This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ech T Press Science Computer Systems Science & Engineering DOI: 10.32604/csse.2023.036185 Article Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors Kainat Ibrar 1 , Abdul Muiz Fayyaz 1 , Muhammad Attique Khan 2 , Majed Alhaisoni 3 , Usman Tariq 4 , Seob Jeon 5 and Yunyoung Nam 6, * 1 Department of Computer Science, University of Wah, Wah Cantt, Pakistan 2 Department of Computer Science, HITEC University, Taxila, Pakistan 3 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia 4 Department of Management Information Systems, CoBA, Prince Sattam Bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia 5 Department of Obstetrics and Gynecology, Soonchunhyang University Cheonan Hospital, Soonchunhyang University College of Medicine, Cheonan, Korea 6 Department of ICT Convergence, Soonchunhyang University, Asan, 31538, Korea *Corresponding Author: Yunyoung Nam. Email: ynam@sch.ac.kr Received: 20 September 2022; Accepted: 14 December 2022 Abstract: Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains. Gait is a person’s identity that can reflect reliable information about his mood, emotions, and substantial personality traits under scrutiny. This research focuses on recog- nizing key personality traits, including neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness, in line with the big- five model of personality. We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors. For experimentation, we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks for a critical evaluation. After data pre-processing, we extracted selected features from each data segment and then applied four multiclass machine learning algorithms for training and classifying the dataset corresponding to the users’ Big-Five Personality Traits Profiles (BFPT). Experimental results and performance evaluation of the classifiers revealed the efficacy of the proposed scheme for all big-five traits. Keywords: Human personality; gait; pattern recognition; smartphone sensors 1 Introduction Every person has a distinctive gait. Gait recognition studies how people walk and how that can be analyzed in detail. It is defined as “the systematic investigation of human movement.” [1]. The human gait cycle, commonly referred to as the stride, is a series of motions that starts and ends with one foot contacting the ground. Stance and swing are the two phases that make up a stride. 60% of the gait