Fall detection through acoustic Local Ternary Patterns. (November 2018)

• Record Type: Journal Article
• Authors: Adnan, Syed M.; Irtaza, Aun; Aziz, Sumair; Ullah, M. Obaid; Javed, Ali; Mahmood, Muhammad Tariq
• Is Part Of: Applied acoustics. Volume 140(2018)
• Page Start: 296
• Page End: 300
• Publication Date: 2018-11
• Subjects: Acoustic-LTP -- SVM -- Fall detection -- Classification; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2018.06.013
• Languages: English
• ISSNs: 0003-682X

Abstract

Abstract: In this paper, we propose a framework that detects falls by using acoustic Local Ternary Patterns (acoustic-LTPs) by analyzing environmental sounds. The proposed method suppresses silence zones in sound signals and distinguishes overlapping sounds. Acoustic features are extracted from the Separated source components by using the proposed acoustic-LTPs. Subsequently, fall events are detected through a support vector machine (SVM) based classifier. The performance of the proposed descriptor is evaluated against state-of-the-art methods that are applied on well-known sound databases. A comparative analysis demonstrates that the proposed descriptor is more powerful and reliable in terms of fall detection than other methods, and it also performs well in a multi-class environment. Moreover, the proposed descriptor possesses a rotation invariant property, and therefore, it demonstrates significant resistance against the rotated sound signals.