A fiber Bragg based semi distributed pressure sensor system for in-vivo vascular applications

Hard-shell thoracolumbar sacral orthoses (TLSOs) are used for treating idiopathic scoliosis, a deformation of the spine with a sideways curvature. The pressure required inside the TLSO for ideal corrective results remains unclear. Retrofitting TLSOs with commercially available pressure measurement systems is expensive and can only be performed in a laboratory. The aim of this study was to develop a cost-effective but accurate pressure sensor system for TLSOs. The sensor was built from a piezoresistive polymer, placed between two closed-cell foam liners, and evaluated with a material testing machine. Because foams are energy absorbers, the pressure-conductance curve was affected by hysteresis. The sensor was calibrated on a force plate with the transitions from loading to unloading used to establish the calibration curve. The root mean square error was 12% on average within the required pressure range of 0.01–0.13 MPa. The sensor reacted to the changing pressure during breathing and different activities when tested underneath a chest belt at different tensions. The peak pressure reached 0.135 MPa. The sensor was further tested inside the scoliosis brace during different activities. The measured pressure was 0.014–0.124 MPa. The results from this study enable cheaper and mobile systems to be used for clinical studies on the comfort and pressure of braces during daily activities.

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A Real Time Vehicle Collision Sensor and Reporting System Using Load Sensor, GSM and GPS Technology

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.718-720.1740 ◽

2013 ◽

Vol 718-720 ◽

pp. 1740-1745

Author(s):

Tulu Muluneh Mekonnen ◽

De Ning Jiang ◽

Yong Xin Feng

Keyword(s):

Real Time ◽

Pressure Sensor ◽

Reporting System ◽

Electronic Device ◽

Text Message ◽

Sensor System ◽

Gps Receiver ◽

Gps Technology ◽

Vehicle Collision ◽

Load Sensor

Vehicle collision sensor system and reporting accident to police is an electronic device installed in a vehicle to inform police man in case of accident to track the vehicles location. This system works using pressure sensor, GPS and GSM technology. These technology embedded together to sense the vehicle collision and indicate the position of the vehicle or locate the place of accident in order to solve the problem immediately (as soon as possible).For doing so AT89S52 microcontroller is interfaced serially to a GSM modem, GPS receiver, and pressure sensor. A GSM modem is used to send the position (Latitude and Longitude) of the vehicle, the plate of the vehicle and the SMS text from the accident place. The GPS modem will continuously give the data (longitude and latitude) and Load sensor senses the collision of the vehicle against obstacles and input to microcontroller. As load sensor senses the collision, the GSM start to send the plate of the vehicle, text message and the position of the vehicle in terms of latitude and longitude in real time.

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Ankle Angle Prediction Using a Footwear Pressure Sensor and a Machine Learning Technique

Sensors ◽

10.3390/s21113790 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3790

Author(s):

Zachary Choffin ◽

Nathan Jeong ◽

Michael Callihan ◽

Savannah Olmstead ◽

Edward Sazonov ◽

...

Keyword(s):

Machine Learning ◽

Pressure Sensor ◽

Learning Algorithm ◽

Flexible Substrate ◽

Ankle Injuries ◽

Sensor System ◽

Measurement Unit ◽

K Nearest Neighbor ◽

Machine Learning Technique ◽

Learning Technique

Ankle injuries may adversely increase the risk of injury to the joints of the lower extremity and can lead to various impairments in workplaces. The purpose of this study was to predict the ankle angles by developing a footwear pressure sensor and utilizing a machine learning technique. The footwear sensor was composed of six FSRs (force sensing resistors), a microcontroller and a Bluetooth LE chipset in a flexible substrate. Twenty-six subjects were tested in squat and stoop motions, which are common positions utilized when lifting objects from the floor and pose distinct risks to the lifter. The kNN (k-nearest neighbor) machine learning algorithm was used to create a representative model to predict the ankle angles. For the validation, a commercial IMU (inertial measurement unit) sensor system was used. The results showed that the proposed footwear pressure sensor could predict the ankle angles at more than 93% accuracy for squat and 87% accuracy for stoop motions. This study confirmed that the proposed plantar sensor system is a promising tool for the prediction of ankle angles and thus may be used to prevent potential injuries while lifting objects in workplaces.

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