# A Resource Constrained Neural Network for the Design of Embedded Human Posture Recognition Systems
###### Abstract  
A custom HW design of a Fully Convolutional Neural Network (FCN) is presented in this paper to implement an embeddable Human Posture Recognition (HPR) system capable of very high accuracy both for laying and sitting posture recognition. The FCN exploits a new base-2 quantization scheme for weight and binarized activations to meet the optimal trade-off between low power dissipation, a very reduced set of instantiated physical resources and state-of-the-art accuracy to classify human postures. By using a limited number of pressure sensors only, the optimized HW implementation allows keeping the computation close to the data sources according to the edge computing paradigm and enables the design of embedded HP systems. The FCN can be simply reconfigured to be used for laying and sitting posture recognition. Tested on a public dataset for in-bed posture classification, the proposed FCN obtains a mean accuracy value of 96.77% to recognize 17 different postures, while a small custom dataset has been used for training and testing for sitting posture recognition, where the FCN achieves 98.88% accuracy to recognize eight positions. The FCN has been prototyped on a Xilinx Artix 7 FPGA where it exhibits a dynamic power dissipation lower than 11 mW and 7 mW for laying and sitting posture recognition, respectively, and a maximum operation frequency of 47.64 MHz and 26.6 MHz, corresponding to an Output Data Rate (ODR) of the sensors of 16.50 kHz and 9.13 kHz, respectively. Furthermore, synthesis results with a CMOS 130 nm technology have been reported, to give an estimation about the possibility of an in-sensor circutical implementation. p 27 April 2021 19 May 2021 21 May 20 person [11]; it has been demonstrated that posture changes and macro/micro movements are the first indicator of increasing discomfort or pain in time [12]. Thus, human posture monitoring can help in understanding what is happening to the driver/passengers and to apply countermeasures for reducing stress, discomfort and consequent errors while driving. Pressure at the interface between the seat and human body is widely used as a good indicator to evaluate the perceived (dis)comfort and to identify movements and postures on the seat [13]. Human Posture Recognition (HPR) has been recently implemented by using Machine Learning (ML) approaches [14; 15] in conjunction with mechanical or image sensors [16; 17; 18]. In particular, recent papers deal with use of machine learning and AI for posture tracking and recognition in order to improve car drivers' safety and car-occupants' comfort. In [PERSON] et al. [19], computer vision technology has been used to recognize the head and neck posture in order to detect drivers' drowsiness or sleepiness. Since 2010, in [PERSON] et al. [20], Neural Networks have been used for parts-based object detection of human body parts, both inside and outside the car. The purpose of this research was focused on techniques for identifying drivers' or pedestrians' postures for safety reasons. The need to acquire and recognize a posture with a contactless or embedded system has become one of the studied topics for future vehicle development. In 2017, [PERSON] et al. [21] used Kinect(tm) in order to track the human segments of car occupants (especially very young occupants) in order to recognize occupant posture and improve safety in case of an accident. In 2020, [PERSON] et al. [22] proposed a head pose estimation method based on deep learning applied to images to demonstrate that the head pose could be used as the basis for distraction detection. Although image-based HPR systems have been favored by the advancements of image processing methods of recent years, they introduce important issues related to the privacy of people captured by the camera, the poor performances when the subject is partially occluded, as well as the cost of the systems.