1,108 research outputs found
The passive operating mode of the linear optical gesture sensor
The study evaluates the influence of natural light conditions on the
effectiveness of the linear optical gesture sensor, working in the presence of
ambient light only (passive mode). The orientations of the device in reference
to the light source were modified in order to verify the sensitivity of the
sensor. A criterion for the differentiation between two states: "possible
gesture" and "no gesture" was proposed. Additionally, different light
conditions and possible features were investigated, relevant for the decision
of switching between the passive and active modes of the device. The criterion
was evaluated based on the specificity and sensitivity analysis of the binary
ambient light condition classifier. The elaborated classifier predicts ambient
light conditions with the accuracy of 85.15%. Understanding the light
conditions, the hand pose can be detected. The achieved accuracy of the hand
poses classifier trained on the data obtained in the passive mode in favorable
light conditions was 98.76%. It was also shown that the passive operating mode
of the linear gesture sensor reduces the total energy consumption by 93.34%,
resulting in 0.132 mA. It was concluded that optical linear sensor could be
efficiently used in various lighting conditions.Comment: 10 pages, 14 figure
Human Sensing via Passive Spectrum Monitoring
Human sensing is significantly improving our lifestyle in many fields such as
elderly healthcare and public safety. Research has demonstrated that human
activity can alter the passive radio frequency (PRF) spectrum, which represents
the passive reception of RF signals in the surrounding environment without
actively transmitting a target signal. This paper proposes a novel passive
human sensing method that utilizes PRF spectrum alteration as a biometrics
modality for human authentication, localization, and activity recognition. The
proposed method uses software-defined radio (SDR) technology to acquire the PRF
in the frequency band sensitive to human signature. Additionally, the PRF
spectrum signatures are classified and regressed by five machine learning (ML)
algorithms based on different human sensing tasks. The proposed Sensing Humans
among Passive Radio Frequency (SHAPR) method was tested in several environments
and scenarios, including a laboratory, a living room, a classroom, and a
vehicle, to verify its extensiveness. The experimental results show that the
SHAPR method achieved more than 95% accuracy in the four scenarios for the
three human sensing tasks, with a localization error of less than 0.8 m. These
results indicate that the SHAPR technique can be considered a new human
signature modality with high accuracy, robustness, and general applicability
Deep Learning Assisted Robust Detection Techniques for a Chipless RFID Sensor Tags
In this paper, we present a new approach for robust reading of identification and sensor data from chipless RFID sensor tags. For the first time, Machine Learning (ML) and Deep Learning (DL) regression modelling techniques are applied to a dataset of measured Radar Cross Section (RCS) data that has been derived from large-scale robotic measurements of custom-designed, 3-bit chipless RFID sensor tags. The robotic system is implemented using the first-of-its-kind automated data acquisition method using an ur16e industry-standard robot. A large data set of 9,600 Electromagnetic (EM) RCS signatures collected using the automated system is used to train and validate four ML models and four 1-dimensional Convolutional Neural Network (1D CNN) architectures. For the first time, we report an end-to-end design and implementation methodology for robust detection of identification (ID) and sensing data using ML/DL models. Also, we report, for the first time, the effect of varying tag surface shapes, tilt angles, and read ranges that were incorporated into the training of models for robust detection of ID and sensing values. The results show that all the models were able to generalise well on the given data. However, the 1D CNN models outperformed the conventional ML models in the detection of ID and sensing values. The best 1D CNN model architectures performed well with a low Root Mean Square Error (RSME) of 0.061 (0.87%) for tag ID and 0.0241 (3.44%) error for the capacitive sensing
Self-Powered Gesture Recognition with Ambient Light
We present a self-powered module for gesture recognition that utilizes small, low-cost photodiodes for both energy harvesting and gesture sensing. Operating in the photovoltaic mode, photodiodes harvest energy from ambient light. In the meantime, the instantaneously harvested power from individual photodiodes is monitored and exploited as a clue for sensing finger gestures in proximity. Harvested power from all photodiodes are aggregated to drive the whole gesture-recognition module including a micro-controller running the recognition algorithm. We design robust, lightweight algorithm to recognize finger gestures in the presence of ambient light fluctuations. We fabricate two prototypes to facilitate user’s interaction with smart glasses and smart watches. Results show 99.7%/98.3% overall precision/recall in recognizing five gestures on glasses and 99.2%/97.5% precision/recall in recognizing seven gestures on the watch. The system consumes 34.6 µW/74.3 µW for the glasses/watch and thus can be powered by the energy harvested from ambient light. We also test system’s robustness under various light intensities, light directions, and ambient light fluctuations. The system maintains high recognition accuracy (\u3e 96%) in all tested settings
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