State-of-the-Art and Future Sensor Technology for Airborne Gravimetry

1995 ◽  
pp. 151-160
Author(s):  
Jürgen Neumeyer ◽  
Klaus Hehl
Keyword(s):  
State Of The Art ◽  
Sensor Technology ◽  
Airborne Gravimetry

scholarly journals Unobtrusive Health Monitoring in Private Spaces: The Smart Home

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 864 ◽  
Author(s):  
Ju Wang ◽  
Nicolai Spicher ◽  
Joana M. Warnecke ◽  
Mostafa Haghi ◽  
Jonas Schwartze ◽  
...  
Keyword(s):  
Literature Review ◽  
Health Monitoring ◽  
Smart Home ◽  
State Of The Art ◽  
Home Health ◽  
Environmental Data ◽  
Behavioral Data ◽  
Sensor Technology ◽  
Current Sensor ◽  
Home Health Monitoring

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking.

Deep Image Demosaicing for Submicron Image Sensors

2019 ◽  
Vol 63 (6) ◽  
pp. 60410-1-60410-12
Author(s):  
Irina Kim ◽  
Seongwook Song ◽  
Soonkeun Chang ◽  
Sukhwan Lim ◽  
Kai Guo
Keyword(s):  
Image Quality ◽  
Mobile Devices ◽  
State Of The Art ◽  
Image Sensor ◽  
Sensor Technology ◽  
Color Filter ◽  
Computationally Efficient ◽  
Linear Feature ◽  
Visual Artifacts ◽  
Very High

Abstract Latest trend in image sensor technology allowing submicron pixel size for high-end mobile devices comes at very high image resolutions and with irregularly sampled Quad Bayer color filter array (CFA). Sustaining image quality becomes a challenge for the image signal processor (ISP), namely for demosaicing. Inspired by the success of deep learning approach to standard Bayer demosaicing, we aim to investigate how artifacts-prone Quad Bayer array can benefit from it. We found that deeper networks are capable to improve image quality and reduce artifacts; however, deeper networks can be hardly deployed on mobile devices given very high image resolutions: 24MP, 36MP, 48MP. In this article, we propose an efficient end-to-end solution to bridge this gap—a duplex pyramid network (DPN). Deep hierarchical structure, residual learning, and linear feature map depth growth allow very large receptive field, yielding better details restoration and artifacts reduction, while staying computationally efficient. Experiments show that the proposed network outperforms state of the art for standard and Quad Bayer demosaicing. For the challenging Quad Bayer CFA, the proposed method reduces visual artifacts better than state-of-the-art deep networks including artifacts existing in conventional commercial solutions. While superior in image quality, it is 2‐25 times faster than state-of-the-art deep neural networks and therefore feasible for deployment on mobile devices, paving the way for a new era of on-device deep ISPs.

scholarly journals Introduction to the Special Issue on “State-of-the-Art Sensor Technology in Japan 2015”

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1350
Author(s):  
Masahiro Tokumitsu ◽  
Yoshiteru Ishida
Keyword(s):  
State Of The Art ◽  
Sensor Technology ◽  
Special Issue

Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning (Preprint)

2021 ◽  
Author(s):  
Oliver Lindhiem ◽  
Mayank Goel ◽  
Sam Shaaban ◽  
Kristie Mak ◽  
Prerna Chikersal ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Mobile Sensing ◽  
Objective Measurement ◽  
Sensor Technology ◽  
School Age Children ◽  
Clinical Settings ◽  
Wearable Sensor ◽  
Core Symptom ◽  
Promising Avenue

UNSTRUCTURED Although hyperactivity is a core symptom of ADHD, there are no objective measures that are widely used in clinical settings. We describe the development of a smartwatch application to measure hyperactivity in school-age children. The LemurDx prototype is a software system for smartwatches that uses wearable sensor technology and machine learning (ML) to measure hyperactivity, with the goal of differentiating children with ADHD combined presentation or predominantly hyperactive/impulsive presentation from children with typical levels of activity. In this pilot study, we recruited 30 children (ages 6-11) to wear the smartwatch with the LemurDx app for two days. Parents also provided activity labels for 30-minute intervals to help train the algorithm. Half the sample had ADHD combined presentation or predominantly hyperactive/impulsive presentation (n = 15) and half were healthy controls (n = 15). Results indicated high usability scores and an overall diagnostic accuracy of .89 (sensitivity = .93; specificity = .86) when the motion sensor output was paired with the activity labels, suggesting that state-of-the-art sensors and ML may provide a promising avenue for the objective measurement of hyperactivity.

scholarly journals Assistive technology solutions for aiding travel of pedestrians with visual impairment

2017 ◽  
Vol 4 ◽  
pp. 205566831772599 ◽  
Author(s):  
Piyush Chanana ◽  
Rohan Paul ◽  
M Balakrishnan ◽  
PVM Rao
Keyword(s):  
Assistive Technology ◽  
Visual Impairment ◽  
State Of The Art ◽  
Sensor Technology ◽  
Independent Mobility ◽  
The Real ◽  
Surrounding Environment ◽  
Processing Techniques ◽  
Real Challenge ◽  
Specific Part

This work systematically reviews the assistive technology solutions for pedestrians with visual impairment and reveals that most of the existing solutions address a specific part of the travel problem. Technology-centered approach with limited focus on the user needs is one of the major concerns in the design of most of the systems. State-of-the-art sensor technology and processing techniques are being used to capture details of the surrounding environment. The real challenge is in conveying this information in a simplified and understandable form especially when the alternate senses of hearing, touch, and smell have much lesser perception bandwidth than that of vision. A lot of systems are at prototyping stages and need to be evaluated and validated by the real users. Conveying the required information promptly through the preferred interface to ensure safety, orientation, and independent mobility is still an unresolved problem. Based on observations and detailed review of available literature, the authors proposed that holistic solutions need to be developed with the close involvement of users from the initial to the final validation stages. Analysis reveals that several factors need serious consideration in the design of such assistive technology solutions.

scholarly journals Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

2019 ◽  
Vol 10 (1) ◽  
pp. 234 ◽  
Author(s):  
Steven Díaz ◽  
Jeannie B. Stephenson ◽  
Miguel A. Labrador
Keyword(s):  
Range Of Motion ◽  
Neurological Disorders ◽  
Functional Outcomes ◽  
Quantitative Methods ◽  
Physical Therapists ◽  
State Of The Art ◽  
Sensor Technology ◽  
Future Research ◽  
Wearable Sensor ◽  
Rehabilitation Assessment

More than 8.6 million people suffer from neurological disorders that affect their gait and balance. Physical therapists provide interventions to improve patient’s functional outcomes, yet balance and gait are often evaluated in a subjective and observational manner. The use of quantitative methods allows for assessment and tracking of patient progress during and after rehabilitation or for early diagnosis of movement disorders. This paper surveys the state-of-the-art in wearable sensor technology in gait, balance, and range of motion research. It serves as a point of reference for future research, describing current solutions and challenges in the field. A two-level taxonomy of rehabilitation assessment is introduced with evaluation metrics and common algorithms utilized in wearable sensor systems.

scholarly journals Introduction to the Special Issue on “State-of-the-Art Sensor Technology in Japan 2012”

Sensors ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 11045-11048 ◽  
Author(s):  
Kouji Harada ◽  
Yoshiteru Ishida
Keyword(s):  
State Of The Art ◽  
Sensor Technology ◽  
Special Issue

Recent developments in sensing methods for eutrophying nutrients with a focus on automation for environmental applications

The Analyst ◽  
2017 ◽  
Vol 142 (23) ◽  
pp. 4355-4372 ◽  
Author(s):  
G. Duffy ◽  
F. Regan
Keyword(s):  
State Of The Art ◽  
Environmental Applications ◽  
Sensor Technology ◽  
Standard Analysis ◽  
Comprehensive Review ◽  
Analysis Methods ◽  
Autonomous Sensors ◽  
Recent Developments

A comprehensive review focusing on eutrophying nutrient monitoring using autonomous sensors, including novel analysis methods, standard analysis methods and state-of-the-art sensor technology.

Sign in / Sign up

Export Citation Format

Share Document