Modeling of Hyperparameter Tuned Fuzzy Deep Neural Network-Based Human Activity Recognition for Disabled People

Human activity recognition (HAR) for disabled people is a vital research area, which aims to help individuals with disabilities in their daily lives. HAR involves using technology, typically wearable devices or sensors, to automatically identify and classify human activities and movements. HAR using deep learning (DL) is an effective and popular method to automatically classify and identify human activities based on sensor information. This article develops a hyperparameter tuned fuzzy deep neural network-based HAR (HTFDNN-HAR) method. The objective of the HTFDNN-HAR method lies in human activities identification and classification. In the presented HTFDNN-HAR technique, cross-guided bilateral filtering (CGBF)-based preprocessing is initially applied and MobileNetV3 architecture is applied for the effectual extraction of the feature vectors. In addition, the HTFDNN-HAR technique makes use of the FDNN method for the efficient detection and classification of human activities. Finally, the HTFDNN-HAR technique applies the manta ray foraging optimization (MRFO) technique for the optimum hyperparameter selection of the FDNN approach. A wide range of experiments have been carried out on the CAUCAFall dataset comprising 10,000 samples with two classes. The simulation value highlighted that the HTFDNN-HAR technique reaches an enhanced accuracy of 99.40% over other recent approaches.