FLEXOR: A support tool for efficient and seamless experiment data processing to evaluate musculo-articular stiffness
Introduction
Free vibration techniques for evaluating the responsiveness of a 1-degree-of-freedom dynamic damped system of the musculo-articular stiffness (MAS) of the plantar flexor muscles have become validated procedures and are widely contrasted in the scientific literature [1], [2], [3], [4]. This procedure models the observed mechanical response of the main muscle-tendon unit (MTU) involved in the plantar flexor movement [5]. The MAS is associated with the evaluation of the functional capacity linked to the physical activity of a subject, and its interpretation has multiple applications, such as sports performance measurement and clinical applications (e.g., normative values of clinical populations [9], [10] and the risk factor of musculoskeletal injuries in lower limbs [11]). However, the applicability and widespread use of MAS has been limited thus far due to different issues. On the one hand, the measurement of this parameter requires a non-invasive, non-stress laboratory test, which implies the use of appropriate in-lab infrastructure, ad hoc mechanical devices and specific signal sensing units and devices [12]. In addition, it requires efficient treatment and processing of the signal data obtained from laboratory studies through different signal filtering techniques, mathematical transformations and the use of several software systems and tools. Nevertheless, and to the best of the authors’ knowledge, there do not exist specific tools to support analysts throughout the signal and analysis procedure.
The absence of suitable software systems (e.g. all-in-one frameworks) to support the entire process results in the use of different, ad hoc and mutually independent software tools for each stage in the signal processing (signal capturing, signal filtering, and signal transformation, as well as signal manipulation). That is, for each one of such purposes, a specific software tool (e.g., LabVIEW, Mathematica, SPSS) is used in a standalone manner.
The lack of integration between such tools, due to interoperability issues, represents a severe handicap for experiment manipulation. This problem also arises in other domains where the proprietary and non-open nature of some of the most popular software solutions hinder the use of their functionalities to foster interoperability between applications [13], [14]. Thus, the definition of downstream signal analysis processes becomes affected since the translation of the output (e.g., files or byte streams) from different tools has to be done manually. As a matter of fact, the processing of data of one subject lasts around one or two hours on average. The situation with a sample procedure for the case of plantar flexor MAS measurement, where different software tools are needed throughout the analysis procedure (LabVIEW, Mathematica, and custom database software) can be observed in the next diagram (see Fig. 1).
Signal analysts, physicians and sport-medicine practitioners interested in analysing MAS properties require automated tools that provide integrated suites of signal-processing functionalities to speed up the process in general, but also functionalities to easily configure and adjust ad-hoc signal parameters.
The aim is to observe the results in real-time because, normally, part of the processing and analysis of signals consists of obtaining a function curve that has been approximated by means of numerical methods. Thus, it is often necessary to try different adjustment parameters and check the results in real-time (i.e., as they are tested), as in a simulation setting.
Otherwise, setting different adjustments and parameters entails going from one software application to another or the explicit relaunch of codified subroutines. Then, this type of analysis becomes impracticable or even unfeasible, besides incredibly cumbersome. Let us consider the burden that managing different files across different software tools by hand would imply. Again, without appropriate automated technical support, this task is rather time-consuming and error-prone [15], [16].
In the present paper, FLEXOR is presented as an integrated, seamless, software solution that implements several procedures, such as signal processing, data management methods, algorithms and flexible functionalities, to support different adjustments that empower physicians and sport-medicine practitioners to observe and measure the responsiveness (i.e., MAS) of plantar flexor muscle-tendon units (MTU) while enabling trial-and-error experimentation with different adjustment parameters and without having to move between different software applications.
In this sense, FLEXOR improves the efficiency and effectiveness of the MAS analysis procedure, integrating all phases of the acquisition, processing and post-processing of the signal received from sensing devices in a timely manner. Likewise, FLEXOR automates certain tasks that were performed manually in the past, such as the selection of appropriate data set intervals for subsequent analysis. In this sense, FLEXOR provides the following clear benefits:
- 1.
Simplification and time reduction of experimentation procedures.
- 2.
Quick checks of curve adjustments and parameterization on the fly.
- 3.
Simplification of experiment management and reduction of the number of errors due to human mistakes.
- 4.
Improved interoperability with potential third-party software analysis and applications through data representation for experimentation samples in a technology-independent format, such as JSON. Although desirable, comparison with other experimentation datasets from other research laboratories it is not feasible at present, due to the lack of standardization in data representation and contents, but this is a first step towards that direction.
- 5.
Multi-platform operation derived from the fact that FLEXOR is implemented in Java. This implies that FLEXOR is fully operable in most popular operating systems, i.e., Windows, Linux-Unix families and Mac OS.
Analytical and numerical processes implemented in the FLEXOR software allow some decision making processes (for example, the valuable data to use in the curve fitting) to be automated, which avoids additional human manipulations and errors.
This paper has been organized as follows. Section 2 analyses the background of the proposal, showing the most relevant research works in the area. Section 3 summarizes the theory and methods of this work. Section 4 presents the proposal to automatize the plantar-flexor stiffness assessment, introducing FLEXOR. An in-the-wild testing example of the proposal is described in Section 5 and the discussion about the results as well as the proposal are explained in Section 6. In the end, the conclusion of the paper and the future work are presented.
Section snippets
Background
The MAS measurement and analysis is an important concern in many research avenues related to healthcare and wellbeing, such as sports performance. In this sense, there exists plenty of recent research concerning novel approaches to enhance stiffness assessment methods and techniques, as they involve different processing software including complex numerical analysis. In this section, it is presented a brief analysis of the most relevant pieces of work so far.
One of the most referenced paper in
Computational methods and theory
This section contains a description of the theoretical fundamentals of our work. Here we describe the assumptions and mathematical bases required to carry out MAS analysis procedures and which are supported by the FLEXOR tool.
Taking as a reference an equilibrium static position, a perturbation induced in the lower part of the leg produces an involuntary free oscillation of the foot around the ankle. This vibration, in which the plantar flexor muscles are involved (mainly the Triceps Surae), can
Software design
A description of the software is presented in this section. Here, the software architecture as well as the mathematical methods implemented are discussed, the user interface is showed, and the functionality of the software is described.
Application example
This section presents the results for a particular hands-on case (fifth view or scenarios view), illustrating the implementation of the previous views. In addition, a full trial conducted using FLEXOR with one subject is described in this section.
To show the main functionalities of the FLEXOR system, three different cases have been selected to display the most significant part of the studies during one project carried out with one subject using the entire range of loads and for the left leg
Discussion
FLEXOR obtains the mechanical response of the muscle-tendon units using the free vibration technique of a one-degree-of-freedom damped system linked to the ankle joint. This damped vibration measured by a load cell hosted in a measurement ad hoc device (where different degrees of freedom related to the position of the subject in the measurement device must be controlled carefully) is graphically displayed in a wave shape in the FLEXOR GUI. In addition, through the integrated GUI of FLEXOR, the
Conclusions and further work
So far, MAS analysis used to consist of a complex, trial-and-error-based estimation procedure involving several stages and software applications (such as device interfaces, numerical computing environments and spreadsheet editors), defining a flow of tasks whose interlinkage had to be managed by hand.
Through a friendly GUI-based solution, FLEXOR supports and further automates analysis of MAS, using suitable hardware (experimentation measurement device load cell), addressing the different steps
Funding
This research has been supported by the project DEP2015- 70980-R of the Spanish Ministry of Economy and Competitiveness (MINECO) and European Regional Development Fund (ERDF), as well as, received inputs from the COST Action IC1303 AAPELE.
Declaration of Competing Interest
The authors declare that there are not competing interests.
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