Human Activity Recognition (HAR) and gender recognition have become pivotal in advancing intelligent systems, as they enable tailored user experiences and enhance automation in healthcare, security, and personalized technology. By accurately identifying human activities and gender, systems can proactively adapt to user needs, improving human-computer interaction, reducing response times in emergency detection, and enhancing the quality of life in smart home and assistive living applications. In this study, a Kolmogorov-Arnold Network model was created to predict daily living activities and the gender of the person performing an activity. The proposed Kolmogorov-Arnold Network (KAN) classifier outperformed the previous studies with 94.5% and 95.6% in terms of overall accuracy for multi-class HAR and gender recognition tasks, respectively. Additionally, the Chi-square test results demonstrated that there is a statistically significant difference between the performances. It can be concluded that KAN method is a robust classifier especially for detecting activities that have a minor number of samples on the utilized dataset.