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Real-time human activity recognition is a popular and challenging topic in sensor systems. Inertial measurement units, vision-based systems, and wearable sensor systems are mostly used for gathering motion data. However, each system has drawbacks such as drift error, illumination, occlusion, etc. Therefore, under certain circumstances, they are not efficient alone in activity estimation. To overcome this, hybrid sensor systems were used as an alternative approach in the last decade. In this study, a human activity recognition system is proposed using textile-based capacitive sensors. The aim of the system is to recognize the basic human actions in real-time such as walking, running, squatting, and standing. The sensor system proposed in this study is used to collect human activity data from the participants with different anthropometrics and create an activity recognition system. The performance of the machine learning models is tested on unseen activity data. The obtained results showed the effectiveness of our approach by achieving high accuracy up to 83.1% on selected human activities in real-time.