Epitaxial manganite freestanding bridges for low power pressure sensors

2015 ◽  
Vol 118 (12) ◽  
pp. 124509 ◽  
Author(s):  
D. Le Bourdais ◽  
G. Agnus ◽  
T. Maroutian ◽  
V. Pillard ◽  
P. Aubert ◽  
...  
Keyword(s):  
Low Power ◽  
Pressure Sensors

scholarly journals Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4539
Author(s):  
Roberto de Fazio ◽  
Elisa Perrone ◽  
Ramiro Velázquez ◽  
Massimo De Vittorio ◽  
Paolo Visconti
Keyword(s):  
Low Power ◽  
Smart Materials ◽  
Applied Pressure ◽  
Pressure Sensors ◽  
Complete Characterization ◽  
Electric Signal ◽  
Sensing Matrix ◽  
Capacitive Accelerometer ◽  
Gait Parameters ◽  
Smart Insole

The evolution of low power electronics and the availability of new smart materials are opening new frontiers to develop wearable systems for medical applications, lifestyle monitoring, and performance detection. This paper presents the development and realization of a novel smart insole for monitoring the plantar pressure distribution and gait parameters; indeed, it includes a piezoresistive sensing matrix based on a Velostat layer for transducing applied pressure into an electric signal. At first, an accurate and complete characterization of Velostat-based pressure sensors is reported as a function of sizes, support material, and pressure trend. The realization and testing of a low-cost and reliable piezoresistive sensing matrix based on a sandwich structure are discussed. This last is interfaced with a low power conditioning and processing section based on an Arduino Lilypad board and an analog multiplexer for acquiring the pressure data. The insole includes a 3-axis capacitive accelerometer for detecting the gait parameters (swing time and stance phase time) featuring the walking. A Bluetooth Low Energy (BLE) 5.0 module is included for transmitting in real-time the acquired data toward a PC, tablet or smartphone, for displaying and processing them using a custom Processing® application. Moreover, the smart insole is equipped with a piezoelectric harvesting section for scavenging energy from walking. The onfield tests indicate that for a walking speed higher than 1 ms−1, the device’s power requirements (i.e., ) was fulfilled. However, more than 9 days of autonomy are guaranteed by the integrated 380-mAh Lipo battery in the total absence of energy contributions from the harvesting section.

Ultra-Miniature and Low-Power Pressure Sensors Using Individual Carbon Nanotubes

2016 ◽  
Vol 25 (3) ◽  
pp. 422-424 ◽  
Author(s):  
T. Suss ◽  
W. Liu ◽  
K. Chikkadi ◽  
C. Roman ◽  
C. Hierold
Keyword(s):  
Carbon Nanotubes ◽  
Low Power ◽  
Pressure Sensors

A Flexible Low-Power 130 nm CMOS Read-Out Circuit With Tunable Sensitivity for Commercial Robotic Resistive Pressure Sensors

2015 ◽  
Vol 15 (11) ◽  
pp. 6650-6658 ◽  
Author(s):  
Alessia Damilano ◽  
Hafiz Muhammad Afzal Hayat ◽  
Alberto Bonanno ◽  
Danilo Demarchi ◽  
Marco Crepaldi
Keyword(s):  
Low Power ◽  
Pressure Sensors

Sandwich structured dielectrics for air-stable and flexible low-voltage organic transistors in ultrasensitive pressure sensing

2020 ◽  
Vol 4 (5) ◽  
pp. 1459-1470 ◽  
Author(s):  
Yue Jiang ◽  
Ziyang Liu ◽  
Zhigang Yin ◽  
Qingdong Zheng
Keyword(s):  
Low Power ◽  
Low Voltage ◽  
Pressure Sensors ◽  
Fast Response ◽  
Organic Transistors ◽  
Pressure Sensing ◽  
Detection Range ◽  
Ultrahigh Sensitivity

A novel type of polymer sandwich dielectric is developed for air-stable, hysteresis-free and flexible OTFTs which can be used for low-power pressure sensors with ultrahigh sensitivity, wide detection range and fast response.

A precision low-power analog front end in 180 nm CMOS for wireless implantable capacitive pressure sensors

Integration ◽  
2020 ◽  
Vol 70 ◽  
pp. 151-158 ◽  
Author(s):  
Chaoping Zhang ◽  
Robert Gallichan ◽  
David M. Budgett ◽  
Daniel McCormick
Keyword(s):  
Low Power ◽  
Pressure Sensors ◽  
Front End ◽  
Analog Front End

scholarly journals Low-Power Embedded System for Gait Classification Using Neural Networks

2020 ◽  
Vol 10 (2) ◽  
pp. 14 ◽  
Author(s):  
Francisco Luna-Perejón ◽  
Manuel Domínguez-Morales ◽  
Daniel Gutiérrez-Galán ◽  
Antón Civit-Balcells
Keyword(s):  
Machine Learning ◽  
Power Consumption ◽  
Embedded System ◽  
Low Power ◽  
Pressure Sensors ◽  
Learning System ◽  
Lower Limbs ◽  
Plantar Pressures ◽  
Learning Classifier ◽  
Static Conditions

Abnormal foot postures can be measured during the march by plantar pressures in both dynamic and static conditions. These detections may prevent possible injuries to the lower limbs like fractures, ankle sprain or plantar fasciitis. This information can be obtained by an embedded instrumented insole with pressure sensors and a low-power microcontroller. However, these sensors are placed in sparse locations inside the insole, so it is not easy to correlate manually its values with the gait type; that is why a machine learning system is needed. In this work, we analyse the feasibility of integrating a machine learning classifier inside a low-power embedded system in order to obtain information from the user’s gait in real-time and prevent future injuries. Moreover, we analyse the execution times, the power consumption and the model effectiveness. The machine learning classifier is trained using an acquired dataset of 3000+ steps from 6 different users. Results prove that this system provides an accuracy over 99% and the power consumption tests obtains a battery autonomy over 25 days.

scholarly journals Design and Optimization of a Low Power Pressure Sensor for Wireless Biomedical Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
J. Sosa ◽  
Juan A. Montiel-Nelson ◽  
R. Pulido ◽  
Jose C. Garcia-Montesdeoca
Keyword(s):  
Blood Pressure ◽  
Power Consumption ◽  
Low Power ◽  
Pressure Sensor ◽  
Biomedical Applications ◽  
Pressure Sensors ◽  
High Sensitivity ◽  
Cmos Technology ◽  
Low Noise ◽  
Least Significant Bit

A blood pressure sensor suitable for wireless biomedical applications is designed and optimized. State-of-the-art blood pressure sensors based on piezoresistive transducers in a full Wheatstone bridge configuration use low ohmic values because of relatively high sensitivity and low noise approach resulting in high power consumption. In this paper, the piezoresistance values are increased in order to reduce by one order of magnitude the power consumption in comparison with literature approaches. The microelectromechanical system (MEMS) pressure sensor, the mixed signal circuits signal conditioning circuitry, and the successive approximation register (SAR) analog-to-digital converter (ADC) are designed, optimized, and integrated in the same substrate using a commercial 1 μm CMOS technology. As result of the optimization, we obtained a digital sensor with high sensitivity, low noise (0.002 μV/Hz), and low power consumption (358 μW). Finally, the piezoresistance noise does not affect the pressure sensor application since its value is lower than half least significant bit (LSB) of the ADC.

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