Assessment of Real-World Fall Detection Solution Developed on Accurate Simulated-Falls
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Science and Business Media Deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
One of the urgent and popular research areas is wearable devices-based fall detection (FD). Over the past 20 years, researchers have conducted many experiments in which falls and activities of daily living were simulated. Researchers inferred that real-world fall data is in demand rather than simulated fall data, but this inference still lacks comparisons. In this study, an assessment of a simulated fall dataset and a real-world fall dataset is proposed. The assessment investigates the efficacy of simulated data for developing an FD solution. Comparisons were conducted between FD methods developed on simulated and real-world data to observe the effectiveness of simulated falls. The experiments showed that the method with real-world data offered similar performances to the method with simulated data. In contrast to existing solutions, the provided comparison revealed that accurate simulated data are beneficial for developing a real-world FD solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Açıklama
12th International Conference on Robotics, Vision, Signal Processing, and Power Applications, ROVISP 2023 -- 28 August 2023 through 29 August 2023 -- Penang -- 310309
Anahtar Kelimeler
Accelerometer, Fall detection, Machine learning, Real-world fall, Simulated fall, SVM
Kaynak
Lecture Notes in Electrical Engineering
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
1123 LNEE
