scholarly journals LifeChair: A Conductive Fabric Sensor-Based Smart Cushion for Actively Shaping Sitting Posture

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2261 ◽  
Author(s):  
Karlos Ishac ◽  
Kenji Suzuki
Keyword(s):  
Detection Accuracy ◽  
Upright Posture ◽  
Performance Study ◽  
Vibrotactile Feedback ◽  
Pressure Sensing ◽  
Sitting Posture ◽  
Conductive Fabric ◽  
The Past ◽  
Sensing System ◽  
Average Accuracy

The LifeChair is a smart cushion that provides vibrotactile feedback by actively sensing and classifying sitting postures to encourage upright posture and reduce slouching. The key component of the LifeChair is our novel conductive fabric pressure sensing array. Fabric sensors have been explored in the past, but a full sensing solution for embedded real world use has not been proposed. We have designed our system with commercial use in mind, and as a result, it has a high focus on manufacturability, cost-effectiveness and adaptiveness. We demonstrate the performance of our fabric sensing system by installing it into the LifeChair and comparing its posture detection accuracy with our previous study that implemented a conventional flexible printed PCB-sensing system. In this study, it is shown that the LifeChair can detect all 11 postures across 20 participants with an improved average accuracy of 98.1%, and it demonstrates significantly lower variance when interfacing with different users. We also conduct a performance study with 10 participants to evaluate the effectiveness of the LifeChair device in improving upright posture and reducing slouching. Our performance study demonstrates that the LifeChair is effective in encouraging users to sit upright with an increase of 68.1% in time spent seated upright when vibrotactile feedback is activated.

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

scholarly journals Entropy Based Features Distribution for Anti-DDoS Model in SDN

2021 ◽  
Vol 13 (3) ◽  
pp. 1522
Author(s):  
Raja Majid Ali Ujjan ◽  
Zeeshan Pervez ◽  
Keshav Dahal ◽  
Wajahat Ali Khan ◽  
Asad Masood Khattak ◽  
...  
Keyword(s):  
Network Security ◽  
False Positive ◽  
Denial Of Service ◽  
Network Services ◽  
Detection Accuracy ◽  
Data Sets ◽  
Traffic Patterns ◽  
Average Accuracy ◽  
Ddos Detection ◽  
Quantitative Results

In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%.

scholarly journals Novel Application of a Multiscale Entropy Index as a Sensitive Tool for Detecting Subtle Vascular Abnormalities in the Aged and Diabetic

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Hsien-Tsai Wu ◽  
Men-Tzung Lo ◽  
Guan-Hong Chen ◽  
Cheuk-Kwan Sun ◽  
Jian-Jung Chen
Keyword(s):  
Glycosylated Hemoglobin ◽  
Reactive Hyperemia ◽  
Multiscale Entropy ◽  
Arterial Pulse ◽  
Vascular Changes ◽  
Entropy Index ◽  
Pressure Sensing ◽  
Vascular Abnormalities ◽  
Sensing System ◽  
Sensitive Tool

Although previous studies have shown the successful use of pressure-induced reactive hyperemia as a tool for the assessment of endothelial function, its sensitivity remains questionable. This study aims to investigate the feasibility and sensitivity of a novel multiscale entropy index (MEI) in detecting subtle vascular abnormalities in healthy and diabetic subjects. Basic anthropometric and hemodynamic parameters, serum lipid profiles, and glycosylated hemoglobin levels were recorded. Arterial pulse wave signals were acquired from the wrist with an air pressure sensing system (APSS), followed by MEI and dilatation index (DI) analyses. MEI succeeded in detecting significant differences among the four groups of subjects: healthy young individuals, healthy middle-aged or elderly individuals, well-controlled diabetic individuals, and poorly controlled diabetic individuals. A reduction in multiscale entropy reflected age- and diabetes-related vascular changes and may serve as a more sensitive indicator of subtle vascular abnormalities compared with DI in the setting of diabetes.

scholarly journals Pendeteksian Septoria pada Tanaman Tomat dengan Metode Deep Learning berbasis Raspberry Pi

2021 ◽  
Vol 5 (1) ◽  
pp. 107-113
Author(s):  
Kahlil Muchtar ◽  
Chairuman ◽  
Yudha Nurdin ◽  
Afdhal Afdhal
Keyword(s):  
Deep Learning ◽  
Leaf Spot ◽  
Raspberry Pi ◽  
Leaf Spot Disease ◽  
Detection Accuracy ◽  
Crop Failure ◽  
Tomato Production ◽  
Spot Disease ◽  
Average Accuracy ◽  
Septoria Leaf Spot

much needed to meet the needs of both industry and households. However, tomato plants still require serious handling in increasing the yields. Data from the Central Bureau of Statistics shows that the number of tomatoes produced is not in accordance with a large number of market demands, resulting from the decrease of tomato yields. One of the obstacles in increasing tomato production is that the crops are attacked by septoria leaf spot disease due to the fungus or the fungus Septoria Lycopersici Speg. Most farmers have limited knowledge of the early symptoms, which are not obvious, and also facing difficulty in detecting this disease earlier. The problem has been causing disadvantages such as crop failure or plant death. Based on this problem, a study will be conducted with the aim of designing a tool that can be used to detect septoria leaf spot disease based on deep learning using the Convolutional Neural Network (ConvNets or CNN) model, where an algorithm that resembles human nerves is one of the supervised learning and widely used for solving linear and non-linear problems. In addition, the researcher used the Raspberry Pi as a microcontroller and used the Intel Movidius Neural Computing Stick (NCS) which functions to speed up the computing process so that the detection process is easier because of its portable, fast and accurate nature. The average accuracy rate is 95.89% with detection accuracy between 84.22% to 100%.  

Implementation of Distributed Polarization Maintaining Fiber Polarization Coupling Pressure Sensing System

2010 ◽  
Vol 37 (6) ◽  
pp. 1467-1472 ◽  
Author(s):  
陈信伟 Chen Xinwei ◽  
张红霞 Zhang Hongxia ◽  
贾大功 Jia Dagong ◽  
刘铁根 Liu Tiegen ◽  
张以谟 Zhang Yimo
Keyword(s):  
Pressure Sensing ◽  
Sensing System ◽  
Polarization Maintaining Fiber ◽  
Polarization Maintaining

scholarly journals Spectra of Southern Radio Sources

1977 ◽  
Vol 74 ◽  
pp. 85-97 ◽  
Author(s):  
J. G. Bolton
Keyword(s):  
Time Scale ◽  
Galactic Plane ◽  
Complete Sample ◽  
High Frequencies ◽  
The Past ◽  
Average Accuracy ◽  
Sky Survey ◽  
Two Factors ◽  
Optical Identifications ◽  
One Year

Surveys of the sky between declinations +25° and −90° at 2700 MHz (11 cm) have been in progress for the past 10 years. Excluding some regions close to the galactic plane the whole sky south of +25° has been surveyed to a flux density limit of 0.6 Jy at 2700 MHz and within this area surveys to limits of 0.35, 0.25 or 0.1 Jy have been made covering 3.5 sr. Flux densities have been measured at 5000 MHz for all sources stronger than 0.35 Jy at 2700 MHz. The source positions have an average accuracy of 10″ arc in both coordinates and the positions have been examined for optical identifications on Palomar, ESO or SRC sky survey plates, which now cover 95% of the area. The first part of this paper concerns the relationships between the spectral indices α(2700 to 5000 MHz) and the identifications of the 2300 sources with galactic latitudes greater than 10°. It is a statistically significant sample, since the sources stronger than 0.35 Jy cover 3.5 sr. It is also a representative sample, since no selection was made on the basis of spectral index or identification. It cannot however be claimed as a complete sample, for two reasons. A substantial fraction of sources found in radio surveys at high frequencies are variable - variations of up to a factor of three can occur on a time scale of a year - thus the various sections of the survey are complete only for the relevant epoch. Many of their optical counterparts are also variables - variations of up to a factor of 100 can occur on a time scale of one year. It is hoped to make some assessment of the effect of these two factors in the next two years, when second-epoch Parkes surveys will begin and SRC Schmidt plates will overlap the Palomar Sky Survey.

Randomized Hough Transform

2011 ◽  
pp. 1343-1350 ◽  
Author(s):  
Lei Xu ◽  
Erkki Oja
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Hough Transform ◽  
Random Sampling ◽  
Computational Cost ◽  
Detection Accuracy ◽  
The Past ◽  
Randomized Hough Transform ◽  
Dynamic Storage ◽  
High Computational Cost

Proposed in 1962, the Hough transform (HT) has been widely applied and investigated for detecting curves, shapes, and motions in the fields of image processing and computer vision. However, the HT has several shortcomings, including high computational cost, low detection accuracy, vulnerability to noise, and possibility of missing objects. Many efforts target at solving some of the problems for decades, while the key idea remains more or less the same. Proposed in 1989 and further developed thereafter, the Randomized Hough Transform (RHT) manages to considerably overcome these shortcomings via innovations on the fundamental mechanisms, with random sampling in place of pixel scanning, converging mapping in place of diverging mapping, and dynamic storage in place of accumulation array. This article will provides an overview on advances and applications of RHT in the past one and half decades.

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