###### Abstract
Deep neural networks are capable of learning discriminative characteristics from raw data efficiently, and they have demonstrated immense promise for evaluating diverse data and a high capacity for generalization. Numerous uni-model and hybrid methods have been developed to capitalize on DL methods by compensating for the weaknesses of traditional machine learning and leveraging the multiple levels of information present at various levels of hierarchies [16]. A hierarchy of layers is employed in DL models to handle low /high-level features and linear/nonlinear feature modifications at different levels, which supports learning and adjusting features. To this end, models such as Recurrent Neural Networks (RNN) [17], Convolutional Neural Networks (CNN) [18], and Long Short-Term Memory (LSTM) [19], among others, are used to address the shortcomings of traditional machine learning algorithms that relied on manual feature selection/classification, in which incorrect feature selection/classification could have unfavorable effects. As a result, DL networks have realized a practical implementation in identification schemes and are often utilized for feature extraction in studies on action recognition [20]. One disadvantage of the DL paradigm, mainly when hybrid structures are being used, is the higher expense of computing the massive quantity of accessible information. Nevertheless, the cost is justified since an S-HAR system relies on accurate classification performance from DL models. While the previous investigations have made substantial advances, two fundamental constraints remain. Conventional algorithms for transitional movement identification rely laboriously on handmade characteristics. Such techniques, nevertheless, are not durable and depend excessively on professional expertise. Several studies combined time-domain, frequency-domain, and nonlinear features to generate feature vectors, a laborious and error-prone process to extract efficient temporal characteristics from short-duration data of transitional events. Additionally, such characteristics are often task-specific and lack universality. Furthermore, inaccuracies in the feature extraction phase might spread to subsequent stages, impairing detection capability. Although a few deep learning-based techniques have been offered to investigate the information about transitions between ADL actions, their performance falls short of time-series ADL signal-based methods. Second, since the categorization borders between transitional movements are not apparent, they may be misidentified when using the same fixed-length windowing settings. Previous studies have found that there is much scope for development in identifying transitional activities. To solve these concerns, this work proposes a hybrid DL model in which networks are trained to identify both fundamental and transitional human behaviors concurrently. The innovative components include the concurrent deployment of several DL models to improve classification accuracy and the addition of transitional behaviors to deliver a robust method of activity identification. A summary of the significant contributions of this study is presented as follows:  
* This work presents a hybrid deep residual model based on convolutional layers and bidirectional gated recurrent unit layers that is capable of autonomously learning local features and modeling their temporal dependency. The proposed method augments the deep residual model with hybrid Squeeze-and-Excitation (SE) residual blocks, allowing for hierarchical extraction of deep spatio-temporal characteristics and effective separation of transitional activities. * The proposed model achieved the recognition of transitional activities and basic ADL activities in short durations of continuous sensor data streams. * We investigated the effects of recognizing transitional activities in various issues including segmentation techniques and multimodal sensors. * We evaluated the exploratory findings in comparison to those obtained from other models using the same HAR dataset.