@mastersthesis{MTMT:33054181,
	title = {Cyber Physical System for Pre-operative Patient Prehabilitation},
	url = {https://m2.mtmt.hu/api/publication/33054181},
	author = {Al-Naime, Khalid Abdulrazak Mahmood},
	unique-id = {33054181},
	year = {2022}
}

@mastersthesis{MTMT:33054162,
	title = {Post-Operative Hip Fracture Rehabilitation Activity Movement Monitoring},
	url = {https://m2.mtmt.hu/api/publication/33054162},
	author = {Gupta, Akash},
	unique-id = {33054162},
	year = {2022}
}

@inproceedings{MTMT:32211537,
	title = {IoT Environment for Monitoring Human Movements: Hip Fracture Rehabilitation Case},
	url = {https://m2.mtmt.hu/api/publication/32211537},
	author = {Gupta, A. and Al-Naime, K. and Al-Anbuky, A.},
	booktitle = {Information and Communication Technologies for Ageing Well and e-Health},
	doi = {10.1007/978-3-030-70807-8_3},
	volume = {1387},
	unique-id = {32211537},
	year = {2021},
	pages = {44-63}
}

@article{MTMT:32440318,
	title = {IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model},
	url = {https://m2.mtmt.hu/api/publication/32440318},
	author = {Gupta, Akash and Al-Anbuky, Adnan},
	doi = {10.3390/fi13080195},
	journal-iso = {FUTURE INTERNET},
	journal = {FUTURE INTERNET},
	volume = {13},
	unique-id = {32440318},
	abstract = {Hip fracture incidence is life-threatening and has an impact on the person's physical functionality and their ability to live independently. Proper rehabilitation with a set program can play a significant role in recovering the person's physical mobility, boosting their quality of life, reducing adverse clinical outcomes, and shortening hospital stays. The Internet of Things (IoT), with advancements in digital health, could be leveraged to enhance the backup intelligence used in the rehabilitation process and provide transparent coordination and information about movement during activities among relevant parties. This paper presents a post-operative hip fracture rehabilitation model that clarifies the involved rehabilitation process, its associated events, and the main physical movements of interest across all stages of care. To support this model, the paper proposes an IoT-enabled movement monitoring system architecture. The architecture reflects the key operational functionalities required to monitor patients in real time and throughout the rehabilitation process. The approach was tested incrementally on ten healthy subjects, particularly for factors relevant to the recognition and tracking of movements of interest. The analysis reflects the significance of personalization and the significance of a one-minute history of data in monitoring the real-time behavior. This paper also looks at the impact of edge computing at the gateway and a wearable sensor edge on system performance. The approach provides a solution for an architecture that balances system performance with remote monitoring functional requirements.},
	keywords = {rehabilitation; Internet of Things (IOT); Activity recognition; Edge Computing; hip fracture model; remote movement monitoring; wearable intelligent sensor},
	year = {2021},
	eissn = {1999-5903}
}

@article{MTMT:3427268,
	title = {Comparison of offline and real-time human activity recognition results using machine learning techniques},
	url = {https://m2.mtmt.hu/api/publication/3427268},
	author = {Sütő, József and Oniga, István and Lung, Claudiu and Orha, Ioan},
	doi = {10.1007/s00521-018-3437-x},
	journal-iso = {NEURAL COMPUT APPL},
	journal = {NEURAL COMPUTING & APPLICATIONS},
	volume = {32},
	unique-id = {3427268},
	issn = {0941-0643},
	year = {2020},
	eissn = {1433-3058},
	pages = {15673-15686},
	orcid-numbers = {Oniga, István/0000-0003-2353-6759}
}

@article{MTMT:3241148,
	title = {Efficiency investigation of artificial neural networks in human activity recognition.},
	url = {https://m2.mtmt.hu/api/publication/3241148},
	author = {Sütő, József and Oniga, István},
	doi = {10.1007/s12652-017-0513-5},
	journal-iso = {J AMBIENT INTELL HUMAN COMPUT},
	journal = {JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING},
	volume = {9},
	unique-id = {3241148},
	issn = {1868-5137},
	year = {2018},
	eissn = {1868-5145},
	pages = {1049-1060},
	orcid-numbers = {Oniga, István/0000-0003-2353-6759}
}

@article{MTMT:27011073,
	title = {Game theoretic decision making in IoT-assisted activity monitoring of defence personnel},
	url = {https://m2.mtmt.hu/api/publication/27011073},
	author = {Bhatia, Munish and Sood, Sandeep K},
	doi = {10.1007/s11042-017-4611-3},
	journal-iso = {MULTIMED TOOLS APPL},
	journal = {MULTIMEDIA TOOLS AND APPLICATIONS: AN INTERNATIONAL JOURNAL},
	volume = {76},
	unique-id = {27011073},
	issn = {1380-7501},
	year = {2017},
	eissn = {1573-7721},
	pages = {21911-21935}
}

@article{MTMT:27011076,
	title = {Internet of Things based activity surveillance of defence personnel},
	url = {https://m2.mtmt.hu/api/publication/27011076},
	author = {Bhatia, Munish and Sood, Sandeep K},
	doi = {10.1007/s12652-017-0507-3},
	journal-iso = {J AMBIENT INTELL HUMAN COMPUT},
	journal = {JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING},
	volume = {Epub},
	unique-id = {27011076},
	issn = {1868-5137},
	year = {2017},
	eissn = {1868-5145}
}

@article{MTMT:3177961,
	title = {Feature Analysis to Human Activity Recognition},
	url = {https://m2.mtmt.hu/api/publication/3177961},
	author = {Sütő, József and Oniga, István and Sitar, PP},
	journal-iso = {INT J COMPUT COMMUN CONTROL},
	journal = {INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL},
	volume = {12},
	unique-id = {3177961},
	issn = {1841-9836},
	year = {2017},
	eissn = {1841-9844},
	pages = {116-130},
	orcid-numbers = {Oniga, István/0000-0003-2353-6759}
}

@inproceedings{MTMT:3276422,
	title = {Recognition rate difference between real-time and offline human activity recognition},
	url = {https://m2.mtmt.hu/api/publication/3276422},
	author = {Sütő, József and Oniga, István and Lung, Claudiu and Orha, Ioan},
	booktitle = {Internet of Things for the Global Community (IoTGC), 2017 International Conference on},
	doi = {10.1109/IoTGC.2017.8008967},
	unique-id = {3276422},
	year = {2017},
	pages = {13-18},
	orcid-numbers = {Oniga, István/0000-0003-2353-6759}
}

@article{MTMT:3035511,
	title = {Activity Recognition in Adaptive Assistive Systems Using Artificial Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/3035511},
	author = {Oniga, István and Sütő, József},
	doi = {10.5755/j01.eee.22.1.14112},
	journal-iso = {ELEKTRON ELEKTROTECH},
	journal = {ELEKTRONIKA IR ELEKTROTECHNIKA},
	volume = {22},
	unique-id = {3035511},
	issn = {1392-1215},
	year = {2016},
	pages = {68-72},
	orcid-numbers = {Oniga, István/0000-0003-2353-6759}
}

@inproceedings{MTMT:3123995,
	title = {Comparison of wrapper and filter feature selection algorithms on human activity recognition},
	url = {https://m2.mtmt.hu/api/publication/3123995},
	author = {Sütő, József and Oniga, István and Sitar, Petrica Pop},
	booktitle = {2016 6th International Conference on Computers Communications and Control (ICCCC)},
	doi = {10.1109/ICCCC.2016.7496749},
	unique-id = {3123995},
	abstract = {Feature selection is an increasingly important part of machine learning. The purpose of feature selection is dimension reduction in a large multi-dimensional data set and it can be the key step of successful knowledge discovery in those problems where the number of features is large. This research area has huge practical significance because it accelerates decisions and improves performance. The requirements of specific applications in different kinds of research areas have led to the development of new feature selection techniques with different properties. In the last few decades, several feature selection algorithms have been proposed with their particular advantages and disadvantages. Despite of the intensive research and the large amount of works, the different kinds of feature selection algorithms have not been tested yet in the human activity recognition problem. It was the main motivation of our work and this paper tries to fill this gap. Therefore, in this article we present a conceptually simple naive Bayesian wrapper feature selection method and compare it with some widely used filter feature selection algorithms. The result of this work demonstrates that, the wrapper technique outperforms filter algorithms in this type of problem. In addition, this paper shows an example, when the classifier dependency of a wrapper method do not visible.},
	year = {2016},
	pages = {124-129},
	orcid-numbers = {Oniga, István/0000-0003-2353-6759}
}