Abstract
The present contribution focuses on the estimation of the Cartesian kinematic jerk of the hips’ orientation during a full three-dimensional movement in the context of enabling eHealth applications of advanced mathematical signal analysis. The kinematic jerk index is estimated on the basis of gyroscopic signals acquired offline through a smartphone. A specific free mobile application is used to acquire the gyroscopic signals and to transmit them to a personal computer through a wireless network. The personal computer elaborates the acquired data and returns the kinematic jerk index associated with a motor task. A comparison of the kinematic jerk index value on a number of data sets confirms that such index can be used to evaluate the fluency of hips orientation during motion. The present research confirms that the proposed gyroscopic data acquisition/processing setup constitutes an inexpensive and portable solution to motion fluency analysis. The proposed data-acquisition and data-processing setup may serve as a supporting eHealth technology in clinical bio-mechanics as well as in sports science.
Highlights
Complex body tasks, such as rock climbing, involve the alternation of periods dedicated to postural regulation and of quadruped displacement
As a preliminary test of the described acquisition procedure and of the kinematic jerk-estimation scripts, three different kinds of gyroscopic signals were acquired during three complex body tasks: walking, running and jumping
The present research work deals with a dedicated acquisition procedure to evaluate hip orientation fluency during a complex body motion task
Summary
Complex body tasks, such as rock climbing, involve the alternation of periods dedicated to postural regulation and of quadruped displacement. The aim of the present contribution is to illustrate the details of smartphone-based acquisition systems to evaluate hip rotation fluency by estimating the kinematic jerk of the acquired signals. A specific M ATLABr software (made by the authors) that is able to read the data stream from a wireless network and to compute the kinematic jerk index associated with a body movement session, with a minimal amount of user setting effort. J = Cj*DT*sum(sqrt(sum(je.^2,1))); The M ATLABr function displayed in Listing 2 inputs an array R of size 3 × 3 × N and a sampling period DT and returns the numerical estimate of the Cartesian kinematic jerk index (9). Note that at Line 9 the operator real was introduced to get rid of the imaginary part of the matrix logarithm, that, in principle, should be equal to zero, while, in practice, might slightly differ from zero because of finite-precision machine calculations
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