In recent times, Human activity recognition (HAR) becomes a major challenging issue among computer vision applications in day to day lives. The HAR is mainly envisioned to be utilized with other technologies, namely Internet of Things (IoT) and sensor technologies. Due to the advancements of deep learning (DL) approaches, automated high level feature extraction process can be utilized to improve the HAR results. In addition, DL techniques can be employed in different domains of sensor enabled

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... R. In this aspect, this study designs an optimal DL based HAR (ODL-HAR) model on sensor enabled IoT environments. The ODL-HAR technique aims to determine human activities in day to day lives using wearables and IoT devices. The proposed ODL-HAR technique involves different stages of operations namely data acquisition, data preprocessing, feature extraction, classification, and parameter optimization. The ODL-HAR technique uses a MobileNet-v2 model as a feature extractor and bidirectional long short-term memory (BiLSTM) model as a classifier. In order to optimally tune the hyperparameter involved in the BiLSTM model, chaos game optimization (CGO) algorithm is employed and thereby raises the recognition performance. The design of CGO algorithm for hyperparameter optimization of HAR shows the novelty of the work. A wide range of simulations takes place to point out the betterment of the ODL-HAR technique on two benchmark datasets. The experimental results portrayed the enhanced performance of the ODL-HAR technique over the other recent HAR approaches interms of different evaluation parameters.