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

Isu Shin, Jongsang Son, Soonjae Ahn, Jeseong Ryu, Sunwoo Park, Jongman Kim, Baekdong Cha, Eunkyoung Choi, Youngho Kim

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Article number394340
JournalMathematical Problems in Engineering
StatePublished - 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Mathematics
  • General Engineering


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