A MODEL BASED ON IOT FOR IMPROVING PROGRAMMING LANGUAGE SKILLS AMONG STUDENTS

2017 ◽  
Vol 5 (2) ◽  
pp. 38
Author(s):  
Kiran Shinde ◽  
Rupali Bhangale
Keyword(s):  
Internet Of Things ◽  
Programming Language ◽  
Activity Recognition ◽  
Human Activity ◽  
Real World ◽  
Language Skills ◽  
Technological Advancement ◽  
Human Intervention ◽  
Proposed Model ◽  
Smart Education

Internet of things (IoT) is the next Buzz word in Computing. It is going to touch much more facets of our lives.It involves real world, physical objects with embedded computational and networking capabilities communicating with one another without human intervention on the global Internet. IoT can be assumed as an umbrella term for interconnected technologies, objects, machines and their services.Due to which objects are communicating   Greater connectivity and technological advancement [1,2] the education has been enriched and expanded. This paper proposes a model for transforming today’s education into SMART education with the use of IOT. There are many areas where human activity recognition is done by using different sensors. Now education sector needs to be connected with such emerging technology.The proposed model will help the students to enhance their grasping level while learning without hesitation.

scholarly journals Design of Optimal Deep Learning based Human Activity Recognition on Sensor Enabled Internet of Things Environment

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fahd N. Al-Wesabi ◽  
Amani Abdulrahman Albraikan ◽  
Anwer Mustafa Hilal ◽  
Asma Abdulghani Al-Shargabi ◽  
Saleh Alhazbi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Internet Of Things Environment

scholarly journals A Novel Multichannel Dilated Convolution Neural Network for Human Activity Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yingjie Lin ◽  
Jianning Wu
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Convolution Neural Network ◽  
Sequence Information ◽  
Practical Applications ◽  
Dilated Convolution ◽  
Proposed Model ◽  
Convolution Structure

A novel multichannel dilated convolution neural network for improving the accuracy of human activity recognition is proposed. The proposed model utilizes the multichannel convolution structure with multiple kernels of various sizes to extract multiscale features of high-dimensional data of human activity during convolution operation and not to consider the use of the pooling layers that are used in the traditional convolution with dilated convolution. Its advantage is that the dilated convolution can first capture intrinsical sequence information by expanding the field of convolution kernel without increasing the parameter amount of the model. And then, the multichannel structure can be employed to extract multiscale gait features by forming multiple convolution paths. The open human activity recognition dataset is used to evaluate the effectiveness of our proposed model. The experimental results showed that our model achieves an accuracy of 95.49%, with the time to identify a single sample being approximately 0.34 ms on a low-end machine. These results demonstrate that our model is an efficient real-time HAR model, which can gain the representative features from sensor signals at low computation and is hopeful for the effective tool in practical applications.

scholarly journals An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zongying Liu ◽  
Shaoxi Li ◽  
Jiangling Hao ◽  
Jingfeng Hu ◽  
Mingyang Pan
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Processing Time ◽  
Kernel Method ◽  
Nearest Neighbors ◽  
Human Activity Recognition ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Proposed Model

With accumulation of data and development of artificial intelligence, human activity recognition attracts lots of attention from researchers. Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. However, these algorithms have their own limitations and their prediction accuracy still has space to improve. In this study, we focus on K-nearest neighbors (KNN) and solve its limitations. Firstly, kernel method is employed in model KNN, which transforms the input features to be the high-dimensional features. The proposed model KNN with kernel (K-KNN) improves the accuracy of classification. Secondly, a novel reduced kernel method is proposed and used in model K-KNN, which is named as Reduced Kernel KNN (RK-KNN). It reduces the processing time and enhances the classification performance. Moreover, this study proposes an approach of defining number of K neighbors, which reduces the parameter dependency problem. Based on the experimental works, the proposed RK-KNN obtains the best performance in benchmarks and human activity datasets compared with other models. It has super classification ability in human activity recognition. The accuracy of human activity data is 91.60% for HAPT and 92.67% for Smartphone, respectively. Averagely, compared with the conventional KNN, the proposed model RK-KNN increases the accuracy by 1.82% and decreases standard deviation by 0.27. The small gap of processing time between KNN and RK-KNN in all datasets is only 1.26 seconds.

scholarly journals Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Human Activity ◽  
Recognition System ◽  
The Internet ◽  
Learning Approaches ◽  
The Public ◽  
Sensory Data ◽  
The Internet Of Things

The growing use of sensor tools and the Internet of Things requires sensors to understand the applications. There are major difficulties in realistic situations, though, that can impact the efficiency of the recognition system. Recently, as the utility of deep learning in many fields has been shown, various deep approaches were researched to tackle the challenges of detection and recognition. We present in this review a sample of specialized deep learning approaches for the identification of sensor-based human behaviour. Next, we present the multi-modal sensory data and include information for the public databases which can be used in different challenge tasks for study. A new taxonomy is then suggested, to organize deep approaches according to challenges. Deep problems and approaches connected to problems are summarized and evaluated to provide an analysis of the ongoing advancement in science. By the conclusion of this research, we are answering unanswered issues and providing perspectives into the future.

scholarly journals Human Activity Recognition Based on Residual Network and BiLSTM

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 635
Author(s):  
Yong Li ◽  
Luping Wang
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Inertial Sensors ◽  
Human Activity Recognition ◽  
Activity Data ◽  
Multidimensional Signals ◽  
Proposed Model ◽  
Public Datasets ◽  
Residual Block

Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters.

scholarly journals Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data (Preprint)

2018 ◽  
Author(s):  
Kenan Li ◽  
Rima Habre ◽  
Huiyu Deng ◽  
Robert Urman ◽  
John Morrison ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Real World ◽  
Sliding Window ◽  
Human Activity Recognition ◽  
Change Points ◽  
Accuracy Rate ◽  
Segmentation Methods ◽  
Variable Duration ◽  
Activity Bout

BACKGROUND Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. OBJECTIVE We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. METHODS We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing. RESULTS In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. CONCLUSIONS In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.

scholarly journals Internet of Things Analytics for Smart Home Applications

2020 ◽  
Vol 9 (7) ◽  
pp. 583-588
Keyword(s):  
Internet Of Things ◽  
Activity Recognition ◽  
Human Activity ◽  
Smart Home ◽  
Wearable Devices ◽  
Sensor Nodes ◽  
Air Conditioner ◽  
Efficient Manner ◽  
Home Appliances ◽  
Iot Devices

With advancement in smart home services on mobile and wearable devices, individual can smartly control his/her home appliances such as fan, refrigerator, TV, air conditioner, etc., in an efficient manner. Internets of Things (IoT) devices are extensively utilized to interchange the data between smart applications, mobiles, and wearables. IoT devices are responsible for monitoring and sensing the data about home appliances with the help of sensor nodes, the obtained data is then communicate to given high-end devices for taking the suitable action. The overall objective of this paper is to study the existing IoT analytics techniques which are used to build smart applications for homes. This paper also discusses the various challenges to design a suitable smart home using IoTs. Thereafter, a comparative analyzes are considered to evaluate the shortcomings of these techniques and various gaps are formulated in the existing techniques. Finally, a methdology has been devised which can overcome the shortcomings of existing models and help enhancing the functioning of human activity recognition in smart homes.

scholarly journals Intelligent system for human activity recognition in IoT environment

2021 ◽  
Author(s):  
Hassan Khaled ◽  
Osama Abu-Elnasr ◽  
Samir Elmougy ◽  
A. S. Tolba
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Performance Enhancement ◽  
Intelligent System ◽  
Experimental Results ◽  
Daily Activities ◽  
Smart Devices ◽  
Proposed Model

AbstractIn recent years, the adoption of machine learning has grown steadily in different fields affecting the day-to-day decisions of individuals. This paper presents an intelligent system for recognizing human’s daily activities in a complex IoT environment. An enhanced model of capsule neural network called 1D-HARCapsNe is proposed. This proposed model consists of convolution layer, primary capsule layer, activity capsules flat layer and output layer. It is validated using WISDM dataset collected via smart devices and normalized using the random-SMOTE algorithm to handle the imbalanced behavior of the dataset. The experimental results indicate the potential and strengths of the proposed 1D-HARCapsNet that achieved enhanced performance with an accuracy of 98.67%, precision of 98.66%, recall of 98.67%, and F1-measure of 0.987 which shows major performance enhancement compared to the Conventional CapsNet (accuracy 90.11%, precision 91.88%, recall 89.94%, and F1-measure 0.93).

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