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 circuital implementation.
Highlights
The capability to classify human postures can be considered a specific subset of the Human Activity Recognition (HAR) [3,4,5], it requires specific technological solutions, very different from HAR, and is very important in peculiar application fields
By using the Cadence toolchain return a power dissipation of 425 μW/MHz and 1.7 mW, respectively, at the maximum operating frequency of 40 MHz, and an area occupation 1.78 mm2 when the Fully Convolutional Neural Network (FCN) is configured for laying posture recognition, which support in real-time Output Data Rate (ODR) up to 8.7 kHz
The commercial measurement system has been chosen to make reliable acquisitions, but the number of sensors is suitable for different applications and, in this case, it is excessive for the FCN operations
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Implemented on a small Xilinx Artix 7 FPGA, FCN dissipates 10.40 mW dynamic power and achieves a maximum operation frequency of 26.6 MHz, corresponding to sensors with Output Data Rate (ODR) of 9.13 kHz, when used for laying posture recognition. By using the Cadence toolchain return a power dissipation of 425 μW/MHz and 1.7 mW, respectively, at the maximum operating frequency of 40 MHz, and an area occupation 1.78 mm when the FCN is configured for laying posture recognition, which support in real-time ODR up to 8.7 kHz. The remainder of the paper is organized as follows: Section 2 describes the proposed models; design choice and architecture of the HW accelerator are discussed in Section 3; implementation results are presented in Section 4; comparisons with the state-of-the-art are discussed in Section 5; Section 6 concludes the paper
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