Very Low Power Neural Network FPGA Accelerators for Tag-Less Remote Person Identification Using Capacitive Sensors

Human detection, identification, and monitoring are essential for many applications aiming to make smarter the indoor environments, where most people spend much of their time (like home, office, transportation, or public spaces). The capacitive sensors can meet stringent privacy, power, cost, and unobtrusiveness requirements, they do not rely on wearables or specific human interactions, but they may need significant on-board data processing to increase their performance. We comparatively analyze in terms of overall processing time and energy several data processing implementations of multilayer perceptron neural networks (NNs) on board capacitive sensors. The NN architecture, optimized using augmented experimental data, consists of six 17-bit inputs, two hidden layers with eight neurons each, and one four-bit output. For the software (SW) NN implementation, we use two STMicroelectronics STM32 low-power ARM microcontrollers (MCUs): one MCU optimized for power and one for performance. For hardware (HW) implementations, we use four ultralow-power field-programmable gate arrays (FPGAs), with different sizes, dedicated computation blocks, and data communication interfaces (one FPGA from the Lattice iCE40 family and three FPGAs from the Microsemi IGLOO family). Our shortest SW implementation latency is 54.4 µs and the lowest energy per inference is 990 nJ, while the shortest HW implementation latency is 1.99 µs and the lowest energy is 39 nJ (including the data transfer between MCU and FPGA). The FPGAs active power ranges between 6.24 and 34.7 mW, while their static power is between 79 and 277 µW. They compare very favorably with the static power consumption of Xilinx and Altera low-power device families, which is around 40 mW. The experimental results show that NN inferences offloaded to external FPGAs have lower latency and energy than SW ones (even when using HW multipliers), and the FPGAs with dedicated computational blocks (multiply-accumulate) perform best

Neural Networks for Indoor Human Activity Reconstructions

Low cost, ubiquitous, tagless, and privacy aware indoor monitoring is essential to many existing or future applications, such as assisted living of elderly persons. We explore how well different types of neural networks in basic configurations can extract location and movement information from noisy experimental data (with both high-pitch and slow drift noise) obtained from capacitive sensors operating in loading mode at ranges much longer that the diagonal of their plates. Through design space exploration, we optimize and analyze the location and trajectory tracking inference performance of multilayer perceptron (MLP), autoregressive feedforward, 1D Convolutional (1D-CNN), and Long-Short Term Memory (LSTM) neural networks on experimental data collected using four capacitive sensors with 16 cm x 16 cm plates deployed on the boundaries of a 3 m x 3 m open space in our laboratory. We obtain the minimum error using a 1D-CNN [0.251 m distance Root Mean Square Error (RMSE) and 0.307 m Average Distance Error (ADE)] and the smoothest trajectory inference using an LSTM, albeit with higher localization errors (0.281 m RMSE and 0.326 m ADE). 1D Convolutional and window-based neural networks have best inference accuracy and smoother trajectory reconstruction. LSTMs seem to infer best the person movement dynamics

Machine Learning Techniques for Device-Free Indoor Person Tracking

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Capacitive sensor for tagless remote human identification using body frequency absorption signatures

Although useful for many applications, the practical use of tagless remote human identification is often hampered by privacy, usability, reliability, or cost concerns. In this paper, we explore the use of capacitive sensors, which appear to address most of these concerns, to identify different persons based on the unique electric and dielectric properties of their bodies given by their specific tissue composition. We present experimental results obtained by measuring the capacitance of a 16 cm × 16 cm transducer plate 70 cm in front of different human bodies at different frequencies in the 5-160-kHz range. The measurements show clearly distinct signatures of capacitance variation with frequency for each person in the experiment, even after accounting for capacitance variations due to different body masses or physical dimensions. This paper focuses on the contactless identification of human body using capacitive sensors in smart home environments