Personality Assessment From Gait With Wearable IoT Sensors and Multiscale CNN

Abstract

Personality reflects an individual’s enduring patterns of thought and behavior, while gait—a measurable and consistent behavioral trait—offers a unique and objective way to assess personality through natural, nonvolitional movement. Unlike traditional methods, such as self-report questionnaires, which are often subject to biases and limited accuracy, gait-based assessment provides a more direct and spontaneous measure of personality. This study introduces a gait-based personality assessment system that leverages a low-cost wearable Internet of Things (IoT) sensor to capture fine-grained motion data, including triaxial acceleration and angular velocity from the wrist and the ankle. By focusing on the natural, involuntary aspects of gait, the system avoids the biases inherent in self-presentation. Additionally, the study presents the “Gait–Personality” dataset, featuring advanced gait phase segmentation and optimized feature extraction techniques to enhance data quality. To tackle challenges like variability in stride length and cadence, a multiscale 1-D convolutional neural network (MS-1D-CNN) was developed. By utilizing convolutional layers with multiple kernel sizes, the model captures both detailed and high-level temporal features, effectively adapting to diverse gait patterns while remaining robust to sensor variability. Experimental results demonstrate classification accuracies ranging from 77% to 84.5% across the Big Five personality dimensions, validating the system’s ability to objectively capture authentic personality traits. This study establishes a reliable, cost-efficient, and scalable framework for personality assessment, offering broad implications for psychological evaluation, mental health monitoring, and human–computer interaction, with the potential for widespread real-world applications.