Accurate tagless indoor person localization is important for several applications, such as assisted living and health monitoring. Machine learning (ML) classifiers can effectively mitigate sensor data variability and noise due to deployment-specific environmental conditions. In this paper, we use experimental data from a capacitive sensor-based indoor human localization system in a 3 m × 3 m room to comparatively analyze the performance of Weka collection ML classifiers. We compare the localization performance of the algorithms, its variation with the training set size, and the algorithm resource requirements for both training and inferring. The results show a large variance between algorithms, with the best accuracy, precision, and recall exceeding 93% and 0.05 m average localization error.

Accurate indoor person localization is essential for several services, such as assisted living. We introduce a tagless indoor person localization system based on capacitive sensing and localization algorithms that can determine the location with less than 0.2 m average error in a 3 m × 3 m room and has recall and precision better than 70%. We also discuss the effects of various noise types on the measurements and ways to reduce them using filters suitable for on-sensor implementation to lower communication energy consumption. We also compare the performance of several standard localization algorithms in terms of localization error, recall, precision, and accuracy of detection of the movement trajectory.

Indoor localization has many pervasive applications, like energy management, health monitoring, and security. Tagless localization detects directly the human body, like passive infrared sensing, and is the most amenable to different users and use cases. We evaluate the localization and tracking performance, as well as resource and processing requirements of various neural network (NN) types using directly the data from a low resolution 16-pixel thermopile sensor array in a 3 m x 3 m room. Out of the multilayer perceptron, autoregressive, 1D-CNN, and LSTM NN architectures that we test, the latter require more resources but can accurately locate and capture best the person movement dynamics, while the 1D-CNN provides the best compromise between localization accuracy (9.6 cm RMSE) and movement tracking smoothness with the least resources, and seem more suited for embedded applications.

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 × 16 cm plates deployed on the boundaries of a 3 m × 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.

Ever since transistor cost stopped decreasing, customized programmable platforms, such as field-programmable gate arrays (FPGAs), became a major way to improve software execution performance and energy consumption. While software developers can use high-level synthesis (HLS) to speed up register-transfer level (RTL) code generation from C++ or OpenCL source code, placement and routing issues, such as congestion, can still prevent achieving an FPGA programming bitstream or dramatically reduce the FPGA implementation performance. Congestion reports from physical design tools refer to thousands of RTL signal names instead of developer-accessible identifiers and statements, considerably complicating the developer understanding and resolution of the issues at the source level. We propose a high-level back-annotation flow that summarizes the routing congestion issues at the source level by analyzing the reports from the FPGA physical design tools and the internal debugging files of the HLS tools. Our flow describes congestion using comments back-annotated on the source code and identifies if the congestion causes are the on-chip memories or the DSP units (multipliers/adders), which are the shared resources very often associated with routing problems on FPGAs. We demonstrate on realistic large designs how the information provided by our flow helps to quickly spot congestion causes at the source level and to solve them using appropriate HLS directives.

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 x 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.