Background: Stumbles are common accidents that can result in falls and serious injuries, particularly in the workplace where back and forth movements are involved and in offices where high heels are imperative. Currently, the characteristics of plantar pressure during a stumble and the differences between stumbling and a normal gait remain unclear.
Objective: This paper is aimed at providing insights into the feasibility of the data mining technique for interventions in stumble-related occupational safety issues.
Methods: The characteristics of plantar pressure distribution during stumbling and normal gait were analyzed by using the power spectrum density (PSD) and the Support Vector Machine (SVM). The PSD, a novel pattern recognition feature, was used to mathematically describe the image signal. The SVM, a powerful data mining technique, was used as the classifier to recognize a stumble. Dynamic plantar pressures were measured from twelve healthy participants as they walked.
Results: The plantar pressures of the stumbling gaits had significantly different patterns compared to the normal ones, from either a qualitative or quantitative perspective. The mean recognition accuracy of the proposed method reached 96.7%.
Conclusions: This study helps better understand stumbles and provides a theoretical basis for stumble-related occupational injuries. In addition, the stumble is the precursor of a fall and the research on stumble recognition would be of value to predict and provide warnings of falls and to design anti-fall devices for potential victims.
Keywords: Gait recognition; SVM; artificial intelligence; power spectrum density.