List of Articles Activity Recognition

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  1 - Human Activity Recognition based on Deep Belief Network Classifier and Combination of Local and Global Features
  Azar Mahmoodzadeh
  10.52547/jist.9.33.45
  During the past decades, recognition of human activities has attracted the attention of numerous researches due to its outstanding applications including smart houses, health-care and monitoring the private and public places. Applying to the video frames, this paper pro More

  During the past decades, recognition of human activities has attracted the attention of numerous researches due to its outstanding applications including smart houses, health-care and monitoring the private and public places. Applying to the video frames, this paper proposes a hybrid method which combines the features extracted from the images using the ‘scale-invariant features transform’ (SIFT), ‘histogram of oriented gradient’ (HOG) and ‘global invariant features transform’ (GIST) descriptors and classifies the activities by means of the deep belief network (DBN). First, in order to avoid ineffective features, a pre-processing course is performed on any image in the dataset. Then, the mentioned descriptors extract several features from the image. Due to the problems of working with a large number of features, a small and distinguishing feature set is produced using the bag of words (BoW) technique. Finally, these reduced features are given to a deep belief network in order to recognize the human activities. Comparing the simulation results of the proposed approach with some other existing methods applied to the standard PASCAL VOC Challenge 2010 database with nine different activities demonstrates an improvement in the accuracy, precision and recall measures (reaching 96.39%, 85.77% and 86.72% respectively) for the approach of this work with respect to the other compared ones in the human activity recognition. Manuscript profile
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  2 - Human Activity Recognition using Switching Structure Model
  Mohammad Mahdi Arzani M. Fathy Ahmad Akbari
  To communicate with people interactive systems often need to understand human activities in advance. However, recognizing activities in advance is a very challenging task, because people perform their activities in different ways, also, some activities are simple while More

  To communicate with people interactive systems often need to understand human activities in advance. However, recognizing activities in advance is a very challenging task, because people perform their activities in different ways, also, some activities are simple while others are complex and comprised of several smaller atomic sub-activities. In this paper, we use skeletons captured from low-cost depth RGB-D sensors as high-level descriptions of the human body. We propose a method capable of recognizing simple and complex human activities by formulating it as a structured prediction task using probabilistic graphical models (PGM). We test our method on three popular datasets: CAD-60, UT-Kinect, and Florence 3D. These datasets cover both simple and complex activities. Also, our method is sensitive to clustering methods that are used to determine the middle states, we evaluate test different clustering, methods. Manuscript profile
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  3 - Robust Human Physical Activity Recognition Using Smartphone Sensors
  Mahdi  Yazdian Dehkordi Zahra Abedi Nasim Khani
  Human physical activity recognition using gyroscope and accelerometer sensors of smartphones has attracted many researches in recent years. In this paper, the performance of principle component analysis feature extraction method and several classifiers including support More

  Human physical activity recognition using gyroscope and accelerometer sensors of smartphones has attracted many researches in recent years. In this paper, the performance of principle component analysis feature extraction method and several classifiers including support vector machine, logestic regression, Adaboost and convolutional neural network are evaluated to propose an efficient system for human activity recognition. The proposed system can improve the classification accuracy in comparison with the state of the art researches in this field. The performance of a physical activity recognition system is expected to be robust on different smartphone platforms. The quality of smartphone sensors and their corresponding noises vary considerably between different smartphone models and sometimes within the same model. Therefore, it is beneficial to study the effect of noise on the efficiency of the human activity recognition system. In this paper, the robustness of the investigated classifiers are also studied in various level of sensor noises to find the best robust solution for this purpose. The experimental results, which is provided on a well-known human activity recognition dataset, show that the support vector machine with averaged accuracy of 96.34% perform more robust than the other classifiers on different level of sensor noises. Manuscript profile
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  4 - A Comparison Analysis of Conventional Classifiers and Deep Learning Model for Activity Recognition in Smart Homes based on Multi-label Classification
  John Kasubi Manjaiah D.  Huchaiah Ibrahim Gad Mohammad Kazim  Hooshmand
  10.61186/jist.36294.12.46.127
  Activity Recognition is essential for exploring the various activities that humans engage in within Smart Homes in the presence of multiple sensors as residents interact with household appliances. Smart homes use intelligent IoT devices linked to residents' homes to tra More

  Activity Recognition is essential for exploring the various activities that humans engage in within Smart Homes in the presence of multiple sensors as residents interact with household appliances. Smart homes use intelligent IoT devices linked to residents' homes to track changes in human behavior as the humans interact with the home's equipment, which may improve healthcare and security issues for the residents. This study presents a research work that compares conventional classifiers such as DT, LDA, Adaboost, GB, XGBoost, MPL, KNN, and DL, focusing on recognizing human activities in Smart Homes using Activity Recognizing Ambient Sensing (ARAS). The experimental results demonstrated that DL Model outperformed with excellent accuracy compared to conventional classifiers in recognizing human activities in Smart Homes. This work proves that DL Models perform best in analyzing ARAS datasets compared to traditional machine learning algorithms. Manuscript profile