Otokodlayıcı Tabanlı Boyut Azaltma ve Akıllı Saat Tabanlı Giyilebilir Hareket Algılayıcıları Kullanarak Yaşlılarda Düşme Tespiti

Year 2023, , 1150 - 1159, 30.10.2023

Ensar Arif Sağbaş , Serkan Ballı

https://doi.org/10.35414/akufemubid.1281350

Abstract

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.

Keywords

Otokodlayıcı, Boyut azaltma, Düşme tespiti, Lojistik regresyon, Akıllı saat, Giyilebilir cihazlar

Supporting Institution

Muğla Sıtkı Koçman Üniversitesi

Project Number

16-061

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

Abstract

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.

Keywords

Autoencoder, Dimensionality reduction, Fall detection, Logistic regression, Smartwatch, Wearable devices

Project Number

16-061

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