Embedded Low-Power Processor for Personalized Stress Detection

Abstract

Personal monitoring systems require sampling and processing on multiple streams of physiological signals to extract meaningful information. These systems require a large number of digital signal processing and machine learning kernels which typically require significant amounts of power. However, to be used in a wearable environment, the processing system needs to be low-power, real-time, and light-weight. In this paper, we present a personalized stress monitoring processor that can meet these requirements. First, various physiological features are explored to maximize stress detection accuracy using two machine learning classifiers including Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). Among different extracted features from four physiological sensors, heart rate and accelerometer features have 96.7% (SVM) and 95.8% (KNN) detection accuracy. In the second part, two fully flexible and multi-modal processing hardware designs are presented that consist of feature extraction and classification algorithms. We first demonstrate the ASIC post-layout implementation of both designs in 65 nm CMOS technology as well as the implementation on Artix-7 FPGA. The proposed SVM and KNN processors on the ASIC platform occupy an area of 0.17 mm2 and 0.3 mm2 and dissipate 39.4 mW and 76.69 mW power, respectively. The ASIC implementation improves the energy efficiency by 42x (SVM) and 12x (KNN) over FPGA implementations. The entire stress monitoring system is further evaluated against a number of other platforms including Raspberry Pi 3B, NVIDIA TX1 GPU and NVIDIA TX2 GPU. The experimental results indicate that ASIC and FPGA platforms have the highest throughput (decision/sec) as well as lowest power consumption over all other platforms. The ASIC/FPGA implementations improve the energy efficiency (throughput/power) by 6/5 and 5/4 order of magnitude compared to TX1 GPU and Raspberry pie ARM platforms, respectively.