Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors

Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains. Gait a person’s identity that can reflect reliable information about his mood, emotions, substantial traits under scrutiny. This focuses on recognizing key traits, including neuroticism, extraversion, openness to experience, agreeableness, conscientiousness, in line with big-five model personality. We inferred based individuals utilizing built-in smartphone sensors. For experimentation, we collected novel dataset 22 participants an android application further segmented it into six data chunks for critical evaluation. After pre-processing, extracted selected features from each segment then applied four multiclass machine learning algorithms training classifying corresponding users’ Big-Five Personality Traits Profiles (BFPT). Experimental results performance evaluation classifiers revealed efficacy proposed scheme all traits.