Low Power Capacitive Ultrasonic Transceiver Array for Airborne Object Detection

Anzinger, Sebastian; Lickert, F.; Fusco, Alessandra; Bosetti, Gabriele; Tumpold, David; Bretthauer, Christian; Dehe, A.

doi:10.1109/mems46641.2020.9056182

Cited by 17 publications

(6 citation statements)

References 7 publications

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“…Ultrasound-based HGR systems such as those surveyed in [12] or presented in [23] require the user to wear a device to record the hand gestures. Other systems use beamforming, like in [24,25]. The researchers in the latter one additionally use more and bigger ultrasonic transducers.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Ultrasonic Hand Gesture Recognition

Fertl,

Nguyen,

Krueger

et al. 2024

Sensors

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As the number of electronic gadgets in our daily lives is increasing and most of them require some kind of human interaction, this demands innovative, convenient input methods. There are limitations to state-of-the-art (SotA) ultrasound-based hand gesture recognition (HGR) systems in terms of robustness and accuracy. This research presents a novel machine learning (ML)-based end-to-end solution for hand gesture recognition with low-cost micro-electromechanical (MEMS) system ultrasonic transducers. In contrast to prior methods, our ML model processes the raw echo samples directly instead of using pre-processed data. Consequently, the processing flow presented in this work leaves it to the ML model to extract the important information from the echo data. The success of this approach is demonstrated as follows. Four MEMS ultrasonic transducers are placed in three different geometrical arrangements. For each arrangement, different types of ML models are optimized and benchmarked on datasets acquired with the presented custom hardware (HW): convolutional neural networks (CNNs), gated recurrent units (GRUs), long short-term memory (LSTM), vision transformer (ViT), and cross-attention multi-scale vision transformer (CrossViT). The three last-mentioned ML models reached more than 88% accuracy. The most important innovation described in this research paper is that we were able to demonstrate that little pre-processing is necessary to obtain high accuracy in ultrasonic HGR for several arrangements of cost-effective and low-power MEMS ultrasonic transducer arrays. Even the computationally intensive Fourier transform can be omitted. The presented approach is further compared to HGR systems using other sensor types such as vision, WiFi, radar, and state-of-the-art ultrasound-based HGR systems. Direct processing of the sensor signals by a compact model makes ultrasonic hand gesture recognition a true low-cost and power-efficient input method.

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Section: Introductionmentioning

confidence: 99%

End-to-End Ultrasonic Hand Gesture Recognition

Fertl,

Nguyen,

Krueger

et al. 2024

Sensors

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“…Based on those physical changes, a wide variety of capacitive sensors have been developed, ranging from proximity sensing [1][2][3][4][5], position sensing [6,7], humidity sensing [8,9], tilt sensing [10,11], strain sensing [12,13,14], etc. As the capacitance is not dependent on the properties of the plate material, and thermal expansion is generally isotropic in three dimensions, capacitive sensors are generally considered to having low-temperature dependence compared with other sensor methods, such as resistive based [15] or resonant based [16].…”

Section: Introductionmentioning

confidence: 99%

Active Temperature Compensation for MEMS Capacitive Sensor

Seshia

2021

IEEE Sensors J.

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Temperature variations are one of the most crucial factors that need to be compensated for in MEMS sensors. Many traditional methodologies require an additional circuit to compensate for temperature. This work describes a new active temperature compensation method for MEMS capacitive strain sensors without additional circuits. The proposed method is based on a complement 2-D capacitive structure design. It consumes zero-power, which is essential toward the realization of a lowpower temperature-compensated sensor in battery-powered or energyharvesting applications. The gauge factor of the developed MEMS capacitive strain sensor is 7. The best result showing a capacitance variation of 1 ppm/ o C compared with 13 ppm/ o C on conventional design.

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“…Capacitive sensors are realized by varying any of the three parameters of a capacitor: the distance between two plates, the overlap area of plates, and the dielectric constant of an insulator. Based on those physical changes, a wide variety of capacitive sensors have been developed, ranging from proximity sensing [1][2][3][4][5], position sensing [6,7], humidity sensing [8,9], tilt sensing [10,11], strain sensing [12,13,14], etc. As the capacitance is not dependent on the properties of the plate material, and thermal expansion is generally isotropic in three dimensions, capacitive sensors are generally considered to having low-temperature dependence compared with other sensor methods, such as resistive based [15] or resonant based [16].…”

Section: Introductionmentioning

confidence: 99%

Active Temperature compensation for MEMS capacitive sensor

Do¹,

Seshia²

2021

Preprint

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Temperature variation is one of the most crucial factors that need to be cancelled in MEMS sensors. Many traditional methodologies require an additional circuit to compensate for temperature. This work describes a new active temperature compensation method for MEMS capacitive strain sensor without any additional circuit. The proposed method is based on a complement 2-D capacitive structure design. It consumes zero-power, which is essential toward the realization of a low-power temperature-compensated sensor in battery-powered or energy-harvesting applications<br>

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Low Power Capacitive Ultrasonic Transceiver Array for Airborne Object Detection

Cited by 17 publications

References 7 publications

End-to-End Ultrasonic Hand Gesture Recognition

End-to-End Ultrasonic Hand Gesture Recognition

Active Temperature Compensation for MEMS Capacitive Sensor

Active Temperature compensation for MEMS capacitive sensor

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