DiscHAR: A Discrete Approach to Enhance Human Activity Recognition in Cyber Physical Systems: Smart Homes
The main challenges in smart home systems and cyber-physical systems come from not having enough data and unclear interpretation; thus, there is still a lot to be done in this field. In this work, we propose a practical approach called Discrete Human Activity Recognition (DiscHAR) based on prior res...
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MDPI AG
2024-11-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/13/11/300 |
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| author | Ishrat Fatima Asma Ahmad Farhan Maria Tamoor Shafiq ur Rehman Hisham Abdulrahman Alhulayyil Fawaz Tariq |
| author_facet | Ishrat Fatima Asma Ahmad Farhan Maria Tamoor Shafiq ur Rehman Hisham Abdulrahman Alhulayyil Fawaz Tariq |
| author_sort | Ishrat Fatima |
| collection | DOAJ |
| description | The main challenges in smart home systems and cyber-physical systems come from not having enough data and unclear interpretation; thus, there is still a lot to be done in this field. In this work, we propose a practical approach called Discrete Human Activity Recognition (DiscHAR) based on prior research to enhance Human Activity Recognition (HAR). Our goal is to generate diverse data to build better models for activity classification. To tackle overfitting, which often occurs with small datasets, we generate data and convert them into discrete forms, improving classification accuracy. Our methodology includes advanced techniques like the R-Frame method for sampling and the Mixed-up approach for data generation. We apply K-means vector quantization to categorize the data, and through the elbow method, we determine the optimal number of clusters. The discrete sequences are converted into one-hot encoded vectors and fed into a CNN model to ensure precise recognition of human activities. Evaluations on the OPP79, PAMAP2, and WISDM datasets show that our approach outperforms existing models, achieving 89% accuracy for OPP79, 93.24% for PAMAP2, and 100% for WISDM. These results demonstrate the model’s effectiveness in identifying complex activities captured by wearable devices. Our work combines theory and practice to address ongoing challenges in this field, aiming to improve the reliability and performance of activity recognition systems in dynamic environments. |
| format | Article |
| id | doaj-art-7877e0c04c764b28abb2eae278cbe1ef |
| institution | OA Journals |
| issn | 2073-431X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Computers |
| spelling | doaj-art-7877e0c04c764b28abb2eae278cbe1ef2025-08-20T01:53:44ZengMDPI AGComputers2073-431X2024-11-01131130010.3390/computers13110300DiscHAR: A Discrete Approach to Enhance Human Activity Recognition in Cyber Physical Systems: Smart HomesIshrat Fatima0Asma Ahmad Farhan1Maria Tamoor2Shafiq ur Rehman3Hisham Abdulrahman Alhulayyil4Fawaz Tariq5Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54000, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences, Lahore 54000, PakistanDepartment of Computer Science, Forman Christian College, Lahore 54000, PakistanCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi ArabiaCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi ArabiaDepartment of Geodesy and Geoinformation Science, Technical University of Berlin, 10623 Berlin, GermanyThe main challenges in smart home systems and cyber-physical systems come from not having enough data and unclear interpretation; thus, there is still a lot to be done in this field. In this work, we propose a practical approach called Discrete Human Activity Recognition (DiscHAR) based on prior research to enhance Human Activity Recognition (HAR). Our goal is to generate diverse data to build better models for activity classification. To tackle overfitting, which often occurs with small datasets, we generate data and convert them into discrete forms, improving classification accuracy. Our methodology includes advanced techniques like the R-Frame method for sampling and the Mixed-up approach for data generation. We apply K-means vector quantization to categorize the data, and through the elbow method, we determine the optimal number of clusters. The discrete sequences are converted into one-hot encoded vectors and fed into a CNN model to ensure precise recognition of human activities. Evaluations on the OPP79, PAMAP2, and WISDM datasets show that our approach outperforms existing models, achieving 89% accuracy for OPP79, 93.24% for PAMAP2, and 100% for WISDM. These results demonstrate the model’s effectiveness in identifying complex activities captured by wearable devices. Our work combines theory and practice to address ongoing challenges in this field, aiming to improve the reliability and performance of activity recognition systems in dynamic environments.https://www.mdpi.com/2073-431X/13/11/300human activity recognition (HAR)data augmentationK-meansvector quantizationconvolutional neural network (CNN)R-Frame sampling |
| spellingShingle | Ishrat Fatima Asma Ahmad Farhan Maria Tamoor Shafiq ur Rehman Hisham Abdulrahman Alhulayyil Fawaz Tariq DiscHAR: A Discrete Approach to Enhance Human Activity Recognition in Cyber Physical Systems: Smart Homes Computers human activity recognition (HAR) data augmentation K-means vector quantization convolutional neural network (CNN) R-Frame sampling |
| title | DiscHAR: A Discrete Approach to Enhance Human Activity Recognition in Cyber Physical Systems: Smart Homes |
| title_full | DiscHAR: A Discrete Approach to Enhance Human Activity Recognition in Cyber Physical Systems: Smart Homes |
| title_fullStr | DiscHAR: A Discrete Approach to Enhance Human Activity Recognition in Cyber Physical Systems: Smart Homes |
| title_full_unstemmed | DiscHAR: A Discrete Approach to Enhance Human Activity Recognition in Cyber Physical Systems: Smart Homes |
| title_short | DiscHAR: A Discrete Approach to Enhance Human Activity Recognition in Cyber Physical Systems: Smart Homes |
| title_sort | dischar a discrete approach to enhance human activity recognition in cyber physical systems smart homes |
| topic | human activity recognition (HAR) data augmentation K-means vector quantization convolutional neural network (CNN) R-Frame sampling |
| url | https://www.mdpi.com/2073-431X/13/11/300 |
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