Abstract
This work contributes to the development of a repeatable and objective methodology for relating the physiological energy spent during a handgrip exercise, identified through the variation of skin temperature, with the average grip force, and evaluate its influence on exercise endurance and handedness dominance. For that purpose, a special handgrip dynamometer is used as well as an Infrared Thermal Imaging (IRT) to map large areas of skin surface temperature. Results suggest that at least a 10-grips test with the dynamometer is required to produce reliable thermal results and the dominant hand should be used. In the future, relationship between the thermal variables and mechanical work involved during the handgrip should be addressed. The developed methodology should be applied to populations at health risk conditions to which the use of the handgrip dynamometer can provide information for diagnose and treatment assessment.
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1 Introduction
In many nutritional status or status alterations, the voluntary hand/arm force (Handgrip strength (HGS)) has been shown to be an easy and relevant indicator to assess muscle function (MF). Therefore, HGS is used as an indicator of overall muscle strength in routine clinical practice [1,2,3]. HGS is also one of the five established characteristics within the Criteria to Define Frailty [4] and is being used in multiple studies [5,6,7,8,9,10]. The devices for HGS evaluation are dynamometers offering an inexpensive and portable method for easy assessment.
This work uses a dynamometer prototype [11] developed at LAETA-INEGI, University of Porto, with patent application P300.3WO (September 2016). It offers a fully instrumented dynamometer with wireless communication for reliable data recording and fast processing. It allows the analysis of grip force over time and associated parameters that can be added to the traditional maximal grip strength for extended studies.
The prototype has demonstrated a very good performance when compared with Jamar dynamometer, considered as a reference in clinical use [11]. It has a parallelepiped shape with reduced dimensions. These features make it very convenient for frailty tests in ageing, recovering, rehabilitation and with children. Despite being a dynamometer, its features are not limited to that of a typical dynamometer, it can also provide different information by post-processing the handgrip force over time evolution as, e.g., estimation of relevant part of the user expended energy, applied mechanical work, rate of rising and dropping of grip force and association with user parameters to be explored as, e.g., endurance, resilience.
The Infrared Thermal Imaging (IRT) is able to map large areas of skin surface temperature distribution, which is linked to and an indirect method to estimate the peripheral blood flow [12, 13]. It is a remote, non-ionizing and safe imaging method that can provide physiological information in response to mechanical stimuli [14].
IRT has been used in the past to identify chronic forearm pain in patients when exposed to a mechanical stimulus, typing on a keyboard, though monitoring temperature changes in the forearm [15].
The used dynamometer measures the muscular mechanical force (N) and the elastic energy applied to the device during the handgrip test. The physiological energy the user spends during the grip test is then accessed in terms of the effect on the change on the skin temperature resulting from the muscle activity and on the mechanical energy and applied force measured with the dynamometer.
It is aim of this research to develop a repeatable and objective methodology for quantifying the physiological energy spent at a handgrip test by infrared thermal imaging and to evaluate its influence on estimation of the endurance and the handedness dominance within the test.
2 Methodology
The designed procedure involves the execution of consecutive handgrip exercises using a dynamometer while recording the grip force and the thermal images for identifying skin temperature. For enforcing the correct positioning, handgrip exercise and avoid unwanted movements of the forearm, a support was designed and developed. Figure 1 shows a data capture setup.
There were made several variations of the exercise, first with only 1 grip, then with 5 consecutive grips and finally with 10 consecutive grips. After initial tests, it was decided to proceed using 10 grips since they provide major thermal amplitude. Thermal images were taken at a rate of one per second during the exercise and up to 60 s counted from the last performed grip. In this additional period the user is still holding the dynamometer, with his arm resting over the support. An acclimatization period of ten minutes with the forearm unclothed to promote thermal equilibrium with the surrounding environment was adopted before starting the data collection.
For measuring and recording grip force over time it was used a special handgrip dynamometer prototype (BodyGrip) that was developed at the authors research group. This device allows the measurement of isometric gripping or puling forces associated with multiple muscle groups, such as the ones from the hand. The BodyGrip device uses onboard electronic circuits for wireless communication and processing data, being powered by a chargeable battery. An application, running on a personal computer, allows the continuous registry of the force data during assessment time, apart from enabling to configure test conditions such as the test duration. The design of the BodyGrip using special force measurement load cells instrumented with strain gauges’ sensors, allows a compact (0.144 × 0.022 × 0.045 [m]) and low weight device (0.25 kg), offering force range of ±900 N, and resolution of 1 N. Considering working details within the specially designed load cells it is expected a maximum displacement of each load cell free end for the full load, with a value of 0.001 m (exhibiting an elastic constant of 1140 N/mm), allowing to determine the elastic energy applied to the device. From the dynamometer, the force (N) and time (ms) is obtained and recorded in a CSV file. The maximum grip forces per handgrip were retrieved and later associated with the thermal data parameters using the same time interval.
To capture the infrared thermal images a laptop attached thermal camera FLIR A325sc (sensor Focal Plane Array of 320 × 240, Noise-equivalent temperature difference of <50 mK at 30 °C and a measurement uncertainty of ±2% of the overall reading) was used.
A total of 21 participants (13 men and 8 women), all right-handed, with ages 35 ± 14 years old and Body Mass Index of 25.7 ± 3.8, participated in the study. To all, the test procedure was explained and an informed consent was obtained. All experiments followed the ethical direction of the declaration of Helsinki. In order to avoid metabolic influencing variables, the participants prior to the experiments refrained from having a heavy meal, alcohol, coffee, tea and drugs and smoking up to 2 h before the appointment. They were also instructed to avoid any sport or physiotherapy and not using oils or ointments in the forearms.
The data was collected in a controlled environment room, in line with the international guidelines [14, 16], with average temperature 23.8 ± 0.4 °C and relative humidity (RH) of 47.4 ± 5.3%, values that were monitored using a Testo 175H1 temperature and humidity data logger (range of −20 to +55 °C and 0 to 100%RH; accuracy of ±0.4 °C and ±2%RH; and resolution of 0.1 °C and 0.1%RH). There was an absence of airflow and incident illumination over the participants to prevent thermal reflections.
The data analysis to study the thermal effects of the handgrip exercise, involved the definition of three Regions of Interest (ROI), Fig. 2, being the ROI1 over the digital flexor muscle, the ROI2 over the wrist ulnar artery and the ROI3 over the wrist radial artery. The ROI1 is upon the main muscle responsible for the handgrip test and ROI2 and ROI3 are over the main arteries that feed the hand. The geometry of the ROIs was chosen because of the facility in placing them at the appropriate location, the circle for ROI1over the muscle and the squares for ROI2 and ROI3 at the wrist over the arteries.
For all the three ROI and per thermal image, at the FLIR ThermaCAM researcher pro 2.10 software, the value of mean temperature is obtained and registered in a Microsoft Excel spreadsheet.
A baseline image holding the dynamometer was then taken and the handgrip at maximum force for 5 s was exerted.
Three time moments are considered: “B” - baseline (before the starting of the exercise), “A” - immediately after the exercise (1 s after for 1-grip, or 15 s after the last exercise for the other test types), and “F” - final recorded image. The middle point was spotted after analyzing the temperature evolution pattern over the exercises and corresponds to the moment where the maximum decrease in temperature occurs (one example is provided at Fig. 5). At the three moments, gradients between the ROIs were also calculated (ROI1–ROI2, ROI1–ROI3 and ROI2–ROI3). Other thermal parameters per ROI were DT1 (the difference between B and A) and DT2 (the difference between F and A). The mean temperatures for the ROIs and the calculated thermal gradients were statistically evaluated to verify the influence of handedness dominance, age, body mass index (BMI), sex and correlation with the average force applied in the handgrip test.
The collected data was later imported to the SPSS v24 statistical analysis software package. Every variable was statistically verified if it follows the normal distribution using the Shapiro-Wilk test. If the values follow the normal distribution, the parametric tests student t-test and Pearson correlation are used, if not the non-parametric tests Kruskal-Wallis and Spearman Correlation are used instead. Influence of factors such as sex, age and BMI are assessed. The ANOVA test is used to analyze the differences among the three-different endurance tests means. The statistical significance value used is p < 0.05.
3 Preliminary Results
As the study involved a group of subjects and not a single subject, the Table 1 presents the average grip force measured per grip in the 10-grips test and the final average grip force that was used for the correlation with the thermal variables. The maximum recorded force in a subject was 708.6 N for the right limb and 703.2 N for the left limb. It can be verified that the grip force decreased for the upcoming grips when compared with the first.
An example of a thermal image obtained from the forearm is shown in Fig. 3 with the ROIs superimposed on the picture.
In all the three different tests with both hands it was verified that the temperature decreased during and immediately after the workout, increasing thereafter. In the Fig. 4 it can be seen that comparing the relative gradients, the maximum variation was obtained in the 10-grips test (±0.3 °C) and it increased proportionally with exercise, being minimal with 1-grip test (±0.1 °C) and 5 grips test (±0.2 °C). This, despites the lack of statistical evidence (ANOVA p > 0.05) of independence between the results of the three types of tests, shows that the endurance matters in terms of thermal energy generated.
The Fig. 5 present the average evolution of the mean temperature difference from baseline on the 3 ROIs of the participants during the 10-grips test. It can be seen that the temperature decreases during the test, reaching the minimum 15 s after the last grip and rising thereafter. The ROI3 showed a response with larger temperature amplitude, followed by ROI2 and ROI1, that presented the minimal amplitude.
From the statistical analysis, using non-parametric methods, it was verified that not all the variables followed the normal distribution. In the single grip and 5-grips tests, for both hands, there was no statistical evidence of the influence of age, BMI and sex in the obtained results. In the 10-grips test, it was found evidence (Kruskal-Wallis p < 0.05) of age in the gradients ROI1–ROI3 at A and F, ROI1–ROI2 at A, ROI1 DT1 and ROI2 DT2, and of sex in ROI DT2. There was no statistical evidence of the influence of BMI.
In terms of correlation between the force measured with the dynamometer and the thermographic studied variables, it was found statistical evidence (Spearman p < 0.05) on ROI2 F, ROI3 A, ROI3 F and ROI1–ROI3 F, of R = −0.305, −0.328, −0.291 and 0.327 respectively.
4 Discussion
To the authors knowledge, this was the first attempt to measure thermal energy from handgrip exercise and relating it to grip force measured with dynamometer. The developed methodology follows the international standards of dynamometer monitoring [1,2,3,4] and IRT imaging [14, 16] of human subjects.
It was verified that during exercising and immediately after the skin temperature decreased recovering thereafter, which is in line with previous related literature [15]. This may be justified by the demand of blood supply by the muscles during exercise and posterior release of the generated energy after the exercise.
This study allowed to evaluate the endurance of an individual exercise with the BodyGrip. Although there is no statistical evidence to support the assumption that more repetitions of the exercise generate more heat, empirically this has been demonstrated and at least ten repetitions are required to come out of the uncertainty range of the thermographic camera.
The handedness dominance question was not statistically demonstrated, however the responses to right-handed individuals were more pronounced in the right limb, which may result from a better endurance capacity than is expected to be the dominant.
Correlations were found between strength and thermal variables. However, the value of R is low, which may mean that the thermal energy expended may be different from the related work, being complementary to the mechanical elastic energy exerted on the device.
In order to corroborate the elastic energy, it is suggested the use of chemical markers for measurement of metabolic equivalents (METs) or explore the measurement of the mechanical tension of the muscular fibers through ultrasound.
The limitations of this experimental setup are: the need of a controlled environment, the need of collaboration of the subject, both measurements have to be taken separately and join together for global quantification of the energy spent by performing the exercise. Although, this data can be used to aid diagnosis and treatments assessment.
Different applications using the dynamometer combined with IRT may include among others the prediction of nutrition status, and of falls and risk of limited mobility in elders.
There are temperature variations at the different studied sites, but in this particular research, different endurance duration tests were considered along with the effect of hand dominance.
5 Conclusion
The aim of developing a repeatable and objective methodology for relating the physiological energy spent during a handgrip exercise, identified through IRT, with the average grip force, and evaluate its influence on exercise endurance and handedness dominance was fulfilled.
There is a measurable correlation between the handgrip force and the thermal variables (ROI2 F, ROI3 A, ROI3 F and ROI1–ROI3 F). With the developed procedure, it was found that the exercise involving ten consecutive grips achieves a significant thermal variation on the skin temperature. It can be concluded that, despite not having statistical significance, there is a higher amplitude in the thermal variables, which is in line with the correlation between force and those variables.
For further work, it is suggested to research the relationship between work produced and thermal variables and apply the developed methodology to populations at risk of health conditions to which the dynamometer can provided information for diagnostic and treatment assessment.
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Acknowledgment
The authors gratefully acknowledge the funding of project NORTE-01-0145-FEDER-000022 - SciTech - Science and Technology for Competitive and Sustainable Industries, co financed by Programa Operacional Regional do Norte (NORTE2020), through Fundo Europeu de Desenvolvimento Regional (FEDER) and of project LAETA - UID/EMS/50022/2013.
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Vardasca, R., Abreu, P., Mendes, J., Restivo, M.T. (2019). Handgrip Evaluation: Endurance and Handedness Dominance. In: Auer, M., Langmann, R. (eds) Smart Industry & Smart Education. REV 2018. Lecture Notes in Networks and Systems, vol 47. Springer, Cham. https://doi.org/10.1007/978-3-319-95678-7_57
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