###### Abstract  
Numerous learning-based techniques for effective human behavior identification have emerged in recent years. These techniques focus only on fundamental human activities, excluding transitional activities due to their infrequent occurrence and short period. Nevertheless, postural transitions play a critical role in implementing a system for recognizing human activity and cannot be ignored. This study aims to present a hybrid deep residual model for transitional activity recognition utilizing signal data from wearable sensors. The developed model enhances the ResNet model with hybrid Squeeze-and-Excitation (SE) residual blocks combining a Bidirectional Gated Recurrent Unit (BiGRU) to extract deep spatio-temporal features hierarchically, and to distinguish transitional activities efficiently. To evaluate recognition performance, the experiments are conducted on two public benchmark datasets (HAPT and MobiAct v2.0). The proposed hybrid approach achieved classification accuracies of 98.03% and 98.92% for the HAPT and MobiAct v2.0 datasets, respectively. Moreover, the outcomes show that the proposed method is superior to the state-of-the-art methods in terms of overall accuracy. To analyze the improvement, we have investigated the effects of combining SE modules and BiGRUs into the deep residual network. The findings indicates that the SE module is efficient in improving transitional activity recognition. deep residual network; human activity recognition; transitional activities; hybrid deep learning model; bidirectional GRUs +
Footnote †: journal: Medical  
of benefits. It is unaffected by the surrounding environment and seems to have the potential to achieve greater precision. Additionally, it will not raise users' privacy concerns. As a result, sensor-based techniques are more appropriate for recognizing human activities [5]. This work is primarily concerned with the issue of sensor-based HAR. Sensor-based Human Activity Recognition (S-HAR) is concerned with identifying, interpreting, and evaluating human activities using sensor data. Wearable sensors, including an accelerometer, gyroscope, or other complex sensors, may be connected to various body areas to monitor movement patterns and behaviors. Numerous S-HAR systems have been developed to automate the applications mentioned above; nevertheless, creating a fully automated S-HAR framework could be a difficult challenge due to the massive quantities of movement data and the effective categorization techniques required. Additionally, it is a complex endeavor since a single motion could be executed in several ways [6]. Human activities are often defined as fundamental Activities in Daily Living (ADL) and the postural changes between or within these activities. Postural transition is a limited motion between two essential activities, which differs amongst persons in terms of time and actions. Most works do not consider the postural variations due to their short duration. However, while executing numerous tasks in a short time frame, they play a significant role in efficiently recognizing activities [7]. Consequently, transition activity recognition is vital for HAR mechanisms. For S-HAR research, learning-based approaches such as machine learning techniques have been used effectively throughout the previous decade. Under controlled circumstances, behavior identification using conventional machine learning techniques such as K-Nearest Neighborhood [8], Support Vector Machine (SVM) [9], Decision Tree (DT) [10], and Random Forest (RF) [11] has been successfully implemented [12]. The accuracy of these standard machine learning models is highly dependent on the process of human-manually extracted and selected features. Deep Learning (DL) algorithms have recently achieved notable advances in the field of S-HAR [13; 14]. One of the most significant characteristics of deep learning is its ability to automatically determine and classify features with a high degree of accuracy, which influenced the HAR study [15].