Open Access

ARTICLE

Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors

by Kainat Ibrar¹, Abdul Muiz Fayyaz¹, Muhammad Attique Khan², Majed Alhaisoni³, Usman Tariq⁴, Seob Jeon⁵, 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:

Computer Systems Science and Engineering 2023, 46(2), 2351-2368. https://doi.org/10.32604/csse.2023.036185

Received 20 September 2022; Accepted 14 December 2022; Issue published 09 February 2023

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 recognizing 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

---