This paper outlines the development of a Hardware Development Kit (HDK) for a remote training platform on FPGA Devices designed to provide university students pursuing degrees in electronic and informatics engineering (at the bachelor’s, master’s, and PhD levels) with the tools they need to learn and develop systems related to artificial intelligence, digital signal processing, image processing, digital systems, integrated and embedded systems using an FPGA chip. Through the internet or a local network, students can engage with the platform to conduct engineering experiments. The HDK is equipped with a Raspberry Pi, a screen, a camera, and LEDs to facilitate the transfer of experiment results and to aid in assessing the validity of lab experiment execution. The Raspberry Pi plays a crucial role in the HDK, providing control and monitoring, remote access, data processing, user interface, and integration with other possible systems.

We present in this paper a machine learning model for detecting violence against children. This model, which uses skeletal data acquired by depth sensors achieved a high accuracy violence detection rate of 99.03 %. In sum, this research method presents: •First ML-based method for detecting most common child abuses, which keeps the privacy of people by using only skeleton joints data.•The model has only two classes (violent action, non-violent action).•The model can be a base for other researches and implementations in schools by school psychologists and counselors.

We present in this paper an FPGA-based IP for recognizing most common violent actions against children (VACR IP). VACR IP uses only skeleton joints data as inputs to keep the privacy of people inside their homes or in the schools. The proposed hardware achieved a detection rate of 97.72%, processing speed 761FPS, and a latency value equals to 2.79msec using a 50MHz system clock.

We present in this paper a novel dataset (MMU VAAC) for violent actions against children recognition. This original dataset has been recorded using Microsoft Kinect with the usage of a child mannequin. MMU VAAC dataset contains skeleton joints, depth, and RGB modalities.

Action recognition is gaining more importance and attention due to its wide applications. But the need of implementing it in real time is essential. This paper suggests a hardware/software co-design which accelerates the whole recognizing function. In this setup we are using Microsoft © Kinect device, Intel © Atom system and FPGA based SoC device from Altera © . The initial experiments show that it is possible to recognize actions in real time using the proposed setup by processing the skeleton data. This promising result would encourage designers to take the step forward of using mixed CPU and FPGA based SoC platform to increase the performance of image and video processing operations especially in recognition systems.