Electromyography (EMG) is a powerful tool for capturing the electrical activity of muscles during movement. When applied to the upper limb, EMG allows researchers and clinicians to decode muscle activation patterns associated with specific functional tasks, such as grasping, reaching, or rotating the forearm. By analyzing multiple EMG channels placed across relevant muscles (flexor carpi radialis, extensor carpi radialis longus, etc), it us possible to classify certain upper limb movements with accuracy using machine learning and pattern recognition techniques.
Accurately classifying upper limb movements using EMG is essential for the development of intelligent prosthetic arms and exoskeletons. These assistive devices rely on EMG signals to interpret a user’s intended actions, enabling more natural and responsive control, particularly for individuals with limb loss or paralysis. Beyond prosthetics, EMG-based movement classification plays a crucial role in rehabilitation. In cases of stroke or spinal cord injury, it allows clinicians to objectively monitor motor recovery over time and deliver real-time biofeedback during therapy, ultimately supporting more effective and personalized treatment.
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Classifying lower limb movements using acceleration data is increasingly important in fields such as rehabilitation, sports science, and wearable technology. By analyzing signals from inertial measurement units (IMUs) or accelerometers, it is possible to distinguish between walking, running, sitting, standing, or stair climbing with accuracy.
This information can be used to monitor gait patterns in real-world environments, providing continuous and objective assessment for patients recovering from orthopedic surgeries, neurological disorders, or lower limb injuries. In sports and fitness, it enables precise tracking of performance and fatigue. Additionally, in fall detection systems for the elderly, real-time classification of abnormal movement patterns using acceleration data can trigger early warnings and improve safety outcomes.
| notebook | description |
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| preprocessing & EDA | preprocessing & EDA |
| model training | modeling training |