scholarly journals Development of Invisible Sensors and a Machine-Learning-Based Recognition System Used for Early Prediction of Discontinuous Bed-Leaving Behavior Patterns

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1415
Author(s):  
Hirokazu Madokoro ◽  
Kazuhisa Nakasho ◽  
Nobuhiro Shimoi ◽  
Hanwool Woo ◽  
Kazuhito Sato
Keyword(s):  
Machine Learning ◽  
Recognition Accuracy ◽  
Behavior Pattern ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Load Test ◽  
Sensor System ◽  
Behavior Patterns ◽  
Benchmark Datasets ◽  
Equivalent Load

This paper presents a novel bed-leaving sensor system for real-time recognition of bed-leaving behavior patterns. The proposed system comprises five pad sensors installed on a bed, a rail sensor inserted in a safety rail, and a behavior pattern recognizer based on machine learning. The linear characteristic between loads and output was obtained from a load test to evaluate sensor output characteristics. Moreover, the output values change linearly concomitantly with speed to attain the sensor with the equivalent load. We obtained benchmark datasets of continuous and discontinuous behavior patterns from ten subjects. Recognition targets using our sensor prototype and their monitoring system comprise five behavior patterns: sleeping, longitudinal sitting, lateral sitting, terminal sitting, and leaving the bed. We compared machine learning algorithms of five types to recognize five behavior patterns. The experimentally obtained results revealed that the proposed sensor system improved recognition accuracy for both datasets. Moreover, we achieved improved recognition accuracy after integration of learning datasets as a general discriminator.

scholarly journals Feasibility of Using Floor Vibration to Detect Human Falls

2020 ◽  
Vol 18 (1) ◽  
pp. 200
Author(s):  
Yu Shao ◽  
Xinyue Wang ◽  
Wenjie Song ◽  
Sobia Ilyas ◽  
Haibo Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Nearest Neighbor ◽  
Body Movement ◽  
Modern Society ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Study Results ◽  
Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

scholarly journals Pervasive Lying Posture Tracking

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5953 ◽  
Author(s):  
Parastoo Alinia ◽  
Ali Samadani ◽  
Mladen Milosevic ◽  
Hassan Ghasemzadeh ◽  
Saman Parvaneh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computational Models ◽  
Learning Algorithms ◽  
Pressure Sensors ◽  
Machine Learning Algorithms ◽  
Sensor System ◽  
Accurate Detection ◽  
Research Questions ◽  
Posture Tracking

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

scholarly journals Toward Palmprint Recognition Methodology Based Machine Learning Techniques

2020 ◽  
Vol 4 (4) ◽  
Author(s):  
M. M. Ata ◽  
K. M. Elgamily ◽  
M. A. Mohamed
Keyword(s):  
Machine Learning ◽  
Hough Transform ◽  
Recognition Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Palmprint Recognition ◽  
Feature Vectors ◽  
Learning Techniques

The presented paper proposes an algorithm for palmprint recognition using seven different machine learning algorithms. First of all, we have proposed a region of interest (ROI) extraction methodology which is a two key points technique. Secondly, we have performed some image enhancement techniques such as edge detection and morphological operations in order to make the ROI image more suitable for the Hough transform. In addition, we have applied the Hough transform in order to extract all the possible principle lines on the ROI images. We have extracted the most salient morphological features of those lines; slope and length. Furthermore, we have applied the invariant moments algorithm in order to produce 7 appropriate hues of interest. Finally, after performing a complete hybrid feature vectors, we have applied different machine learning algorithms in order to recognize palmprints effectively. Recognition accuracy have been tested by calculating precision, sensitivity, specificity, accuracy, dice, Jaccard coefficients, correlation coefficients, and training time. Seven different supervised machine learning algorithms have been implemented and utilized. The effect of forming the proposed hybrid feature vectors between Hough transform and Invariant moment have been utilized and tested. Experimental results show that the feed forward neural network with back propagation has achieved about 99.99% recognition accuracy among all tested machine learning techniques.

scholarly journals AI-based targeted advertising system

2019 ◽  
Vol 13 (2) ◽  
pp. 787
Author(s):  
Tew Jia Yu ◽  
Chin Poo Lee ◽  
Kian Ming Lim ◽  
Siti Fatimah Abdul Razak
Keyword(s):  
Machine Learning ◽  
Recognition Accuracy ◽  
Facial Recognition ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Outdoor Advertising ◽  
Advertising Effect ◽  
Targeted Advertising ◽  
Vehicle Recognition ◽  
Gender And Age

<span>The most common technology used in targeted advertising is facial recognition and vehicle recognition. Even though there are existing systems serving for the targeting purposes, most propose limited functionalities and the system performance is normally unknown. This paper presents an intelligent targeted advertising system with multiple functionalities, namely facial recognition for gender and age, vehicle recognition, and multiple object detection. The main purpose is to improve the effectiveness of outdoor advertising through biometrics approaches and machine learning technology. Machine learning algorithms are implemented for higher recognition accuracy and hence achieved better targeted advertising effect.</span>

Automatic Speaker Recognition System based on Optimised Machine Learning Algorithms

2019 ◽  
Author(s):  
Tumisho Billson Mokgonyane ◽  
Tshephisho Joseph Sefara ◽  
Thipe Isaiah Modipa ◽  
Madimetja Jonas Manamela
Keyword(s):  
Machine Learning ◽  
Speaker Recognition ◽  
Learning Algorithms ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Automatic Speaker Recognition

scholarly journals Employing Divergent Machine Learning Classifiers to Upgrade the Preciseness of Image Retrieval Systems

2020 ◽  
Vol 20 (3) ◽  
pp. 75-85
Author(s):  
Shefali Dhingra ◽  
Poonam Bansal
Keyword(s):  
Machine Learning ◽  
Image Retrieval ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Visual Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Retrieval Systems ◽  
Retrieval Efficiency ◽  
Benchmark Datasets

AbstractContent Based Image Retrieval (CBIR) system is an efficient search engine which has the potentiality of retrieving the images from huge repositories by extracting the visual features. It includes color, texture and shape. Texture is the most eminent feature among all. This investigation focuses upon the classification complications that crop up in case of big datasets. In this, texture techniques are explored with machine learning algorithms in order to increase the retrieval efficiency. We have tested our system on three texture techniques using various classifiers which are Support vector machine, K-Nearest Neighbor (KNN), Naïve Bayes and Decision Tree (DT). Variant evaluation metrics precision, recall, false alarm rate, accuracy etc. are figured out to measure the competence of the designed CBIR system on two benchmark datasets, i.e. Wang and Brodatz. Result shows that with both these datasets the KNN and DT classifier hand over superior results as compared to others.

scholarly journals Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 313 ◽  
Author(s):  
Pengbo Gao ◽  
Yan Zhang ◽  
Linhuan Zhang ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Machine Learning ◽  
Field Experiments ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Learning System ◽  
Computational Time ◽  
Learning Approaches ◽  
Online Systems ◽  
Online Recognition ◽  
Spray System

Unmanned aerial vehicle (UAV)-based spraying systems have recently become important for the precision application of pesticides, using machine learning approaches. Therefore, the objective of this research was to develop a machine learning system that has the advantages of high computational speed and good accuracy for recognizing spray and non-spray areas for UAV-based sprayers. A machine learning system was developed by using the mutual subspace method (MSM) for images collected from a UAV. Two target lands: agricultural croplands and orchard areas, were considered in building two classifiers for distinguishing spray and non-spray areas. The field experiments were conducted in target areas to train and test the system by using a commercial UAV (DJI Phantom 3 Pro) with an onboard 4K camera. The images were collected from low (5 m) and high (15 m) altitudes for croplands and orchards, respectively. The recognition system was divided into offline and online systems. In the offline recognition system, 74.4% accuracy was obtained for the classifiers in recognizing spray and non-spray areas for croplands. In the case of orchards, the average classifier recognition accuracy of spray and non-spray areas was 77%. On the other hand, the online recognition system performance had an average accuracy of 65.1% for croplands, and 75.1% for orchards. The computational time for the online recognition system was minimal, with an average of 0.0031 s for classifier recognition. The developed machine learning system had an average recognition accuracy of 70%, which can be implemented in an autonomous UAV spray system for recognizing spray and non-spray areas for real-time applications.

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