biomechanic analysis

What it does

Biomechanic Analysis turns human movement into an interpretable picture of muscle coordination. Instead of looking only at a joint angle or a single muscle trace, it examines muscle synergies: recurring groups of muscles that the nervous system recruits together to produce stable, efficient movement.

The project is designed as an exploratory analysis tool for biomechanics students, researchers, and clinicians. Given time-series muscle-activity data, it helps reveal which synergies are active, how strongly they contribute across a movement cycle, and where coordination changes may be meaningful.

Inspiration

Movement is richly coordinated, but most analysis workflows still leave people with dozens of separate EMG channels to interpret. We wanted a clearer bridge from raw signals to a human-centered explanation: what coordination strategy is the body using, and how does it change over time?

Muscle-synergy analysis offers that bridge by representing activation as a small set of reusable patterns. It makes complex motor-control data easier to compare across phases of motion, trials, and individuals.

How we built it

We built a workflow that organizes muscle-activity signals into a consistent analysis pipeline:

• prepare and normalize time-series muscle activity;
• decompose the signal into a compact set of synergy weightings and activation profiles;
• visualize each synergy and its contribution through the movement cycle; and
• surface side-by-side patterns that support comparison and interpretation.

The emphasis is on transparency: the output should make it easy to trace a visual insight back to the underlying signals instead of treating the model as a black box.

Challenges and lessons

The hardest part was balancing mathematical reduction with physiological meaning. A lower-dimensional representation can be elegant, but the chosen number of synergies, signal preprocessing, and trial variability all affect the result. We learned to treat the analysis as a decision-support tool—not a substitute for clinical judgment—and to make assumptions visible in the experience.

We also learned that good biomechanics communication matters as much as the algorithm. Clear labels, movement-cycle context, and intuitive visual comparison make a synergy result far more useful than a dense matrix alone.

What's next

Next, we want to support richer trial comparison, more robust quality checks, and interactive exploration of how muscle coordination changes with fatigue, rehabilitation, sport-specific technique, or altered movement patterns.

Built With

• biomechanics