In this paper, we introduce a proof-of-concept multi-sensory footwear prototype with artificial intelligence to facilitate human activity recognition. We equipped a shoe with a force sensitive pressure sensor, an accelerometer, and a gyroscope in order to detect human activity. Such sensors allow the system to capture and analyze data about various physical movements, which are further processed in order to detect specific human activities. To achieve accurate activity recognition, we trained and compared several models, which are two types of convolutional neural networks (CNN) and a conventional support vector machine (SVM). The system’s accuracy in identifying activities like standing, sitting, walking, running, and jumping was evaluated, and scored highest using a MobileNet CNN with 83.33% accuracy. With this work, we demonstrate that a somewhat robust real-time activity recognition is feasible with prototypical hardware.