Real-time inverse kinematics for the upper limb: a model-based algorithm using segment orientations

Abstract

BackgroundModel based analysis of human upper limb movements has key importance in understanding the motor control processes of our nervous system. Various simulation software packages have been developed over the years to perform model based analysis. These packages provide computationally intensive—and therefore off-line—solutions to calculate the anatomical joint angles from motion captured raw measurement data (also referred as inverse kinematics). In addition, recent developments in inertial motion sensing technology show that it may replace large, immobile and expensive optical systems with small, mobile and cheaper solutions in cases when a laboratory-free measurement setup is needed. The objective of the presented work is to extend the workflow of measurement and analysis of human arm movements with an algorithm that allows accurate and real-time estimation of anatomical joint angles for a widely used OpenSim upper limb kinematic model when inertial sensors are used for movement recording.MethodsThe internal structure of the selected upper limb model is analyzed and used as the underlying platform for the development of the proposed algorithm. Based on this structure, a prototype marker set is constructed that facilitates the reconstruction of model-based joint angles using orientation data directly available from inertial measurement systems. The mathematical formulation of the reconstruction algorithm is presented along with the validation of the algorithm on various platforms, including embedded environments.ResultsExecution performance tables of the proposed algorithm show significant improvement on all tested platforms. Compared to OpenSim’s Inverse Kinematics tool 50–15,000x speedup is achieved while maintaining numerical accuracy.ConclusionsThe proposed algorithm is capable of real-time reconstruction of standardized anatomical joint angles even in embedded environments, establishing a new way for complex applications to take advantage of accurate and fast model-based inverse kinematics calculations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-016-0291-x) contains supplementary material, which is available to authorized users.

Performance Analysis of the PMD Camboard Picoflexx Time-of-Flight Camera for Markerless Motion Capture Applications

“…In addition, a lot of effort is being made to develop small, mobile, and wireless solutions for markerless human motion capture, in cases, when a laboratory-free measurement setup is needed (see, for example, the use of inertial motion sensing technology for upper limb motion modeling in [27]). A very interesting example deals with the assisted gait of exoskeleton users, where the use of crutches is mandatory to allow the user to walk [28].…”

A Continuous Estimation Model of Upper Limb Joint Angles by Using Surface Electromyography and Deep Learning Method

“…Current approaches for sEMG based on continuous motion estimation mainly focus on physiological muscle models and neural network models. Physiological model-based systems include kinematic models [11], dynamic models [12] and musculoskeletal models [13]. The HILL muscle model is the most widely used.…”

Non-iterative geometric approach for inverse kinematics of redundant lead-module in a radiosurgical snake-like robot

“…This configuration is redundant, and existing methods cannot simultaneously solve its IK timely and accurately. Point virtualization is useful in solving IK of robotic models [ 36 , 37 ], and collision avoidance with surrounding organs [ 38 ]. This serves as the basis of the geometric method we proposed in this study.…”