A Novel Short-Time Fourier Transform-Based Fall Detection Algorithm Using 3-Axis Accelerations

• Published in: Mathematical problems in engineering Vol. 2015; no. 2015; pp. 1 - 7
• Main Authors: Kim, Youngho, Choi, Eunkyoung, Kim, Jongman, Park, Sunwoo, Ryu, Jeseong, Ahn, Soonjae, Son, Jongsang, Shin, Isu, Cha, Baekdong
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015; John Wiley & Sons, Inc
• Subjects: Acceleration; Activities of daily living; Algorithms; Classification; Engineering; Fall detection; Falls; Fourier analysis; Fourier transforms; Frequencies; Frequency bands; Health care; Inertia; Injuries; Mathematical analysis; Motion capture; Performance evaluation; Sensors; Three axis; Vectors (mathematics); Velocity; Wavelet transforms; United Kingdom > UK; United States > US
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2015/394340

The short-time Fourier transform- (STFT-) based algorithm was suggested to distinguish falls from various activities of daily living (ADLs). Forty male subjects volunteered in the experiments including three types of falls and four types of ADLs. An inertia sensor unit attached to the middle of two anterior superior iliac spines was used to measure the 3-axis accelerations at 100 Hz. The measured accelerations were transformed to signal vector magnitude values to be analyzed using STFT. The powers of low frequency components were extracted, and the fall detection was defined as whether the normalized power was less than the threshold (50% of the normal power). Most power was observed at the frequency band lower than 5 Hz in all activities, but the dramatic changes in the power were found only in falls. The specificity of 1–3 Hz frequency components was the best (100%), but the sensitivity was much smaller compared with 4 Hz component. The 4 Hz component showed the best fall detection with 96.9% sensitivity and 97.1% specificity. We believe that the suggested algorithm based on STFT would be useful in the fall detection and the classification from ADLs as well.