## 1 Introduction  
Modern healthcare trends are riding the rails of digital transformation, with a growing reliance on information technology, wearable devices, and artificial intelligence (AI) [1, 2, 3]. A key driver of medical innovation is stroke, which places significant strain on healthcare systems as rehabilitation demands rise to address motor impairments and loss of functional independence. Current data indicate that about one-third of stroke survivors experience residual motor and sensory deficits [3, 4, 5], a burden that has grown since the COVID-19 pandemic [1, 4]. The post-stroke period is particularly critical, as the recovery window is typically confined to the first weeks and months following the event [5]. In addition, clinical manifestations such as post-stroke depression and cognitive impairment further complicate recovery for patients, their families, and healthcare professionals [1, 2, 4, 5]. Particular attention is given to upper extremity function, especially hand dexterity, because it is susceptible to loss after a stroke [2, 5, 6]. This is important because the hands are essential for most activities of daily living (ADLs) and play a crucial role in determining an individual's physical function and social participation [4, 5, 6]. These multifaceted challenges, which often reduce patients' tolerance for conventional physical therapy, have been partially addressed through broad-spectrum neuro-rehabilitation strategies that rely on biosignal processing and aim to increase patient engagement [2, 7, 8]. However, many of these tools come with inherent limitations, such as high cost, lack of portability, and the need for close medical supervision. As a result, the current trend in biosignal processing is shifting towards decentralized rehabilitation, using mobile assistive technologies and AI, particularly deep learning (DL) and other machine learning (ML) algorithms [2, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]. In biomedical terms, DL is considered a subset of ML that utilizes multilayer artificial neural networks to analyze and interpret biosignal data from patients [9, 10]. Alongside accelerometer sensors, EMG sensors are predominantly used, preferred for their non-invasive nature, relevance to muscle activity, and ease of signal acquisition in both hospital and home settings [8, 15, 16]. Once acquired and processed, these signals can be used for remote diagnostics and aid recovery through feedback mechanisms, such as controlling exoskeletons or applying functional electrical stimulation (FES) [2, 3, 7, 8]. Deep neural networks (DNNs) have achieved promising results across various domains [9, 10], but in the field of EMG-based gesture recognition, only a few architectures are commonly employed: convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) [10, 12, 16]. These models have shown success, but their performance depends heavily on the amount of labeled data available for training [9, 19, 20]. CNNs are designed for spatial learning and are widely used in image classification (i.e., two-dimensional grid-organized image data), capturing hierarchical patterns through convolutional and pooling layers. In EMG signal processing, EMG signals are often transformed using wavelet transforms as an input for CNN-based myoelectrical decoding [19, 20]. More recently, one-dimensional CNNs have emerged as a distinct approach, better suited for time-series data and requiring less feature engineering. These 1D CNNs are being increasingly studied for EMG decoding across various applications [19]. For instance, [PERSON] et al. demonstrated the effectiveness of CNNs in classifying hand gestures using EMG signals from the NinaPro database, achieving accuracies comparable to or better than traditional ML classifiers [18]. However, most CNN studies have focused on healthy subjects or, to a lesser extent, on individuals in the chronic stage of stroke or amputees [11, 17, 18, 19].