scholarly journals A Proposal of Implementation of Sitting Posture Monitoring System for Wheelchair Utilizing Machine Learning Methods

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6349
Author(s):  
Jawad Ahmad ◽  
Johan Sidén ◽  
Henrik Andersson
Keyword(s):  
Machine Learning ◽  
Pressure Distribution ◽  
Real Time ◽  
Monitoring System ◽  
Pressure Ulcers ◽  
Machine Learning Algorithms ◽  
Raspberry Pi ◽  
Support Vector ◽  
Processing Unit ◽  
Posture Recognition

This paper presents a posture recognition system aimed at detecting sitting postures of a wheelchair user. The main goals of the proposed system are to identify and inform irregular and improper posture to prevent sitting-related health issues such as pressure ulcers, with the potential that it could also be used for individuals without mobility issues. In the proposed monitoring system, an array of 16 screen printed pressure sensor units was employed to obtain pressure data, which are sampled and processed in real-time using read-out electronics. The posture recognition was performed for four sitting positions: right-, left-, forward- and backward leaning based on k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM machine learning algorithms. As a result, a posture classification accuracy of up to 99.03 percent can be achieved. Experimental studies illustrate that the system can provide real-time pressure distribution value in the form of a pressure map on a standard PC and also on a raspberry pi system equipped with a touchscreen monitor. The stored pressure distribution data can later be shared with healthcare professionals so that abnormalities in sitting patterns can be identified by employing a post-processing unit. The proposed system could be used for risk assessments related to pressure ulcers. It may be served as a benchmark by recording and identifying individuals’ sitting patterns and the possibility of being realized as a lightweight portable health monitoring device.

scholarly journals Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4956
Author(s):  
Chew Cheik Goh ◽  
Latifah Munirah Kamarudin ◽  
Ammar Zakaria ◽  
Hiromitsu Nishizaki ◽  
Nuraminah Ramli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Support Vector Regression ◽  
Real Time ◽  
Monitoring System ◽  
Machine Learning Algorithms ◽  
Quality Monitoring ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Air Quality Monitoring

This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.

scholarly journals Classification of Children’s Sitting Postures Using Machine Learning Algorithms

2018 ◽  
Vol 8 (8) ◽  
pp. 1280 ◽  
Author(s):  
Yong Kim ◽  
Youngdoo Son ◽  
Wonjoon Kim ◽  
Byungki Jin ◽  
Myung Yun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Monitoring System ◽  
Multinomial Logistic Regression ◽  
Learning Algorithms ◽  
Feedback System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Future Research ◽  
Support Vector

Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children’s sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children’s chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

scholarly journals Predicting Coronavirus Pandemic in Real-Time Using Machine Learning and Big Data Streaming System

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiongwei Zhang ◽  
Hager Saleh ◽  
Eman M. G. Younis ◽  
Radhya Sahal ◽  
Abdelmgeid A. Ali
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Sentiment Analysis ◽  
Real Time ◽  
Machine Learning Algorithms ◽  
Streaming Data ◽  
Support Vector ◽  
Real Time System ◽  
Online Prediction ◽  
Analysis Prediction

Twitter is a virtual social network where people share their posts and opinions about the current situation, such as the coronavirus pandemic. It is considered the most significant streaming data source for machine learning research in terms of analysis, prediction, knowledge extraction, and opinions. Sentiment analysis is a text analysis method that has gained further significance due to social networks’ emergence. Therefore, this paper introduces a real-time system for sentiment prediction on Twitter streaming data for tweets about the coronavirus pandemic. The proposed system aims to find the optimal machine learning model that obtains the best performance for coronavirus sentiment analysis prediction and then uses it in real-time. The proposed system has been developed into two components: developing an offline sentiment analysis and modeling an online prediction pipeline. The system has two components: the offline and the online components. For the offline component of the system, the historical tweets’ dataset was collected in duration 23/01/2020 and 01/06/2020 and filtered by #COVID-19 and #Coronavirus hashtags. Two feature extraction methods of textual data analysis were used, n-gram and TF-ID, to extract the dataset’s essential features, collected using coronavirus hashtags. Then, five regular machine learning algorithms were performed and compared: decision tree, logistic regression, k-nearest neighbors, random forest, and support vector machine to select the best model for the online prediction component. The online prediction pipeline was developed using Twitter Streaming API, Apache Kafka, and Apache Spark. The experimental results indicate that the RF model using the unigram feature extraction method has achieved the best performance, and it is used for sentiment prediction on Twitter streaming data for coronavirus.

Invisible experience to real-time assessment in elite tennis athlete training: Sport-specific movement classification based on wearable MEMS sensor data

2021 ◽  
pp. 175433712110503
Author(s):  
Mingyue Wu ◽  
Ran Wang ◽  
Yang Hu ◽  
Mengjiao Fan ◽  
Yufan Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Test Accuracy ◽  
Z Score ◽  
Mems Sensor ◽  
Score Normalization

This study examined the reliability of a tennis stroke classification and assessment platform consisting of a single low-cost MEMS sensor in a wrist-worn wearable device, smartphone, and computer. The data that was collected was transmitted via Bluetooth and analyzed by machine learning algorithms. Twelve right-handed male elite tennis athletes participated in the study, and each athlete performed 150 strokes. The results from three machine learning algorithms regarding their recognition and classification of the real-time data stream were compared. Stroke recognition and classification went through pre-processing, segmentation, feature extraction, and classification with Support Vector Machine (SVM), including SVM without normalization, SVM with Min–Max, SVM with Z-score normalization, K-nearest neighbor (K-NN), and Naive Bayes (NB) machine learning algorithms. During the data training process, 10-fold cross-validation was used to avoid overfitting and suitable parameters were found within the SVM classifiers. The best classifier was achieved when C = 1 using the RBF kernel function. Different machine learning algorithms’ classification of unique stroke types yielded highly reliable clusters within each stroke type with the highest test accuracy of 99% achieved by SVM with Min–Max normalization and 98.4% achieved using SVM with a Z-score normalization classifier.

Application of Bayesian Learning Mechanism in Power System Transient Stability Assessment

2010 ◽  
Vol 108-111 ◽  
pp. 765-770
Author(s):  
Lin Niu ◽  
Jian Guo Zhao ◽  
Ke Jun Li ◽  
Zhen Yu Zhou
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Real Time ◽  
Bayesian Learning ◽  
Transient Stability ◽  
Decision Function ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Stability Assessment

One of the most challenging problems in real-time operation of power system is the prediction of transient stability. Fast and accurate techniques are imperative to achieve on-line transient stability assessment (TSA). This problem has been approached by various machine learning algorithms, however they find a class decision estimate rather than a probabilistic confidence of the class distribution. To counter the shortcoming of common machine learning methods, a novel machine learning technique, i.e. ‘relevance vector machine’ (RVM), for TSA is presented in this paper. RVM is based on a probabilistic Bayesian learning framework, and as a feature it can yield a decision function that depends on only a very fewer number of so-called relevance vectors. The proposed method is tested on New England power system, and compared with a state-of-the-art ‘support vector machine’ (SVM) classifier. The classification performance is evaluated using false discriminate rate (FDR). It is demonstrated that the RVM classifier can yield a decision function that is much sparser than the SVM classifier while providing higher classification accuracy. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time implementation.

scholarly journals IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS

2020 ◽  
Vol 2 (1) ◽  
pp. 13-25
Author(s):  
M.RAMANA REDDY
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Monitoring System ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Pollution Monitoring ◽  
Sensor Data ◽  
Raspberry Pi

Air pollution is the largest environmental and public health challenge in the world today. Air pollution leads to adverse effects on human health, climate and ecosystem. Air is getting polluted because of release of Toxic gases by industries, vehicular emissions and increased concentration of harmful gases and particulate matter in the atmosphere. In order to overcome these issues an IoT based air and sound pollution monitoring system is designed. To design this monitoring system, machine learning algorithms K-NN and Naive Bayes are used. K-Nearest Neighbour and Naive Bayes are machine learning algorithms used to predict the status of pollution present in the environment. In this system, analog to digital converter, global service mobile communication, temperature sensor, humidity sensor, carbon monoxide and sound sensors are interfaced with raspberry pi using serial cable. The sensor data is uploaded in thinkspeak (IoT) and webpage. This data is compared with the trained data to check accuracy. To calculate the accuracy of both algorithms, Python code is developed using python software tool.

scholarly journals Frequency Maps as Expert Instructions to Lessen Data Dependency on Real-time Traffic Light Recognition

2020 ◽  
Author(s):  
Hendrik Macedo ◽  
Thiago Almeida ◽  
Leonardo Matos ◽  
Bruno Prado
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Background Suppression ◽  
Processing Unit ◽  
Traffic Light ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Computational Resources

Research on Traffic Light Recognition (TLR) has grown in recent years, primarily driven by the growing interest in autonomous vehicles development. Machine Learning algorithms have been widely used to that purpose. Mainstream approaches, however, require large amount of data to properly work, and as a consequence, a lot of computational resources. In this paper we propose the use of Expert Instruction (IE) as a mechanism to reduce the amount of data required to provide accurate ML models for TLR. Given an image of the exterior scene taken from the inside of the vehicle, we stand the hypothesis that the picture of a traffic light is more likely to appear in the central and upper regions of the image. Frequency Maps of traffic light location were thus constructed to confirm this hypothesis. The frequency maps are the result of a manual effort of human experts in annotating each image with the coordinates of the region where the traffic light appears. Results show that EI increased the accuracy obtained by the classification algorithm in two different image datasets by at least 15%. Evaluation rates achieved by the inclusion of EI were also higher in further experiments, including traffic light detection followed by classification by the trained algorithm. The inclusion of EI in the PCANet achieved a precision of 83% and recall of 73% against 75.3% and 51.1%, respectively, of its counterpart. We finally presents a prototype of a TLR Device with that expert model embedded to assist drivers. The TLR uses a smartphone as a camera and processing unit. To show the feasibility of the apparatus, a dataset was obtained in real time usage and tested in an Adaptive Background Suppression Filter (AdaBSF) and Support Vector Machines (SVMs) algorithm to detect and recognize traffic lights. Results show precision of 100% and recall of 65%.

scholarly journals Road surface real-time detection based on Raspberry Pi and recurrent neural networks

2021 ◽  
pp. 014233122110033
Author(s):  
Junyi Wang ◽  
Qinggang Meng ◽  
Peng Shang ◽  
Mohamad Saada
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Real Time ◽  
Recurrent Neural Networks ◽  
Road Surface ◽  
Machine Learning Algorithms ◽  
Raspberry Pi ◽  
Accuracy Rate ◽  
Surface Detection ◽  
Real Time Detection

This paper focuses on road surface real-time detection by using tripod dolly equipped with Raspberry Pi 3 B+, MPU 9250, which is convenient to collect road surface data and realize real-time road surface detection. Firstly, six kinds of road surfaces data are collected by utilizing Raspberry Pi 3 B+ and MPU 9250. Secondly, the classifiers can be obtained by adopting several machine learning algorithms, recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks. Among the machine learning classifiers, gradient boosting decision tree has the highest accuracy rate of 97.92%, which improves by 29.52% compared with KNN with the lowest accuracy rate of 75.60%. The accuracy rate of LSTM neural networks is 95.31%, which improves by 2.79% compared with RNN with the accuracy rate of 92.52%. Finally, the classifiers are embedded into the Raspberry Pi to detect the road surface in real time, and the detection time is about one second. This road surface detection system could be used in wheeled robot-car and guiding the robot-car to move smoothly.

Redis-Based Messaging Queue and Cache-Enabled Parallel Processing Social Media Analytics Framework

2020 ◽  
Author(s):  
Ravindra Kumar Singh ◽  
Harsh Kumar Verma
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Real Time ◽  
Data Analytics ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Social Media Analytics ◽  
Data Engineering ◽  
Extreme Gradient Boosting

Abstract The extensive usage of social media polarity analysis claims the need for real-time analytics and runtime outcomes on dashboards. In data analytics, only 30% of the time is consumed in modeling and evaluation stages and 70% is consumed in data engineering tasks. There are lots of machine learning algorithms to achieve a desirable outcome in prediction points of view, but they lack in handling data and their transformation so-called data engineering tasks, and reducing its time remained still challenging. The contribution of this research paper is to encounter the mentioned challenges by presenting a parallelly, scalable, effective, responsive and fault-tolerant framework to perform end-to-end data analytics tasks in real-time and batch-processing manner. An experimental analysis on Twitter posts supported the claims and signifies the benefits of parallelism of data processing units. This research has highlighted the importance of processing mentioned URLs and embedded images along with post content to boost the prediction efficiency. Furthermore, this research additionally provided a comparison of naive Bayes, support vector machines, extreme gradient boosting and long short-term memory (LSTM) machine learning techniques for sentiment analysis on Twitter posts and concluded LSTM as the most effective technique in this regard.

scholarly journals IoT Based WSN Ground Water Monitoring System with Cloud-Based Monitoring as a Service (Maas) and Prediction using Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 816-821
Keyword(s):  
Machine Learning ◽  
Water Level ◽  
Monitoring System ◽  
Indian Subcontinent ◽  
Current System ◽  
Water Monitoring ◽  
Machine Learning Algorithms ◽  
Sensor Nodes ◽  
Raspberry Pi ◽  
Time Data

The main source of water in the Indian Subcontinent is Groundwater. It is also the most rapidly depleting resource due to various reasons such as rampant unchecked irrigation and exploitation of groundwater by industries and other organizations. The current system is limited by short communication range, high power consumption and the system monitors only the water level and the report is available only to the consumer i.e. it is a single-user system. Due to the unavailability of a centralized system to monitor and prevent overuse of water resources, sudden water crises have become a major issue in India. This project aims at implementing an IoT (Internet of things) based water monitoring system that monitors the water level and the quality of groundwater and updates real-time data to the database. This system is designed to monitor the groundwater level of an entire village or a town. It updates the people and the concerned government authorities in case of any decrease in water level and water quality below the threshold value, and also monitors the water consumption during a period and predicts exhaustion time. This system predicts the availability of water in the future based on current demand and usage and the recharge rate using machine learning algorithms. The data collected and the analysis of the data is made available in a Public Cloud. The modules are based on Raspberry Pi Zero, sensor nodes and LoRa (Long Range) Module or Wi-Fi module according to the network requirement for connectivity

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