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| Title | Neurohar: A Neuroevolutionary Method For Human Activity Recognition (Har) For Health Monitoring |
|---|---|
| ID_Doc | 39083 |
| Authors | Alam F.; Plawiak P.; Almaghthawi A.; Qazani M.R.C.; Mohanty S.; Roohallah Alizadehsani A. |
| Year | 2024 |
| Published | IEEE Access, 12 |
| DOI | http://dx.doi.org/10.1109/ACCESS.2024.3441108 |
| Abstract | Human Activity Recognition (HAR) is becoming increasingly important in the fast-evolving landscapes of wearable sensors, smart applications, and the Internet of Things (IoT) paradigms. HAR is rapidly gaining importance, especially in health monitoring, elderly and infant care, fitness tracking, and security. Machine learning (ML) and Deep Learning (DL) methods are used significantly for HAR problems. ML and DL methods often face four significant problems. Firstly, they lack the adaptability to evolve network architectures dynamically, which is vital in complex tasks like HAR. Secondly, classical ML and DL methods could fall short in comprehensively navigating potential solution spaces. Thirdly, finding optimal hyperparameters is a computationally expensive process, and lastly, expert knowledge is required to configure the hyperparameters. This paper proposes the NeuroHAR, a transformative neuroevolutionary method for HAR, to address these problems. NeuroHAR integrates feedforward deep neural networks (FDNN) with evolutionary algorithms. The highlights of NeuroHAR include its dynamic optimization of network architectures and hyperparameters. It is simple to configure and computationally efficient. Due to its adaptable design, it offers a more flexible and robust solution that handles task complexities better than traditional methods. Results are promising, which is evidence of the effectiveness of the proposed NeuroHAR, which outperformed the explicit contender, the state-of-the-art Grid Search approach. NeuroHAR and Grid Search evaluated 900 and 1080 models in Case I. Even with broader hyperparameter ranges in Case II, NeuroHAR still executed 900 models, whereas Grid Search would need over 2 billion models, which proves the computational efficiency of NeuroHAR. Additionally, for HARTH and HAR70Plus imbalanced datasets, the NeuroHAR model achieved a higher prediction accuracy of 89.91% versus Grid Search's 84.04%. NeuroHAR can facilitate advanced monitoring and analytics, which are crucial for health monitoring, elderly care, and urban management powered by wearable sensors, IoT, and smart applications. © 2013 IEEE. |
| Author Keywords | deep learning; evolutionary algorithms; health monitoring; Human activity recognition (HAR); Internet of Things (IoT); smart cities; wearable sensors |
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