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The name Edge Intelligence , also known as Edge AI , is a recent term used in the last few years to refer to the confluence of Machine Learning, or broadly speaking Artificial Intelligence, with Edge Computing. In this manuscript, we revise the concepts regarding Edge Intelligence, such as Cloud, Edge and Fog Computing, the motivation to use Edge Intelligence, and compare current approaches and analyze application scenarios. To provide a complete review of this technology, previous frameworks and platforms for Edge Computing have been discusses in this manuscript in order to provide the general view of the basis for Edge AI. Similarly, the emerging techniques to deploy Deep Learning (DL) models at the network edge, as well as specialized platforms and frameworks to do so, are review in this manuscript. These devices, techniques and frameworks are analyzed based on relevant criteria at the network edge such as latency, energy consumption and accuracy of the models to determine the current state of the art as well as current limitations of the proposed technologies. Because of this, it is possible to understand what are the current possibilities to efficiently deploy state-of-the-art DL models at the network edge based on technologies such as AI accelerators, Tensor Processing Units and techniques that include Federated Learning and Gossip Training. Finally, the challenges of Edge AI are discusses in the manuscript as well as the Future directions that can be extracted from the evolution of the Edge Computing and Internet of Things (IoT) approaches.

The increasing significance of technology in daily lives led to the need for the development of convenient methods of human-computer interaction (HCI). Given that the existing HCI approaches exhibit various limitations, hand gesture recognition-based HCI may serve as a more intuitive mode of humanmachine interaction in many situations. In addition, the system has to be deployable on low-power devices for applicability in broadly defined Internet of Things (IoT) and smart home solutions. Recent advances exhibit the potential of deep learning models for gesture classification, whereas they are still limited to high-performance hardware. Embedded neural network accelerators are constrained in terms of available memory, central processing unit (CPU) clock speed, graphics processing unit (GPU) performance, and a number of supported operations. The aforementioned problems are addressed in this paper by namely two approaches -simplifying the signal processing pipeline to avoid recurrent structures and efficient topological design. This paper employs an intuitive scheme allowing for the generation of the data in the compressed form from the sequence of range-Doppler images (RDI). Thus, it allows for the design of a neural classifier avoiding the usage of recurrent layers. The proposed framework has been optimized for Intel ® Neural Compute Stick 2 (Intel ® NCS 2), at the same time achieving promising classification accuracy of 97.57%. To confirm the robustness of the proposed algorithm, five independent persons have been involved in the algorithm testing process.

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