataset was classified using machine learning methods including naive Bayes, logistic regression, and C4.5 decision tree, and successful results were obtained. Their performances were then compared. It was observed that reducing the dimensionality had positive effects on both the classification accuracy and the computation time.

 Falling is a serious health risk that can even result in death, especially for the elderly. For this reason, it is crucial to prevent falls and, in cases where prevention is not possible, to detect and intervene as soon as possible. Smartwatches are an ideal tool for fall detection due to their constant presence, rich sensor resources, and communication capabilities. The aim of this study is to detect falls in elderly people with high accuracy using motion sensor data obtained from smartwatches. To achieve this, a dataset was created consisting of falls and daily activities. Then, the feature vector was extracted which has provided successful results in signal processing studies. Afterward, the dimensionality of the dataset was reduced using an autoencoder-based approach in order to decrease the workload on smartwatches and ensure more accurate and faster classification. The dataset was classified using machine learning methods including naive Bayes, logistic regression, and C4.5 decision tree, and successful results were obtained. Their performances were then compared. It was observed that reducing the dimensionality had positive effects on both the classification accuracy and the computation time.

 Falling is a serious health risk that can even result in death, especially for the elderly. For this reason, it is crucial to prevent falls and, in cases where prevention is not possible, to detect and intervene as soon as possible. Smartwatches are an ideal tool for fall detection due to their constant presence, rich sensor resources, and communication capabilities. The aim of this study is to detect falls in elderly people with high accuracy using motion sensor data obtained from smartwatches. To achieve this, a dataset was created consisting of falls and daily activities. Then, the feature vector was extracted which has provided successful results in signal processing studies. Afterward, the dimensionality of the dataset was reduced using an autoencoder-based approach in order to decrease the workload on smartwatches and ensure more accurate and faster classification. The dataset was classified using machine learning methods including naive Bayes, logistic regression, and C4.5 decision tree, and successful results were obtained. Their performances were then compared. It was observed that reducing the dimensionality had positive effects on both the classification accuracy and the computation time.

 Düşme, özellikle yaşlılar için ölümle bile sonuçlanabilecek ciddi bir sağlık riskidir. Bu nedenle düşmelerin önlenmesi, engellenemeyen durumlarda ise en kısa sürede tespit edilerek müdahale edilmesi büyük önem taşımaktadır. Akıllı saatler, her zaman kişinin yanında bulunması, zengin algılayıcı kaynakları ve haberleşme imkânı sayesinde düşme tespiti için ideal bir araçtır. Bu çalışmanın amacı, akıllı saatlerden elde edilen hareket algılayıcısı verilerini kullanarak yaşlı bireylerde düşmeleri yüksek doğrulukla tespit etmektir. Bunun için düşme ve günlük aktivitelerden oluşan bir veri seti oluşturulmuştur. Daha sonra sinyal işleme çalışmalarında başarılı sonuçlar veren öznitelik vektörü çıkarılmıştır. Devamında akıllı saatlerin iş yükünü azaltmak, daha doğru ve hızlı sınıflandırma sağlamak için otokodlayıcı tabanlı bir yaklaşım kullanılarak veri setinin boyutu azaltılmıştır. Naive Bayes, lojistik regresyon ve C4.5 karar ağacı makine öğrenmesi yöntemleri kullanılarak veri seti sınıflandırılmış ve başarılı sonuçlar elde edilmiştir. Sonrasında performansları karşılaştırılmıştır. Boyutsallığın azaltılmasının hem sınıflandırma doğruluğu hem de hesaplama süresi üzerinde olumlu etkileri olduğu gözlemlenmiştir.

Makale detayları

Elderly Fall Detection Using Autoencoder Based Dimensionality Reduction and Smartwatch Based Wearable Motion Detectors

Yazarlar:

Ensar Arif Sağbaş | Serkan Ballı

Yayıncı:

Afyon Kocatepe University

Ana Başlıklar:

Yapay Zeka

Dergi:

Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.23 Issue 5, pp.1150-1159

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001302520

pISSN:

2149-3367

Autoencoder

Dimensionality reduction

Fall detection

Logistic regression

Smartwatch

Wearable devicesOtokodlayıcı

Boyut azaltma

Düşme tespiti

Lojistik regresyon

Akıllı saat

Giyilebilir cihazlar

Sayı

Makale detayları

Yazarlar: Ensar Arif Sağbaş | Serkan Ballı

Yayıncı: Afyon Kocatepe University

Ana Başlıklar: Yapay Zeka

Dergi: Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.23 Issue 5, pp.1150-1159

Belge Tipi: Makale

Yayın Tarihi: 10/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001302520

pISSN: 2149-3367

Sayı

Yazarlar:

Ensar Arif Sağbaş | Serkan Ballı

Yayıncı:

Afyon Kocatepe University

Ana Başlıklar:

Yapay Zeka

Dergi:

Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.23 Issue 5, pp.1150-1159

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001302520

pISSN:

2149-3367