A model-based motion capture marker location refinement approach using inverse kinematics from dynamic trials

Tracking motion kinematics and tremor with intrinsic oscillatory property of instrumental mechanics

Tracking kinematic details of motor behaviors is a foundation to study the neuronal mechanism and biology of motor control. However, most of the physiological motor behaviors and movement disorders, such as gait, balance, tremor, dystonia, and myoclonus, are highly dependent on the overall momentum of the whole-body movements. Therefore, tracking the targeted movement and overall momentum simultaneously is critical for motor control research, but it remains an unmet need. Here, we introduce the intrinsic oscillatory property (IOP), a fundamental mechanical principle of physics, as a method for motion tracking in a force plate. The overall kinetic energy of animal motions can be transformed into the oscillatory amplitudes at the designed IOP frequency of the force plate, while the target movement has its own frequency features and can be tracked simultaneously. Using action tremor as an example, we reported that force plate-based IOP approach has superior performance and reliability in detecting both tremor severity and tremor frequency, showing a lower level of coefficient of variation (CV) compared with video- and accelerometer-based motion tracking methods and their combination. Under the locomotor suppression effect of medications, therapeutic effects on tremor severity can still be quantified by dynamically adjusting the overall locomotor activity detected by IOP. We further validated IOP method in optogenetic-induced movements and natural movements, confirming that IOP can represent the intensity of general rhythmic and nonrhythmic movements, thus it can be generalized as a common approach to study kinematics.

Cost Function Determination for Human Lifting Motion via the Bilevel Optimization Technology

Investigating the optimal control strategy involved in human lifting motion can provide meritorious insights on designing and controlling wearable robotic devices to release human low-back pain and fatigue. However, determining the latent cost function regarding this motion remains challenging due to the complexities of the human central nervous system. Recently, it has been discovered that the underlying cost function of a biological motion can be identified from an inverse optimization control (IOC) issue, which can be handled via the bilevel optimization technology. Inspired by this discovery, this work is dedicated to studying the underlying cost function of human lifting tasks through the bilevel optimization technology. To this end, a nested bilevel optimization approach is developed by integrating particle swarm optimization (PSO) with the direction collocation (DC) method. The upper level optimizer leverages particle swarm optimization to optimize weighting parameters among different predefined performance criteria in the cost function while minimizing the kinematic error between the experimental data and the result predicted by the lower level optimizer. The lower level optimizer implements the direction collocation method to predict human kinematic and dynamic information based on the human musculoskeletal model inserted into OpenSim. Following after a benchmark study, the developed method is evaluated by experimental tests on different subjects. The experimental results reveal that the proposed method is effective at finding the cost function of human lifting tasks. Thus, the proposed method could be regarded as a paramount alternative in the predictive simulation of human lifting motion.

Mechanical Parameters and Trajectory of Two Chinese Cervical Manipulations Compared by a Motion Capture System

Objective: To compare the mechanical parameters and trajectory while operating the oblique pulling manipulation and the cervical rotation–traction manipulation.

Methods: An experimental research measuring kinematics parameter and recording motion trajectories of two cervical manipulations were carried out. A total of 48 healthy volunteers participated in this study, who were randomly divided into two groups of 24 representing each of the two manipulations. A clinician performed two manipulations in two groups separately. A motion capture system was used to monitor and analyze kinematics parameters during the operation.

Results: The two cervical manipulations have similar thrust time, displacement, mean velocity, max velocity, and max acceleration. There were no significant differences in active and passive amplitudes between the two cervical rotation manipulations. The thrust amplitudes of the oblique pulling manipulation and the cervical rotation–traction manipulation were 5.735 ± 3.041° and 2.142 ± 1.742°, respectively. The thrust amplitudes of the oblique pulling manipulation was significantly greater than that of the cervical rotation–traction manipulation (P < 0.001).

Conclusion: Compared with the oblique pulling manipulation, the cervical rotation–traction manipulation has a less thrust amplitudes.