HBM can be applied in Diagnostics, Rehabilitation, Fundamental and applied research, sports, ergonomics and various other fields. Some of the applications are:

Diagnostics:

• Balance compensation anomalies and related postural stability problems.
• Force related postural stability problems.
• Gait problems arising from inefficient muscle usage.
• Early identification of degenerative muscular conditions and dysfunctions.
• Muscle force distribution visualization for detection of core stability problems.
• Means of identifying neural substrates of task difficulty and cognitive effort.
• Muscle recruitment and control in hemiparesis, TBI and neglect patients.
• Early identification of dystonia, possibly due to cp (involuntary contractions).
• Early detection of muscular dystrophies. (examples: ALS, MS)
• Understanding balance compensation strategies and control mechanisms.
• Musculoskeletal systems behavior.
• Explorations of the muscle force interactions with spatial environments.
• Better understanding of sensory inputs & motors in posture and motion.
• Muscle imbalance in athletes with recurrent injuries.
• Identification of technique inefficiencies and performance inhibitors.

Rehabilitation:

• Appropriate postural adjustments.
• Well timed protective extension, reaction to perturbations.
• Improving control of weak muscles, timing of muscle groups activity.
• Muscle coordination and strength with amputees and joint replacements.
• Rehabilitation and training of Osteoarthritis patients.
• Training for core stability control and improvement.
• Neuro rehabilitation (Spinal cord injuries and diseases, paresis, TBI, neglect).
• Control of dystonic movements.
• Prosthetics fitting and dynamic alignment.
• Post-surgical knee rehabilitation, ACL reconstruction + Squat exercises.
• Hamstrings/quad ratio assessment and training.
• Rehabilitation of athletes with recurrent injuries.

The computational pipeline that results in real time muscle force display is flexible and allows forward dynamics simulations to be run at any time during runtime of the system. The flow of movements as an input to the inverse dynamics simulation is stopped during a sequence and the calculated joint movements are now used as input, while the movements become output. Thus forward simulations calculate movements and reaction forces from moments of force produced around the joints of the subjects.

Flowchart

Input from the motion capture in the form of 3D marker coordinates is used as input for the Kinematics Solver. The Kinematics solver is also using resource files of a skeleton definition and marker set templates. The Kinematics Solver is outputting in real-time the current skeleton pose, the deltas of velocities and deltas of acceleration that are used as input to the Motion Equations. The Kinematics Solver also sends out Muscle paths for all respective muscles, and outputs the schematic skeleton used for the visualization. The Motion Equations are also using input from ground reaction forces and other external forces coming from an array of Force sensors. The Motion Equations also use an input from resource files that contain the respective body mass properties. The Equations of Motion output Joint moments to the Optimization process, The Optimization process also uses input of muscle lengths and moment arms coming from the respective muscle paths. The Optimization process outputs Muscle forces used in the Real Time muscle force visualization.

HBM biomechanics skeleton

The HBM biomechanics skeleton is designed for the purpose of studying muscle function and producing validated estimates of muscle force.  The skeleton has all kinematic degrees of freedom (DOF) that are controlled by muscles, i.e. for which muscles have significant moment arms.  This leads to some important differences with traditional analysis protocols, where each joint is typically modeled with three cardanic rotations.  Rather we are concerned with the criterion that the skeleton accurately represents the movement of body mass, and the joints of the model that are working against ground reaction forces (lower extremity) are able to track the subject’s joints, avoiding registration errors between joint centers and measured ground reaction force vectors.