<![CDATA[The human hand is a complex and functionally significant anatomical structure, playing a critical role in daily activities, communication, and professional tasks. Any impairment due to injury, neurological disorders, or musculoskeletal diseases can severely affect an individual's quality of life. Conditions such as stroke-induced hemiparesis, arthritis, carpal tunnel syndrome, and tendon injuries often necessitate rehabilitation to restore function, minimize pain, and prevent secondary complications. Traditional rehabilitation approaches, while beneficial, generally follow a standardized methodology, failing to account for individual variations in muscle strength, neuroplasticity, and adaptive capacity.Modern rehabilitation methods leverage advanced technologies such as electromyography (EMG) and hand grip force measurement to enhance therapy effectiveness. Additionally, artificial intelligence (AI) applications, particularly Long Short-Term Memory (LSTM) networks and Transformer models, have emerged as promising tools for personalized rehabilitation. These models analyze EMG signals to predict hand movement intentions, enabling adaptive rehabilitation strategies tailored to individual needs. This study focuses on the construction of a real-time EMG signal acquisition system and uses them as input to LSTM and Transformer models to compare and analyze the performance of the two types of models. By demonstrating the superiority of applying AI for personalization over the general AI approach, this study highlights the potential of AI in hand rehabilitation in particular and healthcare in general with its ability to specialize for each individual patient.]]>
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