Wearable smart devices, such as smartphones and smartwatches, offer great potential as platforms for automated human action identification. However, accurately monitoring complex human actions on these devices poses a challenge due to the presence of similarities in patterns across different actions. This occurs when distinct human actions exhibit comparable signal patterns or characteristics. The placement of motion sensors on the body plays a crucial role in detecting human behavior. Typically, wearable sensors placed at the trouser pocket or a similar location are used for this purpose. However, this positioning is not suitable for identifying actions involving manual gestures. To address this, wrist-worn motion sensors are employed to detect these specific behaviors. This study aims to investigate the effectiveness of deep learning models in accurately categorizing complex human actions using sensor data from wrist-worn devices. Nine deep learning models utilizing convolutional neural networks and recurrent neural networks were examined for their identification capabilities. The models were evaluated using the WHARF dataset, a publicly available benchmark dataset for human activity recognition. The investigation revealed that the proposed CNN-BiGRU model outperformed other deep learning models, achieving an accuracy rate of 87.20% and an Fl-score of 84.46%.

Human action identification is a practical area of study with broad applicability in various domains, such as medical care, sport science, and manufacturing management. In logistics, it is essential to identify and examine individual actions, enabling machines to perceive and comprehend human motions for non-verbal interaction. This study specifically fo-cuses on efficiently classifying working activities in the logistics industry using wearable sensors, particularly in the context of human activity recognition. To achieve the research objective, a deep residual neural network was introduced, integrating convolutional layers, shortcut connections, and aggregated transformation for human activity recognition in logistics. The authors evaluated the effectiveness of their proposed deep learning model using the publicly accessible LARa dataset. The LARa dataset comprises a diverse range of human actions in the logistics domain, including standing, walking, cart handling, and synchronization. The details of activity were captured using wearable sensors affixed to different anatomical sites of the study participants. The experimental findings indicate that the model achieved a maximum F-measure of 85.30%.