Proprioceptive Timing Performance in Stroke Patients and Healthy Controls

28 individuals with chronic stroke and 28 age-matched healthy controls completed a dynamic proprioceptive timing assessment using a Kinarm Exoskeleton robot. The dataset, created by Devin Austin and last updated in May 2026, quantifies proprioceptive timing as the mean and standard deviation of Timing Error. This data supports the article titled 'Disrupted Proprioceptive Timing Estimates Contribute to Upper Limb Motor Impairment in Chronic Stroke'.

Use Cases

• Compare proprioceptive timing error between stroke patients and healthy controls based on the described assessment task.
• Analyze the relationship between timing error variability and upper limb motor impairment in chronic stroke.
• Validate robotic assessment protocols for proprioception based on data from the Kinarm Exoskeleton.
• Train models to classify or predict motor impairment severity based on proprioceptive timing metrics.

Strengths

• Includes data from a matched cohort of 28 stroke patients and 28 healthy controls.
• Proprioceptive timing is quantified using a standardized robotic assessment (Kinarm Exoskeleton).
• Dataset is associated with a specific, peer-reviewed research article, providing context.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The sample size of 56 participants may limit statistical power for some analyses.

Provenance

Source

Devin Austin via figshare

Collection Method

Data collected from a robotic assessment task where participants' limbs were passively moved and they indicated perceived hand position.

Freshness

Last updated 2026-05-07 17:48:09; freshness should be verified.