###### Abstract  
The accurate and efficient recognition of gym workout activities using wearable sensors holds significant implications for assessing fitness levels, tailoring personalized training regimens, and overseeing rehabilitation progress. This study introduces CNN-ResBiGRU, a novel deep learning architecture that amalgamates residual and hybrid methodologies, aiming to precisely categorize gym exercises based on multimodal sensor data. The primary goal of this model is to effectively identify various gym workouts by integrating convolutional neural networks, residual connections, and bidirectional gated recurrent units. Raw electromyography and inertial measurement unit data collected from wearable sensors worn by individuals during strength training and gym sessions serve as inputs for the CNN-ResBiGRU model. Initially, convolutional neural network layers are employed to extract unique features in both temporal and spatial dimensions, capturing localized patterns within the sensor outputs. Subsequently, the extracted features are fed into the ResBiGRU component, leveraging residual connections and bidirectional processing to capture the exercise activities' long-term temporal dependencies and contextual information. The performance of the proposed model is evaluated using the Myogym dataset, comprising data from 10 participants engaged in 30 distinct gym activities. The model achieves a classification accuracy of 97.29% and an F1-score of 92.68%. Ablation studies confirm the effectiveness of the convolutional neural network and ResBiGRU components. The proposed hybrid model uses wearable multimodal sensor data to accurately and efficiently recognize gym exercise activity. deep learning; gym exercise activity; human activity recognition; electromyography; IMU sensor +
Footnote †: journal: Medical  
from the various types of fitness equipment, movement speeds, and complex muscle engagement associated with these exercises. Accurate and efficient recognition of gym workout activities is practical in fitness tracking, rehabilitation, sports coaching, and related domains [7]. The growing adoption of wearable devices like IMUs and EMG sensors has opened up opportunities for the employment of sensor-driven gym exercise identification to offer personalized feedback and real-time self-tracking data [8]. However, categorizing gym exercises poses challenges due to the diverse array of encountered equipment, movement styles, and intensity levels. Recent research has explored various machine learning techniques to detect exercise activities using sensors. These methods encompass traditional methods, as well as modern deep learning models. Past approaches relied on manually chosen features extracted from sensor data and classifiers like support vector machines and random forests to classify exercises [9; 10]. However, the success of these methods depends heavily on domain expertise in engineering meaningful features. Deep learning has become increasingly common in automating and comprehensively categorizing time series data from sensors. This is accomplished by automatically generating robust hierarchical feature representations [11]. Convolutional neural networks (CNNs) excel at extracting consistent patterns from sensor data streams, both in spatial and temporal aspects [12]. As recurrent models, long short-term memory (LSTM) networks can grasp long-range temporal contexts in sequences of exercises [13]. Hybrid networks that combine CNNs and LSTMs have demonstrated remarkable accuracy in various domains, such as recognizing postures and detecting gestures, mainly when applied to multimodal data collected from wearable devices [14]. This investigation focuses on tailored deep learning approaches specifically crafted for identifying multimodal gym workout activities, leveraging insights from prior accomplishments.