scholarly journals An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts From Non-Intrusive Physiological Signals Inreal-World Situations

2021 ◽  
Author(s):  
Ana Serrano-Mamolar ◽  
Miguel Arevalillo-Herráez ◽  
Jesus G. Boticario
Keyword(s):  
Real World ◽  
Adaptive Learning ◽  
Markov Models ◽  
Low Cost ◽  
High Accuracy ◽  
Physiological Signals ◽  
Detection Accuracy ◽  
Practical Applications ◽  
Related Information ◽  
Wide Range

Emotion recognition is becoming very relevant in educational scenarios, since previous research has proven the strong influence of emotions on the student's engagement and motivation. There is no standard method for stating student's affect, but physiological signals have been widely used in educational contexts. Physiological signals have been proved to offer high accuracy in detecting emotions because they reflect spontaneous affect-related information, and which is fresh and do not require an additional control or interpretation. However, most proposed works use measuring equipment that limit its applicability in real-world scenarios because of their high cost and their intrusiveness. Expensive material means an economic challenge for schools and reduce the scalability of the experiments. Intrusive equipment can be uncomfortable for the students which can lead to errors in the collected data. In this work, we analyse the feasibility of developing a low-cost non-intrusive device that integrates easy-to-capture signals that guarantee high detection accuracy. The advantage of the approach also lies in using user’s centred information sources (intra-subject) in real-world situations, which provide better detection accuracy, by offering models adapted to each subject. To this end, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We study the multi-fusion of every possible combination of these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. Results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task. This work concludes that the implementation of a low-cost wrist-worn device for recognising relevant emotions from each student is possible and open the way to a wide range of practical applications in the context of adaptive learning systems.

scholarly journals An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1777
Author(s):  
Ana Serrano-Mamolar ◽  
Miguel Arevalillo-Herráez ◽  
Guillermo Chicote-Huete ◽  
Jesus G. G. Boticario
Keyword(s):  
Real World ◽  
Markov Models ◽  
Low Cost ◽  
High Accuracy ◽  
Physiological Signals ◽  
Detection Accuracy ◽  
Related Information ◽  
Additional Control ◽  
Affect Detection ◽  
Potential Use

Previous research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.

scholarly journals Utilization of Food Processing By-products in Extrusion Processing: A Review

2021 ◽  
Vol 4 ◽  
Author(s):  
Debomitra Dey ◽  
Jana K. Richter ◽  
Pichmony Ek ◽  
Bon-Jae Gu ◽  
Girish M. Ganjyal
Keyword(s):  
Functional Properties ◽  
Low Cost ◽  
Value Added ◽  
Raw Material ◽  
Practical Applications ◽  
Extrusion Processing ◽  
High Production Rate ◽  
Wide Range ◽  
Extruded Products ◽  
By Products

The processing of agricultural products into value-added food products yields numerous by-products or waste streams such as pomace (fruit and vegetable processing), hull/bran (grain milling), meal/cake (oil extraction), bagasse (sugar processing), brewer's spent grain (brewing), cottonseed meal (cotton processing), among others. In the past, significant work in exploring the possibility of the utilization of these by-products has been performed. Most by-products are highly nutritious and can be excellent low-cost sources of dietary fiber, proteins, and bioactive compounds such as polyphenols, antioxidants, and vitamins. The amount of energy utilized for the disposal of these materials is far less than the energy required for the purification of these materials for valorization. Thus, in many cases, these materials go to waste or landfill. Studies have been conducted to incorporate the by-products into different foods in order to promote their utilization and tackle their environmental impacts. Extrusion processing can be an excellent avenue for the utilization of these by-products in foods. Extrusion is a widely used thermo-mechanical process due to its versatility, flexibility, high production rate, low cost, and energy efficiency. Extruded products such as direct-expanded products, breakfast cereals, and pasta have been developed by researchers using agricultural by-products. The different by-products have a wide range of characteristics in terms of chemical composition and functional properties, affecting the final products in extrusion processing. For the practical applications of these by-products in extrusion, it is crucial to understand their impacts on the qualities of raw material blends and extruded products. This review summarizes the general differences in the properties of food by-products from different sources (proximate compositions, physicochemical properties, and functional properties) and how these properties and the extrusion processing conditions influence the product characteristics. The discussion of the by-product properties and their impacts on the extrudates and their nutritional profile can be useful for food manufacturers and researchers to expand their applications. The gaps in the literature have been highlighted for further research and better utilization of by-products with extrusion processing.

scholarly journals Direct detection of charge and discharge process in supercapacitor by fiber-optic LSPR sensors

Nanophotonics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 1071-1079 ◽  
Author(s):  
Siyu Qian ◽  
Xinlong Chen ◽  
Shiyu Jiang ◽  
Qiwen Pan ◽  
Yachen Gao ◽  
...  
Keyword(s):  
Fiber Optic ◽  
Direct Detection ◽  
Low Cost ◽  
The State ◽  
State Of Charge ◽  
Localized Surface Plasmon ◽  
High Price ◽  
Discharge Process ◽  
Practical Applications ◽  
Wide Range

AbstractSupercapacitors with high power density, ultralong lifespan and wide range operating temperature have drawn significant attention in recent years. However, monitoring the state of charge in supercapacitors in a cost-effective and flexible way is still challenging. Techniques such as transmission electron microscopy and X-ray diffraction can analyze the characteristics of supercapacitor well. But with large size and high price, they are not suitable for daily monitoring of the supercapacitors’ operation. In this paper, a low cost and easily fabricated fiber-optic localized surface plasmon resonance (LSPR) probe is proposed to monitor the state of charge of the electrode in a supercapacitor. The Au nanoparticles were loading on the fiber core as LSPR sensing region. In order to implant the fiber in the supercapacitor, a reflective type of fiber sensor was used. The results show that this tiny fiber-optic LSPR sensor can provide online monitoring of the state of charge during the charging and discharging process in situ. The intensity shift in LSPR sensor has a good linear relationship with the state of charge calculated by standard galvanostatic charging and discharging test. In addition, this LSPR sensor is insensitive to the temperature change, presenting a great potential in practical applications.

scholarly journals A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3531 ◽  
Author(s):  
Lorenzo Manoni ◽  
Claudio Turchetti ◽  
Laura Falaschetti ◽  
Paolo Crippa
Keyword(s):  
Compressed Sensing ◽  
Comparative Study ◽  
Low Power ◽  
Computational Methods ◽  
Low Cost ◽  
Reconstruction Algorithms ◽  
Practical Applications ◽  
Emg Signal ◽  
Wide Range ◽  
Independent Case

Wearable devices offer a convenient means to monitor biosignals in real time at relatively low cost, and provide continuous monitoring without causing any discomfort. Among signals that contain critical information about human body status, electromyography (EMG) signal is particular useful in monitoring muscle functionality and activity during sport, fitness, or daily life. In particular surface electromyography (sEMG) has proven to be a suitable technique in several health monitoring applications, thanks to its non-invasiveness and ease to use. However, recording EMG signals from multiple channels yields a large amount of data that increases the power consumption of wireless transmission thus reducing the sensor lifetime. Compressed sensing (CS) is a promising data acquisition solution that takes advantage of the signal sparseness in a particular basis to significantly reduce the number of samples needed to reconstruct the signal. As a large variety of algorithms have been developed in recent years with this technique, it is of paramount importance to assess their performance in order to meet the stringent energy constraints imposed in the design of low-power wireless body area networks (WBANs) for sEMG monitoring. The aim of this paper is to present a comprehensive comparative study of computational methods for CS reconstruction of EMG signals, giving some useful guidelines in the design of efficient low-power WBANs. For this purpose, four of the most common reconstruction algorithms used in practical applications have been deeply analyzed and compared both in terms of accuracy and speed, and the sparseness of the signal has been estimated in three different bases. A wide range of experiments are performed on real-world EMG biosignals coming from two different datasets, giving rise to two different independent case studies.

scholarly journals Methods for rapid frequency-domain characterization of leakage currents in silicon nanowire-based field-effect transistors

2014 ◽  
Vol 5 ◽  
pp. 964-972 ◽  
Author(s):  
Tomi Roinila ◽  
Xiao Yu ◽  
Jarmo Verho ◽  
Tie Li ◽  
Pasi Kallio ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Leakage Current ◽  
Field Effect ◽  
Field Effect Transistors ◽  
Low Cost ◽  
Silicon Nanowire ◽  
Binary Sequence ◽  
Practical Applications ◽  
Frequency Domain Methods ◽  
Wide Range

Silicon nanowire-based field-effect transistors (SiNW FETs) have demonstrated the ability of ultrasensitive detection of a wide range of biological and chemical targets. The detection is based on the variation of the conductance of a nanowire channel, which is caused by the target substance. This is seen in the voltage–current behavior between the drain and source. Some current, known as leakage current, flows between the gate and drain, and affects the current between the drain and source. Studies have shown that leakage current is frequency dependent. Measurements of such frequency characteristics can provide valuable tools in validating the functionality of the used transistor. The measurements can also be an advantage in developing new detection technologies utilizing SiNW FETs. The frequency-domain responses can be measured by using a commercial sine-sweep-based network analyzer. However, because the analyzer takes a long time, it effectively prevents the development of most practical applications. Another problem with the method is that in order to produce sinusoids the signal generator has to cope with a large number of signal levels. This may become challenging in developing low-cost applications. This paper presents fast, cost-effective frequency-domain methods with which to obtain the responses within seconds. The inverse-repeat binary sequence (IRS) is applied and the admittance spectroscopy between the drain and source is computed through Fourier methods. The methods is verified by experimental measurements from an n-type SiNW FET.

scholarly journals Hospitals and Laboratories on Paper-Based Sensors: A Mini Review

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 5998
Author(s):  
Huaizu Zhang ◽  
Chengbin Xia ◽  
Guangfu Feng ◽  
Jun Fang
Keyword(s):  
Low Cost ◽  
High Accuracy ◽  
Trace Detection ◽  
Laboratory Equipment ◽  
Optical Signals ◽  
Practical Applications ◽  
Number Of Factors ◽  
Acid Protein ◽  
Inorganic Chemical ◽  
Physical And Chemical

With characters of low cost, portability, easy disposal, and high accuracy, as well as bulky reduced laboratory equipment, paper-based sensors are getting increasing attention for reliable indoor/outdoor onsite detection with nonexpert operation. They have become powerful analysis tools in trace detection with ultra-low detection limits and extremely high accuracy, resulting in their great popularity in medical detection, environmental inspection, and other applications. Herein, we summarize and generalize the recently reported paper-based sensors based on their application for mechanics, biomolecules, food safety, and environmental inspection. Based on the biological, physical, and chemical analytes-sensitive electrical or optical signals, extensive detections of a large number of factors such as humidity, pressure, nucleic acid, protein, sugar, biomarkers, metal ions, and organic/inorganic chemical substances have been reported via paper-based sensors. Challenges faced by the current paper-based sensors from the fundamental problems and practical applications are subsequently analyzed; thus, the future directions of paper-based sensors are specified for their rapid handheld testing.

scholarly journals Development of a camera trap for perching dragonflies: a new tool for freshwater environmental assessment

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9681
Author(s):  
Akira Yoshioka ◽  
Akira Shimizu ◽  
Hiroyuki Oguma ◽  
Nao Kumada ◽  
Keita Fukasawa ◽  
...  
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Low Cost ◽  
Time Lapse ◽  
Camera Trap ◽  
Environmental Indicators ◽  
Low Power Consumption ◽  
Detection Accuracy ◽  
Practical Applications ◽  
Detection Frequency

Although dragonflies are excellent environmental indicators for monitoring terrestrial water ecosystems, automatic monitoring techniques using digital tools are limited. We designed a novel camera trapping system with an original dragonfly detector based on the hypothesis that perching dragonflies can be automatically detected using inexpensive and energy-saving photosensors built in a perch-like structure. A trial version of the camera trap was developed and evaluated in a case study targeting red dragonflies (Sympetrum spp.) in Japan. During an approximately 2-month period, the detector successfully detected Sympetrum dragonflies while using extremely low power consumption (less than 5 mW). Furthermore, a short-term field experiment using time-lapse cameras for validation at three locations indicated that the detection accuracy was sufficient for practical applications. The frequency of false positive detection ranged from 17 to 51 over an approximately 2-day period. The detection sensitivities were 0.67 and 1.0 at two locations, where a time-lapse camera confirmed that Sympetrum dragonflies perched on the trap more than once. However, the correspondence between the detection frequency by the camera trap and the abundance of Sympetrum dragonflies determined by field observations conducted in parallel was low when the dragonfly density was relatively high. Despite the potential for improvements in our camera trap and its application to the quantitative monitoring of dragonflies, the low cost and low power consumption of the detector make it a promising tool.

scholarly journals Using crowdsourced web content for informing water systems operations in snow-dominated catchments

2016 ◽  
Vol 20 (12) ◽  
pp. 5049-5062 ◽  
Author(s):  
Matteo Giuliani ◽  
Andrea Castelletti ◽  
Roman Fedorov ◽  
Piero Fraternali
Keyword(s):  
Low Cost ◽  
Management Problem ◽  
Web Content ◽  
Multiple Sources ◽  
Mountainous Regions ◽  
Related Information ◽  
Snow Covered Area ◽  
Covered Area ◽  
Wide Range ◽  
Web Images

Abstract. Snow is a key component of the hydrologic cycle in many regions of the world. Despite recent advances in environmental monitoring that are making a wide range of data available, continuous snow monitoring systems that can collect data at high spatial and temporal resolution are not well established yet, especially in inaccessible high-latitude or mountainous regions. The unprecedented availability of user-generated data on the web is opening new opportunities for enhancing real-time monitoring and modeling of environmental systems based on data that are public, low-cost, and spatiotemporally dense. In this paper, we contribute a novel crowdsourcing procedure for extracting snow-related information from public web images, either produced by users or generated by touristic webcams. A fully automated process fetches mountain images from multiple sources, identifies the peaks present therein, and estimates virtual snow indexes representing a proxy of the snow-covered area. Our procedure has the potential for complementing traditional snow-related information, minimizing costs and efforts for obtaining the virtual snow indexes and, at the same time, maximizing the portability of the procedure to several locations where such public images are available. The operational value of the obtained virtual snow indexes is assessed for a real-world water-management problem, the regulation of Lake Como, where we use these indexes for informing the daily operations of the lake. Numerical results show that such information is effective in extending the anticipation capacity of the lake operations, ultimately improving the system performance.

scholarly journals Using crowdsourced web content for informing water systems operations in snow-dominated catchments

2016 ◽  
Author(s):  
Matteo Giuliani ◽  
Andrea Castelletti ◽  
Roman Fedorov ◽  
Piero Fraternali
Keyword(s):  
Low Cost ◽  
Management Problem ◽  
Web Content ◽  
Multiple Sources ◽  
Mountainous Regions ◽  
Related Information ◽  
Snow Covered Area ◽  
Covered Area ◽  
Wide Range ◽  
Web Images

Abstract. Snow is a key component of the hydrologic cycle in many regions of the world. Despite recent advances in environmental monitoring are making a wide range of data available, continuous snow monitoring systems able to collect data at high spatial and temporal resolution are not well established yet, especially in inaccessible high latitude or mountainous regions. The unprecedented availability of user generated data on the Web is opening new opportunities for enhancing real-time monitoring and modeling of environmental systems based on data that are public, low-cost, and spatio-temporally dense. In this paper, we contribute a novel crowdsourcing procedure for extracting snow-related information from public web images, either produced by users or generated by touristic web cams. A fully automated process fetches mountain images from multiple sources, identifies the peaks present therein, and estimates virtual snow indexes representing a proxy of the snow covered area. Our procedure has the potential for complementing traditional snow-related information, minimizing costs and efforts for obtaining the virtual snow indexes and, at the same time, maximizing the portability of the procedure to several locations where such public images are available. The operational value of the obtained virtual snow indexes is assessed for a real world water management problem, the regulation of Lake Como, where we use these indexes for informing the daily operations of the lake. Numerical results show that such information is effective in extending the anticipation capacity of the lake operations, ultimately improving the system performance.

scholarly journals Low-Cost Multisensor Integrated System for Online Walking Gait Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lingyun Yan ◽  
Guowu Wei ◽  
Zheqi Hu ◽  
Haohua Xiu ◽  
Yuyang Wei ◽  
...  
Keyword(s):  
Detection System ◽  
Low Cost ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Integrated System ◽  
Heel Strike ◽  
Detection Accuracy ◽  
Time Error ◽  
Kinematic Data ◽  
Compact Size

A three-dimensional motion capture system is a useful tool for analysing gait patterns during walking or exercising, and it is frequently applied in biomechanical studies. However, most of them are expensive. This study designs a low-cost gait detection system with high accuracy and reliability that is an alternative method/equipment in the gait detection field to the most widely used commercial system, the virtual user concept (Vicon) system. The proposed system integrates mass-produced low-cost sensors/chips in a compact size to collect kinematic data. Furthermore, an x86 mini personal computer (PC) running at 100 Hz classifies motion data in real-time. To guarantee gait detection accuracy, the embedded gait detection algorithm adopts a multilayer perceptron (MLP) model and a rule-based calibration filter to classify kinematic data into five distinct gait events: heel-strike, foot-flat, heel-off, toe-off, and initial-swing. To evaluate performance, volunteers are requested to walk on the treadmill at a regular walking speed of 4.2 km/h while kinematic data are recorded by a low-cost system and a Vicon system simultaneously. The gait detection accuracy and relative time error are estimated by comparing the classified gait events in the study with the Vicon system as a reference. The results show that the proposed system obtains a high accuracy of 99.66% with a smaller time error (32 ms), demonstrating that it performs similarly to the Vicon system in the gait detection field.

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