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

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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Mathematical problems in engineering Ročník 2015; číslo 2015; s. 1 - 7
Hlavní autoři: Kim, Youngho, Choi, Eunkyoung, Kim, Jongman, Park, Sunwoo, Ryu, Jeseong, Ahn, Soonjae, Son, Jongsang, Shin, Isu, Cha, Baekdong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015
John Wiley & Sons, Inc
Témata:
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, 1563-5147
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1024-123X
1563-5147
DOI:10.1155/2015/394340