Analysis of Anthropometrics and Mechanomyography Signals as Forearm Flexion, Pronation and Supination Torque Predictors

doi:10.21203/rs.3.rs-1403843/v1

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Analysis of Anthropometrics and Mechanomyography Signals as Forearm Flexion, Pronation and Supination Torque Predictors

https://doi.org/10.21203/rs.3.rs-1403843/v1

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The aim of this study was to analyze anthropometrics and mechanomyography (MMG) signals as forearm flexion, pronation and supination torque predictors. Twenty-five young, healthy, male participants performed the isometric forearm flexion, pronation and supination tasks from 20 to 100% maximal voluntary contraction (MVC) maintaining right angle at elbow joint. Nine anthropometric measures and MMG signals from biceps brachii (BB), brachialis (BRA) and brachioradialis (BRD) muscles recorded using triaxial accelerometers were correlated with torque values. Significant positive correlations were found for arm circumference (CA) and MMG root mean square (RMS) values with flexion torque at five submaximal to maximal MVC levels. Flexion torque might be predicted using arm circumference (r = 0.426–0.557), a pseudo for muscle size and MMG RMS (r = 0.404–0.470), an indication of muscle activation.

Anthropometrics

Mechanomyography

Torque

Elbow flexor muscles

Mechanomyography (MMG) is a non-invasive technique for recording of low frequency oscillations in active skeletal muscle fibers [1]. MMG offers notable benefits, which include but not limited to minimal skin preparations, negligible effect of skin impedance and lower susceptibility to external noise [2, 3]. Another benefit, which makes this technique more suitable than its electrical counterpart, surface electromyography (EMG), is the information related to muscle contractile properties carried in the signal’s frequency content [4].

Three elbow flexor muscles namely biceps brachii (BB), brachialis (BRA) and brachioradialis (BRD) are considered in this study for collection of MMG signals during sustained isometric forearm flexion, pronation and supination tasks. The BB inserts into radius and participates in elbow flexion and forearm supination [5] while BRA inserts into ulna and is responsible only for elbow flexion. The BRD passes through elbow joint and participates both in elbow joint flexion and forearm pronation [6].

Time and frequency domain parameters of MMG signal have been plotted against torque produced by elbow flexor muscles [7]. There is evidence from literature on the application of MMG as a tool for muscle torque estimation [8]. MMG can serve as a suitable approach for the measurement of muscle torque estimation under those conditions where direct measurement of torque is not feasible, such as for sustained contractions under fatiguing condition [9]. The findings pertaining to torque have potential to benefit research community working in design and control of prosthetics. These findings may also be useful in future to predict overuse injury development in workplace settings that use repetitive joint maneuvers or high torque tasks [10].

The ability to produce muscle torque is important to gauge the performance of workplace tasks and depends on both muscle activation and muscle cross-sectional area [10]. The anthropometric measures were observed to be predictive of elbow flexion strength [11]. Forearm circumference (CA) and EMG amplitude were observed to influence wrist torque [10]. Although, a study suggested MMG root mean square (RMS) to be invariant to anthropometrics [12], an analysis of anthropometrics and MMG signals as torque predictors is still a shadowed area. Anthropometrics including arm length (LA), CA, age, weight, height, BMI and skin fold thickness were considered in this study for torque prediction. It was hypothesized that a few of this anthropometrics and MMG signal activation will improve the prediction of torque in elbow flexors muscles.

MMG sensor and signal recording

The MMG signals were collected using three triaxial accelerometers (ADXL335, Analogue Devices, USA; full-scale range = ± 3 g; typical frequency response = 0.500–500 Hz; sensitivity = 330 mVg^− 1; size = 15 mm x 15 mm x 1.5 mm; weight < 1.5 grams). The design of experiment and procedures followed were similar to previous studies [1, 12, 13]. For flexion task, the accelerometers were placed on muscle bellies using double adhesive tape, when the forearm is in supinated position and flexed at 90°. For pronation and supination tasks, accelerometers were placed when the forearm is in neutral position and flexed at 90° (Fig. 1). The anatomical position of each muscle belly was determined according to [14]: BB – into the bulk of the muscle in the middle of the arm; BRA – two finger breadths proximal to the elbow crease along and just lateral to the tendon and bulk of the biceps; and BRD – midway between the biceps tendon and lateral epicondyle along the flexor crease. The x, y and z axes of each accelerometer were positioned along the estimated longitudinal, lateral and transverse directions of the muscle fibers, respectively. The MMG signals recorded in transverse axis of the accelerometer, were utilized for further processing and analysis as this direction is least influenced by crosstalk, an undesired effect [13]. The output from each of the sensor was connected to the data acquisition unit (NI cDAQ 9191 with the NI 9205 module with 16-bit resolution at CMRR of 100 dB, National Instruments, Austin, TX, USA), which was connected to a personal computer over Wi-Fi. The MMG signals were sampled at 1 KHz. Data acquisition and storage were performed using custom-made programs in LabVIEW™ (version 2016, National Instruments, Austin, TX, USA) [1, 12, 13, 15].

Subjects

Twenty-five young, healthy, untrained and right-handed male subjects (mean ± SD: age = 25.523 ± 4.545 years, weight = 68.219 ± 9.658 kg, height = 167.523 ± 3.172 cm) with no history of neuromuscular injury participated in the experiment. A written informed consent was taken from the subjects. The study was approved by the local Medical Research and Ethics Committee (MREC), Ministry of Health, Malaysia, and adhered to the guidelines established by the Declaration of Helsinki. A medical officer was present on site to aid the researchers and handle any emergency.

Recording of anthropometric measurements

Familiarization session was initiated by taking a written consent from participants. The personal details and anthropometric measurements were noted afterwards. The anthropometric measurements of all the participants were noted again during experiment with a gap of at least 24 hours. The person who recorded anthropometrics got practiced first under supervision of medical officer for more than 150 test readings and then took the actual measurements. The CA, LA and ST were measured using same procedure as described in a previous study [12]. The details of these anthropometrics along with other parameters of all the subjects are given in Table 1.

Determination of MVC

Each participant performed the experiment in two sessions. Maximal voluntary contractions (MCV) were determined first for all three isometric tasks. Three trials were performed for each task with a gap of 2 minutes between the trials and that of 10 minutes between tasks. Each participant followed postural settings same as described in previous study [12].

The maximum weight in form of dumbbells that a participant could isometrically sustain for 2–3 seconds in proper posture and forearm in supinated position, was considered as MVC for flexion task. The participant held a wrist dynamometer (Baseline™ Evaluation Instruments, Fabrication Enterprises Inc., NY, USA) for MVC determination in pronation and supination tasks. The wrist was maintained at neutral position and MVC was determined as maximum effort the participant could produce for a 2 seconds forearm pronation or supination. A trial only with variation less than ± 5% was acceptable. The participants were strongly encouraged to produce their maximum effort for MVC determination in all three tasks.

Submaximal to maximal tasks

In the second session, the subject performed warmups, followed by submaximal tasks using similar postural settings. Subject was asked to perform three isometric tasks (flexion, pronation and supination) at 20%, 40%, 60%, 80% and 100% MVC for at least 6 seconds, with an inter and intra task rest of at least 10 minutes and 2 minutes, respectively. However, our recordings show that for 100% MVC, most of the participants could hold only for at most 4 seconds, which was, nevertheless, considered sufficient for data processing. All the trials were randomized in terms of levels of effort within each task to avoid any risk of biasness in the data. The subjects were not informed of the order prior to the commencement of the task. Trials were repeated if torque level was not achieved within a deviation of ± 5% of the calculated torque level. During the trials, proper posture, off-axis precautionary measures, maximum ± 5% variations, announcement of time elapsed and verbal encouragement were observed. The generated signals were recorded in a personal computer for further analysis.

Data analysis

The data stored on a personal computer were digitally bandpass filtered (fourth order Butterworth) at 5-100 Hz to obtain the MMG signals. Only those collected signals were used for further processing that provided a signal-to-noise ratio > 10 dB. The 2 seconds long MMG signals from the middle of each isometric contraction were then extracted to avoid transient phenomenon when the muscle passes from rest to activity and vice versa, as recommended by [16]. These segments were then used to quantify RMS.

Statistical analysis

The statistical analyses were performed using IBM SPSS (version 20, IBM SPSS Statistics, NY, USA). The data normality was checked using Shapiro-Wilk’s test and the data was found to be normally distributed. Thus, parametric analyses were employed. Linear regression was performed to determine the estimated relationship of the torque values with RMS and anthropometric parameters. Pearson correlation coefficients (r) at a significance level of 0.05 were used to describe the correlation between the variables as follows: 0.0 - ±0.3 = negligible; ±0.300 - ±0.500 = low; ±0.500 - ±0.700 = moderate; ±0.70 - ±0.90 = high; ±0.90 - ±1.00 = very high [17].

Table 1

Anthropometric details of subjects
Subject#	Age (Years)	Weight (Kg)	Height (cm)	BMI (Kg/m2)	LA (cm)	CA (cm)	ST-BB	ST-BRA	ST-BRD
1	34	82.6	167	29.61741188	35.5	34.5	12.8	15.6	12.8
2	32	65.5	161	25.26908684	36	29.8	10.4	14.4	14.4
3	25	54.8	167	19.64932411	36.5	32.8	8.4	10	9.6
4	32	91.9	183	27.44184658	41	32.8	10.8	14.4	14.8
5	21	56.1	157	22.759544	35	28.5	8.8	9.2	8.8
6	22	73.4	168	26.00623583	38	35	8	10.4	10
7	23	56.5	169	19.78222051	37	26.7	10.4	11.2	10
8	21	55.8	169	19.53713105	35.5	28.3	10.4	8.4	10.4
9	23	66.6	160	26.015625	35	32.3	7.6	9.6	10.8
10	23	67.5	163	25.40554782	36	35.5	8	9.2	8.8
11	35	89.2	172	30.15143321	39.5	35.1	10.4	8.8	8.8
12	35	89.1	181	27.19697201	38	31.6	8.4	10.8	10.4
13	28	70.8	170	24.4982699	38.5	34.3	7.6	9.2	10
14	23	80.3	167	28.79271397	36.5	35.5	11.6	12.8	13.2
15	42	75.5	167	27.07160529	33.5	33.8	6.8	10	8.4
16	22	62	162	23.62444749	35	30	7.6	8.8	8.8
17	22	43.7	166	15.85861518	33	24.8	7.2	6.8	7.2
18	21	62.9	165	23.10376492	37	33	8.4	8	7.6
19	32	65.5	168	23.20719955	37	30.16	5.2	7.6	8
20	23	69.9	170	24.18685121	37.5	34.5	8	10.4	9.6
21	23	54.7	164	20.33759667	36	31.6	7.2	8.8	8.4
22	24	68.3	170	23.63321799	36	32.6	8	8.4	9.6
23	22	78.7	173	26.29556617	39	39.8	8.4	9.2	11.2
24	23	56.5	170	19.55017301	36.5	28.5	6.4	8	7.6
25	24	74.3	170	25.70934256	40.5	33.5	8.4	12.4	14.4

Table 2 summarizes Pearson’s correlation co-efficient values for torque against twelve parameters including anthropometrics and RMS for all the three muscles and tasks. All the parameters showed negligible correlations with torque except CA and MMG RMS of BB muscle during flexion task. Both of these parameters showed significant low positive correlations. Scatter plots in Fig. 2 shows the linear regression line for torque against CA and spread of data for same relation.

Table 2

Pearson’s correlation co-efficient values for torque against anthropometrics and MMG RMS
No.	Parameters	Pearson’s correlations of Torque with RMS and anthropometric parameters
		Flexion					Pronation					Supination
		20% MVC	40% MVC	60% MVC	80% MVC	100% MVC	20% MVC	40% MVC	60% MVC	80% MVC	100% MVC	20% MVC	40% MVC	60% MVC	80% MVC	100% MVC
1	Arm Length	0.010	0.089	0.112	0.135	0.087	0.144	0.165	0.170	0.167	0.170	-0.122	-0.101	-0.086	-0.032	-0.094
2	Arm circumference	0.276	0.575	0.426	0.558	0.524	0.195	0.241	0.244	0.237	0.242	0.159	0.173	0.188	0.191	0.189
3	Age	-0.176	-0.362	-0.199	-0.262	-0.279	-0.175	-0.293	-0.291	-0.290	-0.287	0.002	0.017	0.018	0.039	0.024
4	Weight	-0.077	0.044	0.093	0.149	0.094	0.092	0.113	0.121	0.123	0.122	0.151	0.175	0.185	0.236	0.190
5	Height	-0.211	-0.172	-0.069	-0.053	-0.118	0.015	0.181	0.183	0.187	0.185	-0.126	-0.098	-0.104	-0.028	-0.102
6	BMI	0.018	0.152	0.146	0.210	0.176	0.109	0.038	0.046	0.046	0.046	0.236	0.252	0.268	0.290	0.273
7	ST BB	-0.202	-0.086	-0.068	-0.025	-0.064	-0.057	0.183	0.186	0.187	0.184	0.214	0.199	0.204	0.232	0.216
8	ST BRA	-0.094	-0.095	-0.119	-0.083	-0.085	0.026	-0.027	-0.031	-0.025	-0.026	-0.025	-0.050	-0.041	0.008	-0.028
9	ST BRD	-0.070	-0.060	-0.077	-0.029	-0.043	-0.001	0.149	0.147	0.155	0.151	0.155	0.137	0.142	0.198	0.147
10	RMS BB	0.370	0.014	0.108	0.030	-0.087	-0.010	0.100	-0.063	-0.045	0.039	-0.011	0.212	-0.040	-0.101	-0.264
11	RMS BRA	0.386	0.000	0.014	-0.213	-0.227	0.059	0.134	-0.099	0.168	0.057	-0.007	-0.396	-0.125	0.281	-0.364
12	RMS BRD	0.441	0.059	0.068	-0.159	-0.168	-0.017	0.241	0.054	0.206	0.294	-0.088	-0.111	0.009	-0.017	-0.054

This study was conducted to predict torque produced by elbow flexor muscles through anthropometrics and MMG signal. It was hypothesized that CA and MMG RMS is correlated with muscle torque. In order to test this hypothesis, correlations of torque, at five submaximal to maximal levels of effort, with nine anthropometrics and MMG RMS from BB, BRA and BRD muscles were analyzed. The findings support our hypothesis that circumferential measurements of arm and MMG RMS have greatest influence on flexion torque.

From ergonomics point of view, anthropometrics [18] and strength capabilities [19] play important role in workplace design for injury prevention specifically in case of workers performing repetitive tasks involving forearm pronation, supination and elbow joint flexion. The findings on torque related to anthropometrics and MMG signal have potential to improve design and control of prosthetics in medical field.

The findings in this study indicate CA to be more influential to muscle torque during elbow joint flexion task. Literature indicates that CA is a measure of muscle and limb size [20]. This observation is also supported by the important role of all the three muscles observed in this study during elbow joint flexion task [21]. Similarly, most of significant Pearson’s correlations between MMG RMS and muscle torque were found during flexion task. This observation strengthens the finding on anthropometrics, specifically CA being a prominent torque predictor during elbow flexion task.

It was observed in the scatter plots for CA against muscle torque that positive correlation was there between the two parameters as indicated by linear regression lines. The points in scatter plots for lower submaximal effort levels specifically at 20 and 40% MVC were observed to be more concentrated at few torque values. The scatter of data may be influenced by limited number of torque values at low effort levels and be improved through the use of some digital dynamometer for flexion task offering more sophisticated torque measurement. Further, the co-contractors might also have their part in the tasks performed [22]. Hence study of anthropometrics with co-contractors might also give some useful information.

Significant positive correlations were found for flexion torque with CA (r = 0.426–0.557) and MMG RMS (r = 0.404–0.470). This observation leads to conclude that muscle torque might be predicted using CA, a pseudo for muscle size and MMG RMS, an indication of muscle activation. These findings on torque correlations with anthropometrics and MMG signal might be useful in future while workplace design for injury prevention in the field of ergonomics and prosthetics design in medical field. More sophisticated measurement of torque and engagement of participants having diverse anthropometrics might improve the findings in future experiments.

Acknowledgements

The authors would like to acknowledge the Director General of Health Malaysia for giving us permission to publish this article and the Medical Research and Ethics Committee (MREC) of Malaysia for providing ethical approval for collection of the data used in this study.

Competing interests

The author(s) declare no competing interests.

Author Contributions

I.T, K.S and J.H conceived, designed and performed the experiments and analyzed the data. All authors contributed reagents/materials/analysis tools and wrote the paper.

Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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You are reading this latest preprint version

Analysis of Anthropometrics and Mechanomyography Signals as Forearm Flexion, Pronation and Supination Torque Predictors

Status:

Version 1

Abstract

Figures

1. Introduction

2. Methods

MMG sensor and signal recording

Subjects

Recording of anthropometric measurements

Determination of MVC

Submaximal to maximal tasks

Data analysis

Statistical analysis

3. Results

4. Discussion

5. Conclusion

Declarations

Acknowledgements

Competing interests

Author Contributions

Data availability statement

References

Additional Declarations

Status:

Version 1