Smart City Gnosys
Smart city article details
| Title | Deep Learning-Driven Human Activity Recognition For Remote Health Applications In Internet Of Things-Enabled Smart Cities |
|---|---|
| ID_Doc | 17978 |
| Authors | Sethi N.; Bebortta S.; Tripathy S.S.; Pani S.K. |
| Year | 2025 |
| Published | Nanosensors as Robust Non-Invasive Diagnostic Tools for Remote Health Monitoring |
| DOI | http://dx.doi.org/10.1201/9781003602729-3 |
| Abstract | The Internet of Things (IoT) has significantly changed a number of businesses, with the healthcare sector as one of the main gainers. The idea of the Internet of Health Things (IoHT) has evolved with the intention of utilizing IoT technologies for applications in the field of healthcare. Accurately identifying and comprehending human activity is a key component of IoHT, which has the potential to revolutionize patient monitoring, therapy, and overall healthcare management. This entails creating a trustworthy and efficient system that can automatically identify and classify human actions using information from wearable and ambient sensors. The proposed human activity recognition (HAR) system gathers and processes patient data by integrating wearable and ambient sensors with edge computing techniques. Deep learning models and other advanced machine learning (ML) techniques are used to accurately categorize human behavior and retrieve pertinent data. For data processing and storage, the system also makes use of cloud computing infrastructure, allowing for long-term monitoring and producing insightful data. The proposed HAR system gathers and processes patient data by integrating wearable and ambient sensors with edge computing techniques. Deep learning models and other advanced ML techniques are used to accurately categorize human behavior and retrieve pertinent data. For data processing and storage, the system also makes use of cloud computing infrastructure, allowing for long-term monitoring and producing insightful data. Security is still an issue with IoT integration in healthcare and smart cities, despite the benefits. Wearable technology exposes users to continuous monitoring and data transmission through unprotected channels, which opens them up to cyber-attacks. To protect patients’ essential surveillance data, it is imperative to implement strong cyber-security safeguards. IoHT use also extends outside of healthcare environments, such as smartwatches and smartphones that include health-monitoring functions like heart rate, oxygen saturation, and activity tracking. This enables users to carefully observe their body’s actions, demonstrating how IoHT is becoming an essential component of people’s daily lives in the present era. © 2025 selection and editorial matter, Priyanka Dwivedi, Chinmay Chakraborty, Gwanggil Jeon, and Yan Zhang; individual chapters, the contributors. |
| Author Keywords |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
32858  | 0.9 | Ahmed I.; Camacho D.; Jeon G.; Piccialli F. | Internet Of Health Things Driven Deep Learning-Based System For Non-Invasive Patient Discomfort Detection Using Time Frame Rules And Pairwise Keypoints Distance Feature | Sustainable Cities and Society, 79 (2022) |
| 38615 | 0.897 | Kalimuthu S.; Perumal T.; Yaakob R.; Marlisah E.; Raghavan S. | Multiple Human Activity Recognition Using Iot Sensors And Machine Learning In Device-Free Environment: Feature Extraction, Classification, And Challenges: A Comprehensive Review | AIP Conference Proceedings, 2816, 1 (2024) |
| 29571 | 0.889 | Kalimuthu S.; Perumal T.; Yaakob R.; Marlisah E.; Babangida L. | Human Activity Recognition Based On Smart Home Environment And Their Applications, Challenges | 2021 International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2021 (2021) |
| 29569 | 0.889 | Chen L.; Nugent C.D. | Human Activity Recognition And Behaviour Analysis: For Cyber-Physical Systems In Smart Environments | Human Activity Recognition and Behaviour Analysis: For Cyber-Physical Systems in Smart Environments (2019) |
| 51669 | 0.886 | Gong Z.; Ji J.; Tong P.; Metwally A.S.M.; Dutta A.K.; Rodrigues J.J.P.C.; Mohamad U.H. | Smart Urban Planning: Intelligent Cognitive Analysis Of Healthcare Data In Cloud-Based Iot | Computers and Electrical Engineering, 110 (2023) |
| 33584 | 0.885 | Alshamrani M. | Iot And Artificial Intelligence Implementations For Remote Healthcare Monitoring Systems: A Survey | Journal of King Saud University - Computer and Information Sciences, 34, 8 (2022) |
| 1826 | 0.882 | Turetta C.; Demrozi F.; Pravadelli G. | A Freely Available System For Human Activity Recognition Based On A Low-Cost Body Area Network | Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022 (2022) |
| 55556 | 0.882 | Sivapriyan R.; Sarkar R.; Reddy Y.P.K.; Loganayagi S.; Kareem D.S.; Meassar A.-H. | The Framework For Training And Validation Of Healthcare System Using Accurate Classifier Model | 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024 (2024) |
| 29572 | 0.875 | Dhammi L.; Tewari P. | Human Activity Recognition Based On Smartphone Sensors- A Comparative Study | 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021 (2021) |
| 15335 | 0.874 | Rath S.K.; Sahu M.; Das S.P. | Comprehensive Survey Of Iot, Machine Learning, And Reliability Engineering For Healthcare Applications | Healthcare-Driven Intelligent Computing Paradigms to Secure Futuristic Smart Cities (2024) |