Compara mètodes

	Cinemàtica directa	Dinàmica Inversa	Força de reacció conjunta	Anàlisi de sinergies musculars
Camp	Biomecànica	Biomecànica	Biomecànica	Biomecànica
Família	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	1986	1990	2001	1999
Autor original	John Craig	David Winter	Georg Bergmann	Marc Tresch
Tipus	Computational geometric pipeline	Computational analysis pipeline	Force analysis and joint loading	Dimensionality reduction and pattern extraction
Font seminal	Craig, J. J. (2005). Introduction to Robotics: Mechanics and Control (3rd ed.). Pearson.	Winter, D. A. (1990). Biomechanics and Motor Control of Human Movement. Wiley-Interscience.	Bergmann, G., Deuretzbacher, G., Heller, M., Graichen, F., Rohlmann, A., Strauss, J., & Duda, G. N. (2001). Hip forces and gait patterns from routine activities. Journal of Biomechanics, 34(7), 859-871.	Tresch, M. C., Saltiel, P., Bizzi, E., & Bizzi, E. (1999). The construction of movement by the spinal cord. Nature Neuroscience, 2(2), 162-167.
Àlies	FK, Kinematic chain, Anatomical chain	Inverse problem, Biomechanical inverse dynamics	Joint contact force, Tibiofemoral force, Joint loading	Motor synergy, Synergy extraction, Motor primitives
Relacionats	3	3	3	3
Resum	Forward kinematics is the calculation of the position and orientation of a distal body segment (such as the hand) based on the joint angles of proximal segments. Originally formalized in robotics by John Craig and adapted to biomechanics, it allows practitioners to predict endpoint location from known joint configuration.	Inverse dynamics is a biomechanical analysis technique that estimates the forces and moments acting on joints during movement by working backward from observed motion and ground reaction forces. Introduced by David Winter in the early 1990s, it is fundamental to understanding how muscles and joints generate and control human motion.	Joint reaction force (JRF) estimation calculates the contact forces transmitted across joints during movement using inverse dynamics combined with anatomical modeling. First validated in vivo by Bergmann and colleagues using instrumented hip implants, JRF estimation is essential for understanding joint degeneration, designing orthopedic implants, and assessing injury risk.	Muscle synergy analysis decomposes complex motor behavior into a small set of coactivated muscle groups (synergies or motor primitives). Pioneered by Marc Tresch and colleagues studying frog motor control, this approach reveals how the nervous system simplifies the control of many muscles by organizing them into task-relevant combinations.