A Novel Procrustes Analysis Method to Quantify Multi-Joint Coordination of the Upper Extremity after Stroke

Area of Science:

• Biomechanics
• Rehabilitation Science
• Medical Imaging and Data Analysis

Background:

• Upper extremity motor impairment is common post-stroke, affecting 80% of individuals.
• Upper limb kinematic assessments are crucial for stroke rehabilitation outcome measures.
• A standardized scale for categorizing multi-joint upper extremity movement is currently lacking.

Purpose of the Study:

• To introduce a modified Procrustes statistical shape method for quantitative analysis of upper extremity movement.
• To evaluate multi-joint coordination during movement progression, not just discrete kinematic values.
• To develop a universal quantitative tool for stroke rehabilitation.

Main Methods:

• A modified Procrustes statistical shape analysis was applied to motion capture data.
• Able-bodied and impaired upper extremity movements were analyzed.
• Coordination patterns between limb segments (hand, forearm, shoulder, elbow) were assessed.

Main Results:

• In able-bodied individuals, hand and forearm coordination is high during movement initiation, while shoulder and elbow coordination peaks during completion.
• In stroke survivors, coordination between the hand and forearm is disrupted during arm deceleration.
• The method identified specific segments of movement where coordination is impaired.

Conclusions:

• The modified Procrustes shape analysis offers a quantitative method to recognize multi-joint coordination in upper extremity movement.
• This approach can objectively identify deficits and recovery in patients with movement disorders.
• The method is applicable to existing motion capture data without altering treatments or increasing patient burden.