(eng): The present paper describes hand detection application implemented on Xilinx Zynq Ultrascale+ device, comprising multi-core processor ARM Cortex A53 and FPGA programmable logic. It uses ultrasound data and is based on adaptive QRD RLS lattice algorithm extended with hypothesis testing. The algorithm chooses between two use-cases: (1) “there is a hand in front of the device” vs (2) “there is no hand in front of the device”. For these purposes a new structure of the identification models was designed. The model presenting use-case (1) is a regression model, which has the order sufficient to cover all incoming data. The model responsible for use-case (2) is a regression model, which has a smaller order than the model (1) and a certain time delay, covering the maximal distance where the hand can possibly appear. The offered concept was successfully verified using real ultrasound data in MATLAB optimized for parallel processing and implemented in parallel on four cores of ARM Cortex A53 processor. It was proved that computational time of the algorithm is sufficient for applications requiring real-time processing.\n