scholarly journals A layered, any time approach to sensor validation

1997 ◽  
pp. 336-349 ◽  
Author(s):  
Pablo H. Ibargüengoytia ◽  
Sunil Vadera ◽  
L. Enrique Sucar
Keyword(s):  
Sensor Validation

scholarly journals Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3139
Author(s):  
Julian Varghese ◽  
Catharina Marie van Alen ◽  
Michael Fujarski ◽  
Georg Stefan Schlake ◽  
Julitta Sucker ◽  
...  
Keyword(s):  
Low Noise ◽  
Disease Classification ◽  
Multilayer Perceptrons ◽  
Sensor Validation ◽  
Diagnostic Potential ◽  
A Prospective Study ◽  
Learning Architectures ◽  
Diagnosis Classification ◽  
Healthy Participants ◽  
Digital Biomarkers

Smartwatches provide technology-based assessments in Parkinson’s Disease (PD). It is necessary to evaluate their reliability and accuracy in order to include those devices in an assessment. We present unique results for sensor validation and disease classification via machine learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches throughout a 15-min examination. Symptoms and medical history were captured on the paired smartphone. The amplitude error of both smartwatches reaches up to 0.005 g, and for the measured frequencies, up to 0.01 Hz. A broad range of different ML classifiers were cross-validated. The most advanced task of distinguishing PD vs. DD was evaluated with 74.1% balanced accuracy, 86.5% precision and 90.5% recall by Multilayer Perceptrons. Deep-learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle tremor signs with low noise. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers, but it remains challenging for distinguishing similar disorders.

Intelligent sensor validation for sustainable influent quality monitoring in wastewater treatment plants using stacked denoising autoencoders

2021 ◽  
Vol 43 ◽  
pp. 102206
Author(s):  
Abdulrahman H. Ba-Alawi ◽  
Paulina Vilela ◽  
Jorge Loy-Benitez ◽  
SungKu Heo ◽  
ChangKyoo Yoo
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Quality Monitoring ◽  
Intelligent Sensor ◽  
Sensor Validation

scholarly journals Ship-based measurements for infrared sensor validation during Aerosol and Ocean Science Expedition 2004

2006 ◽  
Vol 111 (D9) ◽  
Author(s):  
Nicholas R. Nalli ◽  
Pablo Clemente-Colón ◽  
Peter J. Minnett ◽  
Malgorzata Szczodrak ◽  
Vernon Morris ◽  
...  
Keyword(s):  
Infrared Sensor ◽  
Ocean Science ◽  
Sensor Validation

scholarly journals Sensor Validation for Indoor Air Quality using Machine Learning

2020 ◽  
Author(s):  
Vagner Seibert ◽  
Ricardo Araújo ◽  
Richard McElligott
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Nearest Neighbor ◽  
Contextual Information ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Sensor Validation ◽  
Single Reading

To guarantee a high indoor air quality is an increasingly important task. Sensors measure pollutants in the air and allow for monitoring and controlling air quality. However, all sensors are susceptible to failures, either permanent or transitory, that can yield incorrect readings. Automatically detecting such faulty readings is therefore crucial to guarantee sensors' reliability. In this paper we evaluate three Machine Learning algorithms applied to the task of classifying a single reading from a sensor as faulty or not, comparing them to standard statistical approaches. We show that all tested machine learning methods -- Multi-layer Perceptron, K-Nearest Neighbor and Random Forest -- outperform their statistical counterparts, both by allowing better separation boundaries and by allowing for the use of contextual information. We further show that this result does not depend on the amount of data, but ML methods are able to continue to improve as more data is made available.

Fetal heart rate monitoring device using condenser microphone sensor: Validation and comparison to standard devices

2018 ◽  
Vol 26 (4) ◽  
pp. 573-579 ◽  
Author(s):  
Husna Azyan Binti Ahmad ◽  
Ismail M. El-Badawy ◽  
Om Prakash Singh ◽  
Rozana Binti Hisham ◽  
M.B. Malarvili
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Monitoring Device ◽  
Heart Rate Monitoring ◽  
Fetal Heart Rate Monitoring ◽  
Sensor Validation ◽  
Condenser Microphone

Intelligent Bed Sheet Detects Respiratory Patterns

2018 ◽  
Vol 62 (1) ◽  
pp. 1177-1181 ◽  
Author(s):  
Laura Nicholson ◽  
Olivia Lin ◽  
Edward Shim
Keyword(s):  
Continuous Monitoring ◽  
Resource Constraints ◽  
New Technology ◽  
Vital Signs ◽  
The Body ◽  
Second Phase ◽  
Age In Place ◽  
Sensor Validation ◽  
Health Canada ◽  
Respiratory Conditions

A new technology using an intelligent bed sheet made of fabric sensors is described as a novel advancement that supports wireless and continuous monitoring of vital signs without requiring wire attachments to the body. The intelligent bed sheet developed by Studio 1 Labs Inc. (Studio 1 Labs), can be used to support three distinct groups: i) healthcare institutions with human resource constraints, ii) caregivers who provide care for seniors, infants and children at home, and iii) independent seniors who prefer to age in place. This article describes two complementary research phases using the intelligent bed sheet to detect heart rate, respiratory rate, and respiratory effort. The first phase explores sensor validation from the intelligent bed sheet with preset respiratory conditions from high technology mannequins. The second phase involves a use case with healthy young adults comparing between physiological signals from the bed sheet with standard nursing protocols of manual counts and a pulse oximeter approved by Health Canada.

scholarly journals Heart rate sensor validation and seasonal and diurnal variation of body temperature and heart rate in domestic sheep

2019 ◽  
Vol 8 ◽  
pp. 100075 ◽  
Author(s):  
Boris Fuchs ◽  
Kristin Marie Sørheim ◽  
Matteo Chincarini ◽  
Emma Brunberg ◽  
Solveig Marie Stubsjøen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Body Temperature ◽  
Diurnal Variation ◽  
Domestic Sheep ◽  
Sensor Validation ◽  
Seasonal And Diurnal Variation ◽  
Rate Sensor

Online sensor validation in sensor networks for bioprocess monitoring using swarm intelligence

2019 ◽  
Vol 412 (9) ◽  
pp. 2165-2175
Author(s):  
Vincent Brunner ◽  
Lukas Klöckner ◽  
Roland Kerpes ◽  
Dominik Ulrich Geier ◽  
Thomas Becker
Keyword(s):  
Sensor Networks ◽  
Swarm Intelligence ◽  
Bioprocess Monitoring ◽  
Sensor Validation ◽  
Online Sensor
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