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Notes on Measuring Motor Performance

Performance measures in motor skills

  • Learning aim in this lecture: understand two broad categorizations of performance measures and how to measure motor skill performance.
  • Two main categories:
    • Performance outcome measures: focus on the observable outcome of performing a skill.
    • Performance production measures: focus on what the body does during the performance (nervous system, muscles, skeleton) to produce the movement.
  • Other topics covered:
    • Measures of error in motor performance
    • Reaction time in movement and motor performance
    • Biomechanical variables (kinematics) and their use in motor skill contexts
    • Brief mention of electromyography (EMG) and muscle activation
    • Connections to biomechanics knowledge and practical assessment tools

What is performance in this context?

  • Performance = observable or measurable behavior of performing a skill (the observable component or outcome).
  • Good measures of motor performance should have several qualities:
    • Validity: the measure actually assesses what it is intended to measure.
    • Reliability: consistency of the measure across occasions or raters.
    • Objectivity: degree to which the measure is quantifiable and not dependent on subjective judgment; more objective = more numerical.
    • Feasibility: ease of setup and use; measures should not be overly complex or burdensome.

Performance outcome measures vs. performance production measures

  • Performance outcome measures:
    • Relate to the outcome or result of performing a skill.
    • Examples: time to complete a task, reaction time, amount of error, number of successful attempts (e.g., goals scored), time on/off a target, time on balance.
  • Performance production measures:
    • Relate to what the body actually does to perform the skill.
    • Examples include:
    • Displacement measures of joints or body segments (how far something moved).
    • Velocity and acceleration of body segments.
    • Electromyography (EMG) to measure muscle activation timing and magnitude (broader category alongside kinematics).
    • Note: EMG and other measures (EEG, PET, MRI) exist but are not the focus of this unit; more complex techniques are acknowledged but not detailed here.

Common variables used to measure motor skills

  • Broad categories discussed:
    • Error measures
    • Kinematic variables (displacement, velocity, acceleration)
    • Muscle activation (EMG) measures
  • Key idea: combine these measures to quantify performance from multiple angles.

Reaction time and its types

  • Definition: RT is the time between the onset of a stimulus and the initiation of the response.
    • Formal representation: if the stimulus occurs at time t{stimulus} and the response initiates at t{init}, then
      RT = t{init} - t{stimulus}.
  • Movement time (MT): the time from response initiation to response termination.
    • If the movement finishes at t{terminate}, then MT = t{terminate} - t_{init}.
  • Overall response time (often called total response time):
    RT_{total} = RT + MT.
  • Simple reaction time (RT): one signal, one response; e.g., see red light and press a single key.
  • Choice reaction time: multiple possible signals and different responses; more processing time is typically required.
  • Discrimination reaction time: more than one signal, but only one of them requires a response; requires detection and inhibition of non-relevant signals.
  • Practical implication: generally, faster RT and MT correlate with better skill performance, but context matters (e.g., accuracy vs speed trade-offs).

Error measures in motor performance

  • Error is the difference between actual performance and the target or goal value.
  • Error can be spatial, temporal, or a combination of both.
  • Types of error:
    • Absolute error (AE): magnitude of error irrespective of direction.
    • Example: if target is a bullseye and your hit is off-center, AE = |e| where e = P - T.
    • So, AE reflects how far away the performance is from the target.
    • Constant error (CE): considers the direction of error (bias);
    • CE =
      ar{e} = rac{1}{N} rac{ ext{sum of all } e_i}{N}
    • Indicates bias toward a side (e.g., consistently overshooting to the right).
    • Variable error (VE): reflects variability or consistency across trials.
    • VE is the dispersion of errors around the mean error, often represented as the standard deviation of the errors:
      VE = ext{SD}(e) = rac{1}{N}igg[ rac{1}{N}
      igg( rac{1}{N} rac{}{}igg)igg]
    • In practice, VE = \sqrt{\frac{1}{N}\sum{i=1}^{N} (ei - \bar{e})^2}.
  • Use in labs/assessments: AE, CE, and VE provide a comprehensive picture of accuracy and consistency.

Kinematic measures of movement (production measures)

  • Kinematics describe motion without regard to the forces that produce it (as opposed to kinetics, which deals with forces).
  • The three broad types of kinematic data:
    • Displacement: change in spatial position of a body, limb, or joint.
    • Example: \Delta x = xf - xi.
    • Velocity: rate of change of displacement over time.
    • Example: v(t) = \frac{dx(t)}{dt}.
    • Acceleration: rate of change of velocity over time.
    • Example: a(t) = \frac{dv(t)}{dt} = \frac{d^2x(t)}{dt^2}.
  • These measures can be captured using precise instrumentation (motion capture, force plates, etc.) or via observational tools that categorize movement at a practical level.
  • Non-quantitative kinematic assessment examples discussed:
    • Functional Movement Screen (FMS): a tool to assess movement patterns and identify dysfunctional or performance-limiting movement patterns.
    • FMS structure:
    • Seven tests in total; example focus on the squat assessment.
    • Each test has criteria and a rating on a scale (0–3):
      • 0 indicates pain.
      • 1–3 reflect degrees of achieving the described criteria (e.g., alignment, form, symmetry).
    • Observational, feasible method requiring trained raters; rapid screening rather than full biomechanical analysis.
  • Fundamental Motor Skills (FMS framework): developmentally important skills grouped into three components:
    • Stability (balance-related): bending, twisting, swaying, etc.
    • Locomotive: crawling, walking, running, hopping, jumping, leaping, etc.
    • Manipulative: throwing, catching, kicking, striking, etc.
  • Assessment tool example: Test of Gross Motor Development (TGMD) to evaluate fundamental motor skills in children.
    • Locomotor skills (six examples in TGMD context here): gallop, run, horizontal jump, hop, slide, leap.
    • Object control skills (six examples): striking a stationary object, catching, stationary dribble, overhand throw, kick, underhand roll.
    • Observational scoring: criteria for each skill; each criterion may be scored as 1 (criterion met) or 0 (not met); total score per skill reflects performance quality.
    • Training and reliability of raters are important for consistent scoring.
  • Related observation tools mentioned:
    • The TGMD framework (or similar tools) focuses on population and context-specific applicability (e.g., pediatric rehabilitation, exercise/sports science).
    • Criteria-based scoring enables efficient, feasible assessment without high-end equipment.

Electromyography (EMG) and muscle activation

  • EMG measures skeletal muscle activation (timing and amplitude) and provides information about how muscles coordinate during movement.
  • What EMG tells us:
    • Magnitude of muscle activation (how much a muscle is activated).
    • Timing of activation (onset, duration, and phasing relative to movement).
    • Co-activation patterns: simultaneous activation of agonist and antagonist muscles across a joint, contributing to joint stability.
  • Coactivation considerations:
    • Can be beneficial for stability and control in some movements.
    • In other situations, excessive coactivation might reduce movement efficiency; optimal coordination often involves synergistic, not excessive, coactivation.
  • Practical implications of EMG in research and practice:
    • Potential indicators of motor disorders or atypical motor control (not a diagnostic tool by itself).
    • Changes with aging and fatigue can be detected via EMG signal changes.
    • Can assess training effects by comparing muscle activation patterns before and after interventions.
  • Example visualization: EMG traces across muscles during a movement (e.g., stand-to-sit or sit-to-stand) and across gait cycles.
    • Illustrates timing and relative activation of multiple muscles (e.g., vastus medialis, vastus lateralis, rectus femoris) and how they coordinate to control knee motion.
  • Note: The detailed interpretation of EMG requires context (movement phase, muscle roles, and normalization of EMG signals) and is not presented in full depth here.

Practical and conceptual implications

  • Two broad categories of measures offer complementary views:
    • Outcome measures provide a straightforward summary of performance success or error.
    • Production measures reveal the mechanics and control processes underlying the performance.
  • Observational methods (e.g., FMS and TGMD) can be highly feasible and informative when administered by trained assessors, but rely on observer judgment and training.
  • EMG adds a different layer by revealing muscle activation patterns, timing, and coordination, informing our understanding of motor control and the effects of aging, fatigue, or training.
  • Ethical and practical considerations:
    • EMG data interpretation should consider variability across individuals and contexts;
    • Stated purpose: EMG helps understand motor control, not to diagnose conditions alone;
    • Training and reliability of raters for observational tools are essential to ensure fair assessment.
  • Connection to previous coursework:
    • Concepts from biomechanics (kinematics vs kinetics) underlie kinematic measures.
    • Knowledge of muscle activation and muscle groups informs interpretation of EMG data during movement tasks.

Summary for exam preparation

  • Performance in motor control is about observable behavior and its measurement via two main categories: outcome and production.
  • Good measures must be valid, reliable, objective, and feasible.
  • Reaction time consists of RT and MT, with RT_total = RT + MT; RT can be simple, choice, or discrimination depending on the number and type of stimuli.
  • Error measures include AE (magnitude only), CE (bias with direction), and VE (consistency). They provide a comprehensive picture of accuracy and reliability of performance.
  • Kinematic (production) measures include displacement, velocity, and acceleration; they can be obtained via precise instrumentation or through observational scales like FMS and TGMD.
  • FMS and TGMD provide practical, observational assessment tools to gauge fundamental movement competencies and development.
  • EMG provides insight into muscle activation timing and magnitude, revealing coordination patterns and potential issues related to aging, fatigue, or training adaptations.
  • Practical takeaway: combine outcome, production, and EMG measures to fully characterize motor performance and guide training or rehabilitation plans.