Edge-AI uses Artificial Intelligence algorithms directly embedded on a device, contrary to a remote AI that uses an AI on a cloud or remote server for prediction. Recent improvements in microcontroller computing capabilities and enhanced deep learning algorithms and conversion frameworks made it easier to run small AI models directly on microcontroller units. Is the current interest in on-device AI justified in terms of its energy consumption on resource-constrained devices when compared to AI on the cloud? This study presents how an embedded deep convolutional neural network (DCNN) is used for real-time human activity recognition with more than 98% classification accuracy and its impact on battery life. Experiments conducted on a triaxial accelerometer with data collected and processed by an ARM Cortex-M4-based development board showed that energy consumption could be reduced up to 21% when inferences are run on an edge device versus using a remote server/cloud without compromising the overall classification precision and accuracy. We can reduce energy consumption by limiting data transmission by considering pseudo-real-time or non-real-time application scenarios.