This dataset contains raw and processed accelerometer signals recorded during the performance of the Heel Rise Test, a clinical assessment commonly used to evaluate calf muscle strength, endurance, and functional performance. Wearable sensors were positioned on specific anatomical landmarks to capture tri-axial acceleration data throughout the test. The recordings include both the upward and downward phases of each heel rise cycle. Data were collected from participants performing repeated heel rises at a self-selected pace until fatigue or until completing a predefined number of repetitions. Along with accelerometry, the dataset includes basic metadata for each trial such as sampling rate, sensor placement, participant identifier codes, and trial number. No personally identifiable information is included. The dataset may support research in biomechanics, movement analysis, rehabilitation, strength assessment, and algorithm development for automated detection of heel rise cycles or muscular performance metrics. Researchers can use the signals for feature extraction, signal processing validation, and machine learning approaches aimed at functional assessment.

This dataset contains raw and processed accelerometer signals recorded during the performance of the Heel Rise Test, a clinical assessment commonly used to evaluate calf muscle strength, endurance, and functional performance. Wearable sensors were positioned on specific anatomical landmarks to capture tri-axial acceleration data throughout the test. The recordings include both the upward and downward phases of each heel rise cycle. Data were collected from participants performing repeated heel rises at a self-selected pace until fatigue or until completing a predefined number of repetitions. Along with accelerometry, the dataset includes basic metadata for each trial such as sampling rate, sensor placement, participant identifier codes, and trial number. No personally identifiable information is included. The dataset may support research in biomechanics, movement analysis, rehabilitation, strength assessment, and algorithm development for automated detection of heel rise cycles or muscular performance metrics. Researchers can use the signals for feature extraction, signal processing validation, and machine learning approaches aimed at functional assessment.

This dataset contains raw and processed accelerometer signals recorded during the performance of the Heel Rise Test, a clinical assessment commonly used to evaluate calf muscle strength, endurance, and functional performance. Wearable sensors were positioned on specific anatomical landmarks to capture tri-axial acceleration data throughout the test. The recordings include both the upward and downward phases of each heel rise cycle. Data were collected from participants performing repeated heel rises at a self-selected pace until fatigue or until completing a predefined number of repetitions. Along with accelerometry, the dataset includes basic metadata for each trial such as sampling rate, sensor placement, participant identifier codes, and trial number. No personally identifiable information is included. The dataset may support research in biomechanics, movement analysis, rehabilitation, strength assessment, and algorithm development for automated detection of heel rise cycles or muscular performance metrics. Researchers can use the signals for feature extraction, signal processing validation, and machine learning approaches aimed at functional assessment.

This dataset contains raw and processed accelerometer signals recorded during the performance of the Heel Rise Test, a clinical assessment commonly used to evaluate calf muscle strength, endurance, and functional performance. Wearable sensors were positioned on specific anatomical landmarks to capture tri-axial acceleration data throughout the test. The recordings include both the upward and downward phases of each heel rise cycle. Data were collected from participants performing repeated heel rises at a self-selected pace until fatigue or until completing a predefined number of repetitions. Along with accelerometry, the dataset includes basic metadata for each trial such as sampling rate, sensor placement, participant identifier codes, and trial number. No personally identifiable information is included. The dataset may support research in biomechanics, movement analysis, rehabilitation, strength assessment, and algorithm development for automated detection of heel rise cycles or muscular performance metrics. Researchers can use the signals for feature extraction, signal processing validation, and machine learning approaches aimed at functional assessment.