Personality Assessment from Gait with Wearable IoT Sensors and Multi-Scale CNN

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, non-volitional 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 tri-axial acceleration and angular velocity from the wrist and 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, amulti-scale 1D 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.