Determination of Footstep Sounds for Elderly Fall Prevention

Open Access
Article
Conference Proceedings
Authors: Keiho NaganoYoshihisa NakatohHideaki Kawano

Abstract: With the aging of the population, the number of people aged 65 and over living alone is increasing every year. Accompanying this trend, the number of deaths due to falls is also on the rise. One of the characteristics of the walking style of people who fall down is that they walk with a frosted gait. The ultimate goal of this research is to construct a system that can issue a warning when it is determined from footsteps that a person is walking with a slipshod gait. The proposed method uses three types of audio data: normal footsteps, footsteps caused by a mildly slip walking, and footsteps caused by a slip walking. Two-step silence intervals and 1-12 dimensional MFCC were used as features, and P-values between footsteps were obtained for the silence intervals and MFCC of the three types of footsteps by Wilcoxon's signed-rank test. The results showed significant differences. Although significant differences were obtained when the silent interval was used as a feature, there is a possibility that footsteps are misrecognized as normal sounds because the silent interval varies depending on the speech data, even for the same walking style. On the other hand, with MFCC, significant differences were obtained, especially for the 6th, 7th, and 8th order coefficients, and the variation was small. Therefore, we decided to use MFCC in our identification experiments. As a discrimination model, we used Random Forest (RF), a type of machine learning, for three classifications and obtained a 91% correct response rate after evaluation by 10-fold cross-validation. The reason for the high correct response rate can be attributed to the fact that the footsteps were recorded at a stable volume in a quiet environment. It could also be attributed to the large differences among the three types of walking styles.

Keywords: sliding footsteps elderly assistance acoustic signature

DOI: 10.54941/ahfe1002795

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