Why correct calculations can still lead to weak explanations
Students can lose marks in biomechanics evaluations not because they failed to use the correct equation or didn’t understand the method for solving the problem, but because they failed to check that their answer or conclusions made sense when they reported, for example, that the runner’s velocity was 1200 m/s. That answer arose due to a minor calculation error, but the intuitive understanding of how people move was lacking in the student’s thought process. This article discusses how to develop biomechanical explanations that are faithful to the empirical nature of the science, and how to avoid mistakes in the future.
In biomechanics, it is often tempting to move quickly from what we measure to why a movement occurred. Numbers feel authoritative. Time-series plots suggest mechanisms. If a variable changes, it must have caused what we observed.
That intuition is understandable. It can often be incorrect.
Why Causation Is So Tempting
Human reasoning is naturally causal. We look for agents, drivers, and origins. When something changes, we want to know what made it change.
Biomechanical data encourage this tendency. Joint moments increase and decrease. Ground reaction forces have definite magnitudes and directions. Muscle activity appears to switch on and off. These signals look as though they are telling a story.
The problem is not that students are careless. The problem is that biomechanics operates in systems where:
- Multiple structures contribute simultaneously,
- Many internal variables cannot be measured directly,
- Different mechanisms can produce the same observable outcome.
As a result, causal claims require more support than biomechanical measurements alone can usually provide.
Observation, Inference, and Explanation
A useful way to think about biomechanical reasoning is to separate it into three layers.
Observation
What was measured or computed? Examples include joint angles, ground reaction forces, joint moments, or EMG signals.
Inference
What must have been true for those observations to occur? This often involves mechanical constraints or consistency with known physical laws.
Explanation
Why did the system behave the way it did? This typically involves control strategies, intent, or underlying mechanisms.
Biomechanics is strongest at observation and inference. Explanation is possible, but it must be approached cautiously.
Most strong student answers live in the space between observation and inference. Many weak answers leap too quickly into explanation.
Variables Students Think They Understand (But Often Don’t)
Several commonly used biomechanical variables are particularly prone to over-interpretation.
Joint Moments
Joint moments describe the net mechanical effect of all forces acting at a joint. They do not identify which muscles produced those forces, nor how much each contributed.
A statement such as “the rectus femoris produced the knee extension moment” is often stronger than the data justify. A safer interpretation is that the observed joint moment is consistent with net knee extensor action.
Ground Reaction Forces
Ground reaction forces describe the interaction between the body and the environment. They are the sum of all the mass-acceleration products of the body segments during the time the subject was in contact with the ground. They do not, by themselves, explain how a person chose to move each of those segments.
Based on Newton’s 3rd Law, we can use the ground reaction forces as inverse dynamics equation inputs when exploring potential causes of human motion.
Electromyography (EMG)
EMG provides information about muscle activation, not muscle force. Activation does not map directly to force, particularly across different joint angles, contraction types, or fatigue states, and in the presence of co-contraction.
Treating EMG amplitude as a direct proxy for force is a common and understandable error. A clear way to understand the problem in doing this is to consider that muscles exert force when shortening, lengthening, or staying the same length. In each case, the EMG profile could not tell you which way the muscle was being utilized.
In each case, the variable is valuable, but only within a clearly defined interpretive scope.
Why “Which Muscle Did This?” Is Usually the Wrong Question
Students often want biomechanics to identify the specific muscle responsible for a movement. This question reflects a desire for clarity, but it assumes a level of determinacy that rarely exists.
Human movement is mechanically redundant. Multiple muscles can contribute to the same joint action, and many muscles act at more than one joint. Different coordination patterns can produce similar outcomes. Inverse problems are often underdetermined, meaning more than one solution is mathematically indeterminant and physiologically plausible.
As a result, biomechanics more often tells us:
- Which actions were required,
- Which contributions are possible or consistent,
- Which interpretations are unlikely,
rather than identifying a single responsible structure.
What Biomechanics Is Actually Good At Explaining
Recognizing limits does not weaken biomechanics. It clarifies its strengths.
Biomechanics excels at:
- identifying constraints on movement,
- revealing trade-offs between mechanisms,
- determining what must accompany an observed behaviour,
- ruling out mechanically inconsistent explanations.
These are powerful insights. They allow us to narrow the space of plausible explanations, even when definitive causation cannot be established.
Strong explanations are careful not because the analyst is uncertain, but because the system itself is complex.
Where This Leads
The distinction between description, inference, and explanation underpins everything that follows in biomechanics. It shapes how joint moments are interpreted, how muscle activity is discussed, and how results are communicated in both exams and research.
The next step is to examine the tools that formalize these interpretations: models. Models make assumptions explicit, impose structure on complexity, and create the appearance of precision.
In the next Foundations article, we examine why models are indispensable, why they are never complete, and why apparent clarity should always be treated with caution.