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Electromyography-based analysis of human upper limbs during 45-day head-down bed-rest
Acta Astronautica 120 (2016) 260–269
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Electromyography-based analysis of human upper limbs during 45-day head-down bed-rest Anshuang Fu a, Chunhui Wang b,1, Hongzhi Qi a,n, Fan Li b, Zheng Wang b, Feng He a, Peng Zhou a, Shanguang Chen b,n, Dong Ming a,n a
Department of Biomedical Engineering, Tianjin University, Tianjin, People’s Republic of China National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, People’s Republic of China b
a r t i c l e in f o
abstract
Article history: Received 24 April 2015 Received in revised form 29 October 2015 Accepted 8 December 2015 Available online 4 January 2016
Muscle deconditioning occurs in response to simulated or actual microgravity. In spaceflight, astronauts become monkey-like for mainly using their upper limbs to control the operating system and to complete corresponding tasks. The changes of upper limbs' athletic ability will directly affect astronauts’ working performance. This study investigated the variation trend of surface electromyography (sEMG) during prolonged simulated microgravity. Eight healthy males participating in this study performed strict 45-day head-down bed-rest (HDBR). On the 5th day of pre-HDBR, and the 15th, the 30th and the 45th days of HDBR, the subjects performed maximum pushing task and maximum pulling task, and sEMG was collected from upper limbs synchronously. Each subject’s maximum volunteer contractions of both the tasks during these days were compared, showing no significant change. However, changes were detected by sEMG-based analysis. It was found that integrated EMG, root mean square, mean frequency, fuzzy entropy of deltoid, and fuzzy entropy of triceps brachii changed significantly when comparing pre-HDBR with HDBR. The variation trend showed a recovery tendency after significant decline, which is inconsistent with the monotonic variation of lower limbs that was proved by previous research. These findings suggest that EMG changes in upper limbs during prolonged simulated microgravity, but has different variation trend from lower limbs. & 2015 IAA. Published by Elsevier Ltd. All rights reserved.
Keywords: HDBR sEMG Upper limbs Muscle deconditioning Non-linear
1. Introduction Human beings have been living and evolving on the earth with 1G gravitational acceleration for ages. The morphological specificity, structure and function of our physiological system have adapted with the 1G condition. During long-duration spaceflights with microgravity, there are some physiological changes occurring in the body, especially muscle atrophy and muscular endurance drop [1,2]. The series changes of muscle
n
Corresponding authors. 1 The author contributes as equal as the first author and is the co-first author. http://dx.doi.org/10.1016/j.actaastro.2015.12.007 0094-5765/& 2015 IAA. Published by Elsevier Ltd. All rights reserved.
system, generally termed as muscle deconditioning, consist mainly of loss of muscle mass, force and power, increased muscle fatigability and abnormal reflex pattern [3]. The deconditioning not only lowers astronauts’ working performance or even results in the failure of entire mission in space, but also has bad influence on their health after coming back to earth. Therefore, during the past several decades, it has become a core research focus of space physiology to understand the mechanism of physiological changes and to develop countermeasures to alleviate deconditioning progression through investigation on motor system [2]. However, due to restrictions in spacecraft, it is impossible to run integrated experiments on sufficient subjects during spaceflight. In order to overcome the difficulty, scientists discovered that
A. Fu et al. / Acta Astronautica 120 (2016) 260–269
head-down bed-rest that can lead to redistribution of body fluid was an appropriate model to simulate many physiological responses to microgravity, including cardiovascular changes, bone mass loss and muscle change. First adopted in the 1970s, HDBR has a history of about 40 years [4]. Change of human body’s movement ability is mainly reflected in two aspects: the change of upper limbs’ athletic ability and the change of lower limbs’ athletic ability. And these changes of athletic ability are usually reflected via muscle force and muscle cross-section area (mCSA). Since muscles with antigravity function mainly cluster on back and lower limbs [5–7], studies on human physiological system under microgravity or simulated microgravity focus more frequently on lower limbs. Leg muscles showed significant atrophy in the early weeks of bed rest [6–9]. This atrophy is different from the most severe type of muscle atrophy called neurogenic atrophy that usually results from an injury or disease of a nerve that connects with muscle. And the atrophy we relate to our study is another type of atrophy called disuse atrophy, which results from long-duration muscle wasting without enough physical activity. People with sedentary jobs, medical conditions and long space missions that limit their movement or decrease activity levels might lose muscle tone and develop disuse atrophy [28]. A series of experiments using MRI measurements revealed a significantly decreased muscle cross-section area (CSA) of lower limbs with HDBR [10–14]. A decrease in power as well as a decrease in both force and velocity was also observed in lower limb, but not in upper limb movements after a 20-day bed rest [15]. LeBlanc also reported that no change had occurred in upper limbs during 17 weeks of bed rest [8]. However, in spaceflight, astronauts mainly use their upper limbs to control the operating system and to complete corresponding tasks [16]. Furthermore, they become monkeylike in space, using their hands and arms much more than they do when coming back on earth [17]. That is to say, the changes of upper limbs’ athletic ability will directly affect astronauts’ working performance. Up to now, few studies have focused on upper limbs during space missions. Therefore, for the sake of getting the whole picture of deconditioning in muscle system, it is important to investigate muscle deconditioning of upper limbs. Besides, few studies have associated the analysis of muscle system with sEMG, and the small number of sEMG studies have mainly focused on time domain and frequency domain [5,6,18]. But in theory, sEMG signal is widely considered to be chaotic or to contain chaotic elements since muscle contraction is affected by various factors, and a variety of physiological and pathological factors influencing sEMG are often mutable [19]. Hence, having been widely used in neurotic electrophysiology in recent years, non-linear method can give another perspective to understand the effect of muscle system under simulated microgravity [20]. For the above reasons, we investigated upper limbs during 45 days of 6° HDBR experiment and provided more comprehensive tracking of the changes in muscle system by analyzing time domain, frequency domain and non-linear parameters of sEMG under prolonged simulated microgravity.
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2. Method 2.1. Ethics Assessment Conclusion for Research Proposal by China Astronaut Research and Training Center Ethics Committee. China Astronaut Research and Training Center Ethics Committee passed the assessment and approved the experiment. After explaining experiment protocol, written informed consent was obtained from all the subjects. 2.2. Subjects Eight healthy non-smoking right-handed men in average aged at 27.13 years old (SD¼ 4.49, range: 22–34 years old), height 168.81 (SD ¼4.52, range: 165–173 cm) and body mass 61.63 kg (SD ¼5.80, range: 55–72 kg) participated in the 45-day 6° head-down bed-rest (HDBR). The subjects have no drug dependency, no history of infectious diseases, no hereditary diseases, and no mental and psychological diseases within three generations of their family. They received junior middle-school or above education, and have normal intelligence (Raven’s IQ test is more than 70 points). 2.3. Experimental paradigm The total duration of the experiment was 65–70 days (Fig. 1). Before the 45 days of HDBR, the subjects had been trained for 10 days to adapt to the experimental environment, and the period was called pre-HDBR. And then the subjects began the rigorous 45-day 6° HDBR. After HDBR, the 10–15 days of convalescence called post-HDBR was designed for their recovery. The duration of the postHDBR period depended on subject-specific recovery time. During the 45-day HDBR period, the subjects were restricted to absolute head-down recumbence and were not allowed to walk. All activities such as testing, taking food, drinking water, washing and defecation should be done under 6° HDBR condition. They were allowed to watch television or videos, listen to CDs or radio, play games and read books or magazines. To restrict the influence of nutrition on the results of the experiment, the subjects were fed with a fixed number of calories per kg of lean body mass during the HDBR and post-HDBR, but their daily water intake was not limited. The study designed single-handed pushing and pulling tasks (Fig. 2). BTE Primus RS and Biovison were used for isometric strength test and sEMG acquisition on the 5th day of pre-HDBR (pre-HDBR5), the 15th day of HDBR (HDBR15), the 30th day of HDBR (HDBR30) and the 45th day of HDBR (HDBR45). The instrument to measure sEMG was calibrated before data collection. Experiment preparation: before restricted HDBR, in order to set appropriate force for the following study, isotonic contraction method was used. The subjects were required to warm up in sports shoes, and then to fix their feet on the ground and to repeat pushing and pulling for 3 min. Force application: during restricted HDBR, the subjects were required to make their efforts to keep their body posture unchanged. Within 2 s after the start of the
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experiment, there should be a maximum volunteer contraction (MVC) applied rapidly and steadily by the subjects. Then they should keep the maximum force for another 8 s. Target muscles: triceps brachii (TB), deltoid (DT), ectopectoralis (EP), trapezius (TP), brachioradialis (BR) and extensor carpus radialis (ECR) in pushing task; triceps brachii (TB), deltoid (DT), ectopectoralis (EP), trapezius (TP), brachioradialis (BR) and biceps brachii (BB) in pulling task (Fig. 3). Repetition times: each type of force was repeated for 3 times. Test duration: a single force test lasted for 10 s. Rest requirement: a 30-s rest was required between each test of the same force type. A rest for no less than 3 min was required between the tests of different force types. Our study is only a part of the whole HDBR experiment, which relates to muscle and EMG research. To get more comprehensive understanding of the whole experiment, you can read Changes in the Diurnal Rhythms during a 45Day Head Down Bed Rest (2012) and Effects of Head-Down Bed Rest on the Executive Functions and Emotional Response (2012). 2.4. Data analysis Signals were sampled at 2000 Hz per channel, fullwave rectified and then Butterworth filtered at 5–800 Hz. Since some subjects did not maintain their forces for exact 8 s (shorter or longer than 8 s), the stable phase of 6 s was extracted for the following calculation. All the EMG
analyses were conducted in Matlab using customwritten codes. 2.4.1. Time-domain analysis of EMG Integrated EMG (IEMG) is normally used as an onset detection index in clinical application. It is related to EMG signal sequence firing point reflecting the contraction property of muscle. IEMG is defined as a summation of absolute values of EMG signal amplitude, which can be expressed as IEMG ¼
N X
Pushing
ð1Þ
where xi represents the ith sample of EMG signal and N is the length of the EMG signal. Root mean square (RMS) relates to constant force and non-fatiguing contraction. It is also similar to standard deviation method. It is defined by following equation: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N u1 X RMS ¼ t x2 ð2Þ Ni¼1 i where xi represents the ith sample of EMG signal and N is the length of the EMG. 2.4.2. Frequency-domain analysis of EMG Frequency or spectral domain features are mostly used to study the fatigue of muscle and motor unit recruitment analysis. Power spectral density (PSD) becomes a major analysis in frequency domain. Two widely used variables of the PSD are mean frequency (MNF) and median frequency (MDF). MNF is the average frequency that is calculated as the sum of product of the EMG power spectrum and the frequency divided by total sum of the spectrum intensity, which can be calculated as , M M X X MNF ¼ f j Pj Pj ð3Þ j¼1
Fig. 1. Total duration of the experiment, including pre-HDBR, HDBR and post-HDBR.
jxi j
i¼1
j¼1
where f j is the frequency of the spectrum at the frequency bin j, P j is the EMG power spectrum at the frequency bin j, and M is the length of frequency bin. Frequency bin represents f j þ 1 f j . In our study, the sample frequency was 2000 Hz, and the data length was 6 s. So we did
Pulling
Fig. 2. Schematic diagram of the experiment. Left: the subject was performing pushing task. Right: the subject was performing pulling task.
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Fig. 3. Signal acquisition from target muscles in pulling task.
2000*6 points FFT, thus the frequency bin is 1/6 Hz, and M is 2000*6/2, the half of FFT points. MDF is the frequency at which the spectrum is divided into two regions with equal amplitude. It can be expressed as M DF X
Pj ¼
j¼1
M X j ¼ MDF
Pj ¼
M 1X PJ 2j¼1
ð4Þ
2.4.3. Non-linear analysis of EMG Fuzzy entropy (FuzzyEn) is a measurement for complexity. More details obtained by fuzzy functions make FuzzyEn a more accurate entropy definition. In addition, FuzzyEn has greater relative consistency and less dependency on data length. Both theoretical analysis and experimental results show that FuzzyEn provides an improved evaluation of signal complexity and is more conveniently and powerfully applied to short time series contaminated by noise [21]. Many studies have shown that FuzzyEn could be a good tool applied in electrophysiology, such as EEG, EMG and HRV, signal processing [22–24]. Low FuzzyEn value is associated with a low complexity of time series, and high FuzzyEn value with a high complexity of time series. For time series uðiÞ: 1 ri r N form vectors: Xm i ¼ ½uðiÞ; uði þ 1Þ; :::; uði þ m 1Þu0ðiÞ; i ¼ 1; :::; Nm þ 1 ð5Þ where uðiÞ represents one channel’s EMG reading. M is vector dimension. As suggested by Pincus [25], in our study, the vector dimension m was set to 2. X m i represents m consecutive u values, commencing with the ith point and generalized by removing a baseline: u0ðiÞ ¼
1 X 1m uðiþ jÞ mj¼0
ð6Þ
m Given vector X m i , calculate the similarity degree Dij of its neighboring vector X m to it through the similarity j
degree defined by a fuzzy function: m Dm ij ¼ μ dij ; r
ð7Þ
m
where dij is the maximum absolute difference of correm sponding scalar components of X m i ; X j , defined as follow: m m dij ¼ d½X m i ; X j ¼ maxk A ð0;m1Þ uði þ kÞ u0ðiÞ ðuðjþ kÞ u0ðjÞÞ; ð8Þ For each vector X m i (i¼1, …, N m þ1), averaging all the similarity degree of its neighboring vectors X m j (j ¼1, …, N m þ1, and jai), we get: NX m
1 φm i ðr Þ ¼ ðN m 1Þ
Dm ij
ð9Þ
j ¼ 1;j a i
construct φm ðr Þ ¼ ðN mÞ 1
NX m
φm i ðr Þ
ð10Þ
i¼1
and φm þ 1 ðr Þ ¼ ðN mÞ 1
NX m
þ1 φm ðr Þ i
ð11Þ
i¼1
And then we can define the parameter FuzzyEn (m, r) of the time series as FuzzyEnðm; r Þ ¼ lim ln φm ðr Þ ln φm þ 1 ðr Þ ð12Þ N-1
which, for finite datasets, can be estimated by the statistic: FuzzyEnðm; r; N Þ ¼ ln φm ðr Þ ln φm þ 1 ðr Þ
ð13Þ
2.4.4. Statistical analysis Conventional statistical methods were used to calculate means and standard deviation (SD). Values were given as mean (SD) throughout. SEMG-based parameters were compared in relative terms via one-way analysis of variance (oneway ANOVA). The Po0.1 criterion was used to establish statistical significance. The differences between HDBR45 and
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HDBR15 or HDBR30 were tested for significance by Paired t test. Significant differences between means were set at the Po0.05 level.
3. Results 3.1. Force Each subject’s maximum volunteer contraction (MVC) of pushing and pulling tasks on pre-HDBR5, HDBR15, HDBR30 and HDBR45 were compared, showing no significant change (one-way ANOVA, Pushing: P¼0.878; Pulling: P ¼0.709). The forces at each time point are displayed in Fig. 4. The shaded portions are the maintenance stage of MVC. 3.2. sEMG parameter 3.2.1. Time-domain analysis IEMG and RMS were calculated on the 5th day of preHDBR, the 15th, the 30th and the 45th days of HDBR
(Table 1). Both DT in pushing task, as well as TB and BR in pulling task changed significantly in iEMG and RMS (oneway ANOVA, DT and TB: P o0.05, BR: Po0.1). During HDBR, iEMG and RMS had significant decreases. For example, the maximum decreases of DT’s iEMG and RMS were about 67% and 65% respectively. However, this kind of decrease was non-monotonic in that HDBR45 had a recovering effect compared with the minimum value in HDBR15 or HDBR30 (Fig. 5). Taking DT as an example, it was detected that RMS in HDBR45 was significantly higher than that in HDBR30 (Paired t test, p o0.05). 3.2.2. Frequency-domain analysis MDF and MNF were calculated on the 5th day of preHDBR, the 15th, the 30th and the 45th day of HDBR (Table 2). Significant change was found only in MNF of DT (one-way ANOVA, P o0.05) in pushing task. On pre-HDBR5, MNF showed maximum value. During HDBR, MNF had a significant decrease by about 11%. Likewise, the decrease was non-monotonic because RMS of HDBR45 increased significantly compared with that of HDBR15 (Paired t test, p o0.05).
Fig. 4. One typical subject’s forces in 4 days (Upper left: pushing forces on pre-HDBR5, HDBR15, HDBR30 and HDBR45. Bottom left: pulling forces on preHDBR5, HDBR15, HDBR30 and HDBR45.) And statistical forces of 8 subjects in 4 days (Upper right: statistics of pushing forces. Bottom right: statistics of pulling forces).
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Table 1 Analysis of time-domain in pushing and pulling tasks using iEMG and RMS. Task iEMG
Pushing
Pulling
RMS
Pushing
Pulling
TB DT EP TP BR ECR TB DT EP TP BR BB TB DT EP TP BR ECR TB DT EP TP BR BB
pre-HDBR5
HDBR15
HDBR30
HDBR45
1.46 1.48 1.56 0.34 0.73 1.14 1.56 0.39 0.54 1.13 2.16 2.16 1.93 1.90 1.96 0.45 0.92 1.82 1.90 0.50 0.74 1.44 2.62 2.59
1.57 0.49 1.25 0.18 0.37 1.19 0.79 0.62 0.46 0.72 1.97 2.03 2.09 0.66 1.58 0.24 0.50 1.58 1.00 0.79 0.62 0.95 2.42 2.44
1.38 0.62 1.27 0.23 0.35 1.00 0.88 0.38 0.48 0.99 1.67 2.21 1.85 0.83 1.59 0.30 0.47 1.30 1.12 0.51 0.65 1.26 2.07 2.62
1.25 0.95 1.59 0.30 0.39 0.99 1.03 0.26 0.41 1.02 1.97 2.23 1.63 1.25 1.96 0.40 0.52 1.30 1.30 0.34 0.55 1.30 2.42 2.69
(0.37) (0.63) (0.38) (0.16) (0.67) (0.45) (0.84) (0.29) (0.35) (0.58) (0.31) (0.73) (0.44) (0.76) (0.41) (0.22) (0.80) (0.55) (0.91) (0.38) (0.48) (0.66) (0.33) (0.75)
(0.40) (0.17) (0.28) (0.07) (0.16) (0.42) (0.25) (0.47) (0.31) (0.21) (0.29) (0.85) (0.48) (0.24) (0.35) (0.10) (0.21) (0.54) (0.31) (0.58) (0.43) (0.27) (0.31) (0.87)
(0.44) (0.47) (0.50) (0.09) (0.15) (0.49) (0.33) (0.19) (0.30) (0.63) (0.46) (0.96) (0.52) (0.63) (0.58) (0.12) (0.22) (0.62) (0.40) (0.27) (0.41) (0.77) (0.52) (1.00)
(0.64) (0.56) (0.58) (0.18) (0.19) (0.36) (0.49) (0.16) (0.15) (0.65) (0.41) (0.65) (0.73) (0.69) (0.64) (0.25) (0.25) (0.47) (0.60) (0.21) (0.20) (0.78) (0.44) (0.67)
ANOVA
nn
nn
n
nn
nn
n
nnnPo 0.01. n Po 0.1. nn Po 0.05.
Fig. 5. Variations of iEMG and RMS in pushing and pulling tasks.
3.2.3. FuzzyEn analysis FuzzyEn was chosen as a measurement of non-linear analysis (Table 3). The variation of non-linear characteristic described by FuzzyEn had the same trend with that of timedomain and frequency-domain parameters (Figs. 5 and 6).
FuzzyEn of both DT and TB in pushing task, as well as TB in pulling task changed significantly (one-way ANOVA, pushing task: DT and TB: Po0.05, pulling task: TB: Po0.1). During HDBR, FuzzyEn had a significant decrease. For example, the maximum decrease of DT was about 58%. Also,
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A. Fu et al. / Acta Astronautica 120 (2016) 260–269
Table 2 Analysis of frequency-domain in pushing and pulling tasks using MDF and MNF. Task MDF
Pushing
Pulling
MNF
Pushing
Pulling
TB DT EP TP BR ECR TB DT EP TP BR BB TB DT EP TP BR ECR TB DT EP TP BR BB
pre-HDBR5
HDBR15
HDBR30
HDBR45
81.57 83.10 50.33 72.32 92.02 140.85 67.50 65.94 50.02 73.97 68.64 70.32 163.95 164.05 139.74 156.87 171.83 207.71 154.27 151.75 141.86 152.63 154.34 155.99
76.51 62.82 45.99 70.65 108.25 148.53 68.28 62.01 47.13 81.45 65.53 69.90 153.58 146.03 134.88 154.64 184.40 213.66 153.05 147.85 144.91 165.51 154.14 153.19
80.84 69.52 46.88 70.15 110.78 141.70 67.13 69.24 46.05 79.06 73.58 61.73 163.67 153.36 138.11 153.95 191.42 206.49 154.11 162.42 139.69 166.52 161.21 143.03
80.54 76.90 48.22 71.99 96.33 131.69 65.25 63.19 50.85 75.10 70.38 68.67 161.15 164.02 132.43 159.18 179.16 204.53 147.44 151.16 149.80 153.49 159.97 147.55
(11.40) (15.09) (6.50) (9.62) (16.83) (21.91) (6.95) (8.11) (7.11) (8.20) (8.18) (15.71) (14.45) (21.28) (15.78) (16.35) (18.31) (19.42) (14.23) (17.29) (9.22) (10.99) (8.51) (16.50)
(7.37) (12.19) (8.71) (5.94) (23.51) (24.00) (3.68) (6.50) (5.78) (6.53) (6.60) (18.47) (6.41) (17.03) (12.08) (11.02) (23.52) (22.16) (8.47) (8.41) (12.25) (13.77) (10.04) (19.79)
(8.09) (10.45) (6.34) (9.30) (30.10) (23.99) (6.63) (9.90) (5.07) (10.13) (15.20) (8.00) (7.94) (14.24) (9.77) (12.19) (24.07) (22.34) (12.15) (14.09) (13.36) (16.12) (17.60) (10.14)
(11.66) (11.35) (7.73) (6.72) (29.07) (23.77) (10.16) (9.57) (7.64) (12.19) (8.36) (7.96) (17.29) (15.18) (11.97) (14.71) (21.40) (24.92) (21.51) (16.96) (13.60) (18.00) (9.88) (13.89)
ANOVA
nn
nPo 0.1; nnnP o0.01. nn P o0.05.
Table 3 Analysis of FuzzyEn in pushing and pulling tasks. Task FuzzyEn
Pushing
Pulling
TB DT EP TP BR ECR TB DT EP TP BR BB
pre-HDBR5
HDBR15
HDBR30
HDBR45
ANOVA
0.44 0.40 0.25 0.15 0.38 0.75 0.33 0.12 0.12 0.42 0.50 0.44
0.43 0.17 0.21 0.07 0.29 0.68 0.24 0.18 0.08 0.28 0.42 0.45
0.34 0.17 0.22 0.08 0.22 0.67 0.24 0.14 0.08 0.32 0.51 0.38
0.34 0.28 0.22 0.12 0.22 0.64 0.26 0.09 0.08 0.32 0.45 0.42
nn
(0.08) (0.10) (0.06) (0.07) (0.24) (0.07) (0.08) (0.06) (0.09) (0.09) (0.11) (0.07)
(0.04) (0.11) (0.05) (0.03) (0.15) (0.25) (0.06) (0.11) (0.05) (0.14) (0.07) (0.10)
(0.08) (0.12) (0.06) (0.03) (0.09) (0.29) (0.07) (0.10) (0.07) (0.14) (0.08) (0.08)
(0.08) (0.17) (0.06) (0.11) (0.17) (0.23) (0.07) (0.05) (0.03) (0.11) (0.07) (0.06)
nnn
n
n
Po 0.1. P o0.05. nnn Po 0.01. nn
this kind of decrease was non-monotonic because HDBR45 had a recovering effect compared with the minimum value on HDBR15 or HDBR30 (Fig. 7). Taking DT as an example, it was detected that FuzzyEn on HDBR45 was significantly higher than that in HDBR15 (Paired t test, po0.05). To conclude, the results showed that: (1) significant change was not found at force level, but was found in timedomain, frequency-domain and non-linear parameters, (2) TB, DT and BR were the three main muscles showing significant changes in electrophysiological parameters, (3) during HDBR, sEMG parameters showed significant decreases, but this kind of decrease is non-monotonic. Comparing with minimum value, it was found that HDBR45 showed a recovering effect.
4. Discussion From force level, many previous studies revealed a decreasing MVC in body limbs after prolonged simulated microgravity [6,18,26]. For example, after 42 days of bed rest, the maximum strength of lower limb muscles decreased by about 30% [6]. However, in this study, the experiment of 45-day simulated microgravity revealed no force change on upper limbs. The finding is in agreement with a previous result, which showed that no force decrease had been observed in upper limbs’ movements after a 20-day bed rest [15]. However, significant changes of sEMG parameters were found during HDBR in both
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pushing and pulling tasks. We imply that disuse atrophy probably will result in a reduction of muscular motor units’ recruitment capacity that fewer motor units were activated, and maximal firing frequency of motor units decreased [29–30]. Thus it explains why there was significant decrease of IEMG and RMS in the earlier stage of HDBR. IEMG and RMS represent the total active effect of motor units, which might reflect the whole performance of muscles. Considering that a previous study has showed there was a positive correlation between force and EMG parameters [31], we suggest that electrophysiological parameters, compared with muscle force, are probably more sensitive to muscle deconditioning in pushing and pulling tasks during prolonged microgravity. Though no force losing was observed in our study, muscle atrophy could still exist. A large number of studies have shown that simulated or actual microgravity leads to significant muscle atrophy in lower limbs [6–9]. And within the same time period in HDBR, muscle atrophy of lower limbs is more serious than that of upper limbs with a more pronounced decrease in mCSA [32]. This is mainly due to the fact that in human bodies, muscle atrophy preferentially occurs in postural muscles that cluster on back and lower limbs, since those muscles support the weight of the body on ground [5,33–37]. LeBlanc has reported that no change occurred in upper limbs during 17 weeks of bed rest [8]. However, a 90-day HDBR revealed a reduction of
Fig. 6. Variations of MPF in DT in pushing task.
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7% in forearm mCSA [36]. In the present study, we observed changes in sEMG, which could contribute to the investigation of muscle atrophy in upper limbs. During HDBR, both DT in pushing task, as well as TB and BR in pulling task had significant decreases in IEMG and RMS. However, this kind of decrease was nonmonotonic in that recovering effect occurred during the last stage, which means IEMG and RMS rise again. Previous studies have reported that the balance between protein synthesis and degradation would be disturbed in both simulated and actual microgravity, and then the imbalance caused muscle atrophy both in human and animal [38,39]. However, this imbalance can be recovered to a certain degree. Indeed, hind limbs suspension in rats has proved it. After 2 weeks of accommodation, the equilibrium between synthesis and degradation was achieved again [40–42]. Although we have no direct evidence proving that this recovering effect in EMG results is caused by the regaining equilibrium between protein synthesis and degradation, this finding is still meaningful since it suggests that the subjects involved in this study might suffer slight muscle atrophy during the early stage and they might relieve from this atrophy, or even acquire certain but incomplete degree of recovery in late stage. MNF is widely considered to have a linear relationship with muscle fiber conductive velocity (MFCV) [43]. MFCV decreases when muscle is with disuse atrophy and improves when the atrophy is relieved [44]. A previous study provided evidence that FuzzyEn had a similar trend to that of MNF during the development of muscle fatigue [45]. We only find that MNF of DT changed significantly in pushing task, but FuzzyEn of both DT and TB in pushing task as well as TB in pulling task changed significantly. These findings indicate that FuzzyEn is more sensitive than MNF in our study. The change of FuzzyEn reflects that the motor units’ active order is influenced by HDBR, which might give another perspective to support the possibility of muscle atrophy. Furthermore, only three muscles had significant changes. We speculate that DT and TB are main muscles to maintain the stability of shoulder joint, so they are more sensitive than other muscles. Another possible reason is that these two tasks are not complex enough to detect more muscle condition changing. If there is a more complex task that needs stronger muscle coordination, the change of EMG parameters might be detected significantly in more muscles.
Fig. 7. Variations of FuzzyEn in pushing and pulling tasks.
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Lots of previous studies have stated that atrophy of lower limbs is described by an exponential function of time [46–50]. Though common agreement exists regarding the general picture, the time course of upper limbs atrophy has been still less well studied. In the present study, time-domain and frequency-domain parameters of sEMG combined with nonlinear parameter suggests a consistent non-monotonic decrease trend of upper limb muscle deconditioning in 45-day HDBR. But the specific day when the most serious atrophy occurred could not be provided, since not only the testing time points were too sparse, but also different muscles had different time responses. Furthermore, this study aims at 45 days of HDBR. However, it is still possible that there is another variation trend of upper limb muscle atrophy after 45 days in simulated or actual microgravity.
5. Conclusion The study investigated pushing and pulling tasks in 45 days HDBR and acquired MVCs and sEMG from each subject’s right arm on pre-HDBR5, HDBR15, HDBR30 and HDBR45 respectively. Significant change was not found in MVC of upper limbs, but was found in sEMG parameters. During HDBR, sEMG parameters showed significant decrease, but this kind of decrease was non-monotonic. Comparing with minimum value, a recovery rise of HDBR45 was detected. The results are expected to provide new perspective to the study of upper limbs affected by actual or simulated microgravity.
Acknowledgments The authors express gratitude to all the subjects who participated in the study and endured the long term HDBR, the staff of the China Astronaut Research and Training Center, and the nurses who cared for the subjects. It is impossible to finish the work without their efforts. This work is supported by the National Basic Research Program of China (973 Program no.2011CB711000), the National Natural Science Foundation of China (Grant no. 31400843) and the Advanced Space Medico-engineering Research Project of China (no.2013SY54B0701). None of the authors have potential conflicts of interest to be disclosed.
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Acta Astronautica journal homepage: www.elsevier.com/locate/actaastro
Electromyography-based analysis of human upper limbs during 45-day head-down bed-rest Anshuang Fu a, Chunhui Wang b,1, Hongzhi Qi a,n, Fan Li b, Zheng Wang b, Feng He a, Peng Zhou a, Shanguang Chen b,n, Dong Ming a,n a
Department of Biomedical Engineering, Tianjin University, Tianjin, People’s Republic of China National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, People’s Republic of China b
a r t i c l e in f o
abstract
Article history: Received 24 April 2015 Received in revised form 29 October 2015 Accepted 8 December 2015 Available online 4 January 2016
Muscle deconditioning occurs in response to simulated or actual microgravity. In spaceflight, astronauts become monkey-like for mainly using their upper limbs to control the operating system and to complete corresponding tasks. The changes of upper limbs' athletic ability will directly affect astronauts’ working performance. This study investigated the variation trend of surface electromyography (sEMG) during prolonged simulated microgravity. Eight healthy males participating in this study performed strict 45-day head-down bed-rest (HDBR). On the 5th day of pre-HDBR, and the 15th, the 30th and the 45th days of HDBR, the subjects performed maximum pushing task and maximum pulling task, and sEMG was collected from upper limbs synchronously. Each subject’s maximum volunteer contractions of both the tasks during these days were compared, showing no significant change. However, changes were detected by sEMG-based analysis. It was found that integrated EMG, root mean square, mean frequency, fuzzy entropy of deltoid, and fuzzy entropy of triceps brachii changed significantly when comparing pre-HDBR with HDBR. The variation trend showed a recovery tendency after significant decline, which is inconsistent with the monotonic variation of lower limbs that was proved by previous research. These findings suggest that EMG changes in upper limbs during prolonged simulated microgravity, but has different variation trend from lower limbs. & 2015 IAA. Published by Elsevier Ltd. All rights reserved.
Keywords: HDBR sEMG Upper limbs Muscle deconditioning Non-linear
1. Introduction Human beings have been living and evolving on the earth with 1G gravitational acceleration for ages. The morphological specificity, structure and function of our physiological system have adapted with the 1G condition. During long-duration spaceflights with microgravity, there are some physiological changes occurring in the body, especially muscle atrophy and muscular endurance drop [1,2]. The series changes of muscle
n
Corresponding authors. 1 The author contributes as equal as the first author and is the co-first author. http://dx.doi.org/10.1016/j.actaastro.2015.12.007 0094-5765/& 2015 IAA. Published by Elsevier Ltd. All rights reserved.
system, generally termed as muscle deconditioning, consist mainly of loss of muscle mass, force and power, increased muscle fatigability and abnormal reflex pattern [3]. The deconditioning not only lowers astronauts’ working performance or even results in the failure of entire mission in space, but also has bad influence on their health after coming back to earth. Therefore, during the past several decades, it has become a core research focus of space physiology to understand the mechanism of physiological changes and to develop countermeasures to alleviate deconditioning progression through investigation on motor system [2]. However, due to restrictions in spacecraft, it is impossible to run integrated experiments on sufficient subjects during spaceflight. In order to overcome the difficulty, scientists discovered that
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head-down bed-rest that can lead to redistribution of body fluid was an appropriate model to simulate many physiological responses to microgravity, including cardiovascular changes, bone mass loss and muscle change. First adopted in the 1970s, HDBR has a history of about 40 years [4]. Change of human body’s movement ability is mainly reflected in two aspects: the change of upper limbs’ athletic ability and the change of lower limbs’ athletic ability. And these changes of athletic ability are usually reflected via muscle force and muscle cross-section area (mCSA). Since muscles with antigravity function mainly cluster on back and lower limbs [5–7], studies on human physiological system under microgravity or simulated microgravity focus more frequently on lower limbs. Leg muscles showed significant atrophy in the early weeks of bed rest [6–9]. This atrophy is different from the most severe type of muscle atrophy called neurogenic atrophy that usually results from an injury or disease of a nerve that connects with muscle. And the atrophy we relate to our study is another type of atrophy called disuse atrophy, which results from long-duration muscle wasting without enough physical activity. People with sedentary jobs, medical conditions and long space missions that limit their movement or decrease activity levels might lose muscle tone and develop disuse atrophy [28]. A series of experiments using MRI measurements revealed a significantly decreased muscle cross-section area (CSA) of lower limbs with HDBR [10–14]. A decrease in power as well as a decrease in both force and velocity was also observed in lower limb, but not in upper limb movements after a 20-day bed rest [15]. LeBlanc also reported that no change had occurred in upper limbs during 17 weeks of bed rest [8]. However, in spaceflight, astronauts mainly use their upper limbs to control the operating system and to complete corresponding tasks [16]. Furthermore, they become monkeylike in space, using their hands and arms much more than they do when coming back on earth [17]. That is to say, the changes of upper limbs’ athletic ability will directly affect astronauts’ working performance. Up to now, few studies have focused on upper limbs during space missions. Therefore, for the sake of getting the whole picture of deconditioning in muscle system, it is important to investigate muscle deconditioning of upper limbs. Besides, few studies have associated the analysis of muscle system with sEMG, and the small number of sEMG studies have mainly focused on time domain and frequency domain [5,6,18]. But in theory, sEMG signal is widely considered to be chaotic or to contain chaotic elements since muscle contraction is affected by various factors, and a variety of physiological and pathological factors influencing sEMG are often mutable [19]. Hence, having been widely used in neurotic electrophysiology in recent years, non-linear method can give another perspective to understand the effect of muscle system under simulated microgravity [20]. For the above reasons, we investigated upper limbs during 45 days of 6° HDBR experiment and provided more comprehensive tracking of the changes in muscle system by analyzing time domain, frequency domain and non-linear parameters of sEMG under prolonged simulated microgravity.
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2. Method 2.1. Ethics Assessment Conclusion for Research Proposal by China Astronaut Research and Training Center Ethics Committee. China Astronaut Research and Training Center Ethics Committee passed the assessment and approved the experiment. After explaining experiment protocol, written informed consent was obtained from all the subjects. 2.2. Subjects Eight healthy non-smoking right-handed men in average aged at 27.13 years old (SD¼ 4.49, range: 22–34 years old), height 168.81 (SD ¼4.52, range: 165–173 cm) and body mass 61.63 kg (SD ¼5.80, range: 55–72 kg) participated in the 45-day 6° head-down bed-rest (HDBR). The subjects have no drug dependency, no history of infectious diseases, no hereditary diseases, and no mental and psychological diseases within three generations of their family. They received junior middle-school or above education, and have normal intelligence (Raven’s IQ test is more than 70 points). 2.3. Experimental paradigm The total duration of the experiment was 65–70 days (Fig. 1). Before the 45 days of HDBR, the subjects had been trained for 10 days to adapt to the experimental environment, and the period was called pre-HDBR. And then the subjects began the rigorous 45-day 6° HDBR. After HDBR, the 10–15 days of convalescence called post-HDBR was designed for their recovery. The duration of the postHDBR period depended on subject-specific recovery time. During the 45-day HDBR period, the subjects were restricted to absolute head-down recumbence and were not allowed to walk. All activities such as testing, taking food, drinking water, washing and defecation should be done under 6° HDBR condition. They were allowed to watch television or videos, listen to CDs or radio, play games and read books or magazines. To restrict the influence of nutrition on the results of the experiment, the subjects were fed with a fixed number of calories per kg of lean body mass during the HDBR and post-HDBR, but their daily water intake was not limited. The study designed single-handed pushing and pulling tasks (Fig. 2). BTE Primus RS and Biovison were used for isometric strength test and sEMG acquisition on the 5th day of pre-HDBR (pre-HDBR5), the 15th day of HDBR (HDBR15), the 30th day of HDBR (HDBR30) and the 45th day of HDBR (HDBR45). The instrument to measure sEMG was calibrated before data collection. Experiment preparation: before restricted HDBR, in order to set appropriate force for the following study, isotonic contraction method was used. The subjects were required to warm up in sports shoes, and then to fix their feet on the ground and to repeat pushing and pulling for 3 min. Force application: during restricted HDBR, the subjects were required to make their efforts to keep their body posture unchanged. Within 2 s after the start of the
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experiment, there should be a maximum volunteer contraction (MVC) applied rapidly and steadily by the subjects. Then they should keep the maximum force for another 8 s. Target muscles: triceps brachii (TB), deltoid (DT), ectopectoralis (EP), trapezius (TP), brachioradialis (BR) and extensor carpus radialis (ECR) in pushing task; triceps brachii (TB), deltoid (DT), ectopectoralis (EP), trapezius (TP), brachioradialis (BR) and biceps brachii (BB) in pulling task (Fig. 3). Repetition times: each type of force was repeated for 3 times. Test duration: a single force test lasted for 10 s. Rest requirement: a 30-s rest was required between each test of the same force type. A rest for no less than 3 min was required between the tests of different force types. Our study is only a part of the whole HDBR experiment, which relates to muscle and EMG research. To get more comprehensive understanding of the whole experiment, you can read Changes in the Diurnal Rhythms during a 45Day Head Down Bed Rest (2012) and Effects of Head-Down Bed Rest on the Executive Functions and Emotional Response (2012). 2.4. Data analysis Signals were sampled at 2000 Hz per channel, fullwave rectified and then Butterworth filtered at 5–800 Hz. Since some subjects did not maintain their forces for exact 8 s (shorter or longer than 8 s), the stable phase of 6 s was extracted for the following calculation. All the EMG
analyses were conducted in Matlab using customwritten codes. 2.4.1. Time-domain analysis of EMG Integrated EMG (IEMG) is normally used as an onset detection index in clinical application. It is related to EMG signal sequence firing point reflecting the contraction property of muscle. IEMG is defined as a summation of absolute values of EMG signal amplitude, which can be expressed as IEMG ¼
N X
Pushing
ð1Þ
where xi represents the ith sample of EMG signal and N is the length of the EMG signal. Root mean square (RMS) relates to constant force and non-fatiguing contraction. It is also similar to standard deviation method. It is defined by following equation: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N u1 X RMS ¼ t x2 ð2Þ Ni¼1 i where xi represents the ith sample of EMG signal and N is the length of the EMG. 2.4.2. Frequency-domain analysis of EMG Frequency or spectral domain features are mostly used to study the fatigue of muscle and motor unit recruitment analysis. Power spectral density (PSD) becomes a major analysis in frequency domain. Two widely used variables of the PSD are mean frequency (MNF) and median frequency (MDF). MNF is the average frequency that is calculated as the sum of product of the EMG power spectrum and the frequency divided by total sum of the spectrum intensity, which can be calculated as , M M X X MNF ¼ f j Pj Pj ð3Þ j¼1
Fig. 1. Total duration of the experiment, including pre-HDBR, HDBR and post-HDBR.
jxi j
i¼1
j¼1
where f j is the frequency of the spectrum at the frequency bin j, P j is the EMG power spectrum at the frequency bin j, and M is the length of frequency bin. Frequency bin represents f j þ 1 f j . In our study, the sample frequency was 2000 Hz, and the data length was 6 s. So we did
Pulling
Fig. 2. Schematic diagram of the experiment. Left: the subject was performing pushing task. Right: the subject was performing pulling task.
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Fig. 3. Signal acquisition from target muscles in pulling task.
2000*6 points FFT, thus the frequency bin is 1/6 Hz, and M is 2000*6/2, the half of FFT points. MDF is the frequency at which the spectrum is divided into two regions with equal amplitude. It can be expressed as M DF X
Pj ¼
j¼1
M X j ¼ MDF
Pj ¼
M 1X PJ 2j¼1
ð4Þ
2.4.3. Non-linear analysis of EMG Fuzzy entropy (FuzzyEn) is a measurement for complexity. More details obtained by fuzzy functions make FuzzyEn a more accurate entropy definition. In addition, FuzzyEn has greater relative consistency and less dependency on data length. Both theoretical analysis and experimental results show that FuzzyEn provides an improved evaluation of signal complexity and is more conveniently and powerfully applied to short time series contaminated by noise [21]. Many studies have shown that FuzzyEn could be a good tool applied in electrophysiology, such as EEG, EMG and HRV, signal processing [22–24]. Low FuzzyEn value is associated with a low complexity of time series, and high FuzzyEn value with a high complexity of time series. For time series uðiÞ: 1 ri r N form vectors: Xm i ¼ ½uðiÞ; uði þ 1Þ; :::; uði þ m 1Þu0ðiÞ; i ¼ 1; :::; Nm þ 1 ð5Þ where uðiÞ represents one channel’s EMG reading. M is vector dimension. As suggested by Pincus [25], in our study, the vector dimension m was set to 2. X m i represents m consecutive u values, commencing with the ith point and generalized by removing a baseline: u0ðiÞ ¼
1 X 1m uðiþ jÞ mj¼0
ð6Þ
m Given vector X m i , calculate the similarity degree Dij of its neighboring vector X m to it through the similarity j
degree defined by a fuzzy function: m Dm ij ¼ μ dij ; r
ð7Þ
m
where dij is the maximum absolute difference of correm sponding scalar components of X m i ; X j , defined as follow: m m dij ¼ d½X m i ; X j ¼ maxk A ð0;m1Þ uði þ kÞ u0ðiÞ ðuðjþ kÞ u0ðjÞÞ; ð8Þ For each vector X m i (i¼1, …, N m þ1), averaging all the similarity degree of its neighboring vectors X m j (j ¼1, …, N m þ1, and jai), we get: NX m
1 φm i ðr Þ ¼ ðN m 1Þ
Dm ij
ð9Þ
j ¼ 1;j a i
construct φm ðr Þ ¼ ðN mÞ 1
NX m
φm i ðr Þ
ð10Þ
i¼1
and φm þ 1 ðr Þ ¼ ðN mÞ 1
NX m
þ1 φm ðr Þ i
ð11Þ
i¼1
And then we can define the parameter FuzzyEn (m, r) of the time series as FuzzyEnðm; r Þ ¼ lim ln φm ðr Þ ln φm þ 1 ðr Þ ð12Þ N-1
which, for finite datasets, can be estimated by the statistic: FuzzyEnðm; r; N Þ ¼ ln φm ðr Þ ln φm þ 1 ðr Þ
ð13Þ
2.4.4. Statistical analysis Conventional statistical methods were used to calculate means and standard deviation (SD). Values were given as mean (SD) throughout. SEMG-based parameters were compared in relative terms via one-way analysis of variance (oneway ANOVA). The Po0.1 criterion was used to establish statistical significance. The differences between HDBR45 and
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HDBR15 or HDBR30 were tested for significance by Paired t test. Significant differences between means were set at the Po0.05 level.
3. Results 3.1. Force Each subject’s maximum volunteer contraction (MVC) of pushing and pulling tasks on pre-HDBR5, HDBR15, HDBR30 and HDBR45 were compared, showing no significant change (one-way ANOVA, Pushing: P¼0.878; Pulling: P ¼0.709). The forces at each time point are displayed in Fig. 4. The shaded portions are the maintenance stage of MVC. 3.2. sEMG parameter 3.2.1. Time-domain analysis IEMG and RMS were calculated on the 5th day of preHDBR, the 15th, the 30th and the 45th days of HDBR
(Table 1). Both DT in pushing task, as well as TB and BR in pulling task changed significantly in iEMG and RMS (oneway ANOVA, DT and TB: P o0.05, BR: Po0.1). During HDBR, iEMG and RMS had significant decreases. For example, the maximum decreases of DT’s iEMG and RMS were about 67% and 65% respectively. However, this kind of decrease was non-monotonic in that HDBR45 had a recovering effect compared with the minimum value in HDBR15 or HDBR30 (Fig. 5). Taking DT as an example, it was detected that RMS in HDBR45 was significantly higher than that in HDBR30 (Paired t test, p o0.05). 3.2.2. Frequency-domain analysis MDF and MNF were calculated on the 5th day of preHDBR, the 15th, the 30th and the 45th day of HDBR (Table 2). Significant change was found only in MNF of DT (one-way ANOVA, P o0.05) in pushing task. On pre-HDBR5, MNF showed maximum value. During HDBR, MNF had a significant decrease by about 11%. Likewise, the decrease was non-monotonic because RMS of HDBR45 increased significantly compared with that of HDBR15 (Paired t test, p o0.05).
Fig. 4. One typical subject’s forces in 4 days (Upper left: pushing forces on pre-HDBR5, HDBR15, HDBR30 and HDBR45. Bottom left: pulling forces on preHDBR5, HDBR15, HDBR30 and HDBR45.) And statistical forces of 8 subjects in 4 days (Upper right: statistics of pushing forces. Bottom right: statistics of pulling forces).
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Table 1 Analysis of time-domain in pushing and pulling tasks using iEMG and RMS. Task iEMG
Pushing
Pulling
RMS
Pushing
Pulling
TB DT EP TP BR ECR TB DT EP TP BR BB TB DT EP TP BR ECR TB DT EP TP BR BB
pre-HDBR5
HDBR15
HDBR30
HDBR45
1.46 1.48 1.56 0.34 0.73 1.14 1.56 0.39 0.54 1.13 2.16 2.16 1.93 1.90 1.96 0.45 0.92 1.82 1.90 0.50 0.74 1.44 2.62 2.59
1.57 0.49 1.25 0.18 0.37 1.19 0.79 0.62 0.46 0.72 1.97 2.03 2.09 0.66 1.58 0.24 0.50 1.58 1.00 0.79 0.62 0.95 2.42 2.44
1.38 0.62 1.27 0.23 0.35 1.00 0.88 0.38 0.48 0.99 1.67 2.21 1.85 0.83 1.59 0.30 0.47 1.30 1.12 0.51 0.65 1.26 2.07 2.62
1.25 0.95 1.59 0.30 0.39 0.99 1.03 0.26 0.41 1.02 1.97 2.23 1.63 1.25 1.96 0.40 0.52 1.30 1.30 0.34 0.55 1.30 2.42 2.69
(0.37) (0.63) (0.38) (0.16) (0.67) (0.45) (0.84) (0.29) (0.35) (0.58) (0.31) (0.73) (0.44) (0.76) (0.41) (0.22) (0.80) (0.55) (0.91) (0.38) (0.48) (0.66) (0.33) (0.75)
(0.40) (0.17) (0.28) (0.07) (0.16) (0.42) (0.25) (0.47) (0.31) (0.21) (0.29) (0.85) (0.48) (0.24) (0.35) (0.10) (0.21) (0.54) (0.31) (0.58) (0.43) (0.27) (0.31) (0.87)
(0.44) (0.47) (0.50) (0.09) (0.15) (0.49) (0.33) (0.19) (0.30) (0.63) (0.46) (0.96) (0.52) (0.63) (0.58) (0.12) (0.22) (0.62) (0.40) (0.27) (0.41) (0.77) (0.52) (1.00)
(0.64) (0.56) (0.58) (0.18) (0.19) (0.36) (0.49) (0.16) (0.15) (0.65) (0.41) (0.65) (0.73) (0.69) (0.64) (0.25) (0.25) (0.47) (0.60) (0.21) (0.20) (0.78) (0.44) (0.67)
ANOVA
nn
nn
n
nn
nn
n
nnnPo 0.01. n Po 0.1. nn Po 0.05.
Fig. 5. Variations of iEMG and RMS in pushing and pulling tasks.
3.2.3. FuzzyEn analysis FuzzyEn was chosen as a measurement of non-linear analysis (Table 3). The variation of non-linear characteristic described by FuzzyEn had the same trend with that of timedomain and frequency-domain parameters (Figs. 5 and 6).
FuzzyEn of both DT and TB in pushing task, as well as TB in pulling task changed significantly (one-way ANOVA, pushing task: DT and TB: Po0.05, pulling task: TB: Po0.1). During HDBR, FuzzyEn had a significant decrease. For example, the maximum decrease of DT was about 58%. Also,
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Table 2 Analysis of frequency-domain in pushing and pulling tasks using MDF and MNF. Task MDF
Pushing
Pulling
MNF
Pushing
Pulling
TB DT EP TP BR ECR TB DT EP TP BR BB TB DT EP TP BR ECR TB DT EP TP BR BB
pre-HDBR5
HDBR15
HDBR30
HDBR45
81.57 83.10 50.33 72.32 92.02 140.85 67.50 65.94 50.02 73.97 68.64 70.32 163.95 164.05 139.74 156.87 171.83 207.71 154.27 151.75 141.86 152.63 154.34 155.99
76.51 62.82 45.99 70.65 108.25 148.53 68.28 62.01 47.13 81.45 65.53 69.90 153.58 146.03 134.88 154.64 184.40 213.66 153.05 147.85 144.91 165.51 154.14 153.19
80.84 69.52 46.88 70.15 110.78 141.70 67.13 69.24 46.05 79.06 73.58 61.73 163.67 153.36 138.11 153.95 191.42 206.49 154.11 162.42 139.69 166.52 161.21 143.03
80.54 76.90 48.22 71.99 96.33 131.69 65.25 63.19 50.85 75.10 70.38 68.67 161.15 164.02 132.43 159.18 179.16 204.53 147.44 151.16 149.80 153.49 159.97 147.55
(11.40) (15.09) (6.50) (9.62) (16.83) (21.91) (6.95) (8.11) (7.11) (8.20) (8.18) (15.71) (14.45) (21.28) (15.78) (16.35) (18.31) (19.42) (14.23) (17.29) (9.22) (10.99) (8.51) (16.50)
(7.37) (12.19) (8.71) (5.94) (23.51) (24.00) (3.68) (6.50) (5.78) (6.53) (6.60) (18.47) (6.41) (17.03) (12.08) (11.02) (23.52) (22.16) (8.47) (8.41) (12.25) (13.77) (10.04) (19.79)
(8.09) (10.45) (6.34) (9.30) (30.10) (23.99) (6.63) (9.90) (5.07) (10.13) (15.20) (8.00) (7.94) (14.24) (9.77) (12.19) (24.07) (22.34) (12.15) (14.09) (13.36) (16.12) (17.60) (10.14)
(11.66) (11.35) (7.73) (6.72) (29.07) (23.77) (10.16) (9.57) (7.64) (12.19) (8.36) (7.96) (17.29) (15.18) (11.97) (14.71) (21.40) (24.92) (21.51) (16.96) (13.60) (18.00) (9.88) (13.89)
ANOVA
nn
nPo 0.1; nnnP o0.01. nn P o0.05.
Table 3 Analysis of FuzzyEn in pushing and pulling tasks. Task FuzzyEn
Pushing
Pulling
TB DT EP TP BR ECR TB DT EP TP BR BB
pre-HDBR5
HDBR15
HDBR30
HDBR45
ANOVA
0.44 0.40 0.25 0.15 0.38 0.75 0.33 0.12 0.12 0.42 0.50 0.44
0.43 0.17 0.21 0.07 0.29 0.68 0.24 0.18 0.08 0.28 0.42 0.45
0.34 0.17 0.22 0.08 0.22 0.67 0.24 0.14 0.08 0.32 0.51 0.38
0.34 0.28 0.22 0.12 0.22 0.64 0.26 0.09 0.08 0.32 0.45 0.42
nn
(0.08) (0.10) (0.06) (0.07) (0.24) (0.07) (0.08) (0.06) (0.09) (0.09) (0.11) (0.07)
(0.04) (0.11) (0.05) (0.03) (0.15) (0.25) (0.06) (0.11) (0.05) (0.14) (0.07) (0.10)
(0.08) (0.12) (0.06) (0.03) (0.09) (0.29) (0.07) (0.10) (0.07) (0.14) (0.08) (0.08)
(0.08) (0.17) (0.06) (0.11) (0.17) (0.23) (0.07) (0.05) (0.03) (0.11) (0.07) (0.06)
nnn
n
n
Po 0.1. P o0.05. nnn Po 0.01. nn
this kind of decrease was non-monotonic because HDBR45 had a recovering effect compared with the minimum value on HDBR15 or HDBR30 (Fig. 7). Taking DT as an example, it was detected that FuzzyEn on HDBR45 was significantly higher than that in HDBR15 (Paired t test, po0.05). To conclude, the results showed that: (1) significant change was not found at force level, but was found in timedomain, frequency-domain and non-linear parameters, (2) TB, DT and BR were the three main muscles showing significant changes in electrophysiological parameters, (3) during HDBR, sEMG parameters showed significant decreases, but this kind of decrease is non-monotonic. Comparing with minimum value, it was found that HDBR45 showed a recovering effect.
4. Discussion From force level, many previous studies revealed a decreasing MVC in body limbs after prolonged simulated microgravity [6,18,26]. For example, after 42 days of bed rest, the maximum strength of lower limb muscles decreased by about 30% [6]. However, in this study, the experiment of 45-day simulated microgravity revealed no force change on upper limbs. The finding is in agreement with a previous result, which showed that no force decrease had been observed in upper limbs’ movements after a 20-day bed rest [15]. However, significant changes of sEMG parameters were found during HDBR in both
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pushing and pulling tasks. We imply that disuse atrophy probably will result in a reduction of muscular motor units’ recruitment capacity that fewer motor units were activated, and maximal firing frequency of motor units decreased [29–30]. Thus it explains why there was significant decrease of IEMG and RMS in the earlier stage of HDBR. IEMG and RMS represent the total active effect of motor units, which might reflect the whole performance of muscles. Considering that a previous study has showed there was a positive correlation between force and EMG parameters [31], we suggest that electrophysiological parameters, compared with muscle force, are probably more sensitive to muscle deconditioning in pushing and pulling tasks during prolonged microgravity. Though no force losing was observed in our study, muscle atrophy could still exist. A large number of studies have shown that simulated or actual microgravity leads to significant muscle atrophy in lower limbs [6–9]. And within the same time period in HDBR, muscle atrophy of lower limbs is more serious than that of upper limbs with a more pronounced decrease in mCSA [32]. This is mainly due to the fact that in human bodies, muscle atrophy preferentially occurs in postural muscles that cluster on back and lower limbs, since those muscles support the weight of the body on ground [5,33–37]. LeBlanc has reported that no change occurred in upper limbs during 17 weeks of bed rest [8]. However, a 90-day HDBR revealed a reduction of
Fig. 6. Variations of MPF in DT in pushing task.
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7% in forearm mCSA [36]. In the present study, we observed changes in sEMG, which could contribute to the investigation of muscle atrophy in upper limbs. During HDBR, both DT in pushing task, as well as TB and BR in pulling task had significant decreases in IEMG and RMS. However, this kind of decrease was nonmonotonic in that recovering effect occurred during the last stage, which means IEMG and RMS rise again. Previous studies have reported that the balance between protein synthesis and degradation would be disturbed in both simulated and actual microgravity, and then the imbalance caused muscle atrophy both in human and animal [38,39]. However, this imbalance can be recovered to a certain degree. Indeed, hind limbs suspension in rats has proved it. After 2 weeks of accommodation, the equilibrium between synthesis and degradation was achieved again [40–42]. Although we have no direct evidence proving that this recovering effect in EMG results is caused by the regaining equilibrium between protein synthesis and degradation, this finding is still meaningful since it suggests that the subjects involved in this study might suffer slight muscle atrophy during the early stage and they might relieve from this atrophy, or even acquire certain but incomplete degree of recovery in late stage. MNF is widely considered to have a linear relationship with muscle fiber conductive velocity (MFCV) [43]. MFCV decreases when muscle is with disuse atrophy and improves when the atrophy is relieved [44]. A previous study provided evidence that FuzzyEn had a similar trend to that of MNF during the development of muscle fatigue [45]. We only find that MNF of DT changed significantly in pushing task, but FuzzyEn of both DT and TB in pushing task as well as TB in pulling task changed significantly. These findings indicate that FuzzyEn is more sensitive than MNF in our study. The change of FuzzyEn reflects that the motor units’ active order is influenced by HDBR, which might give another perspective to support the possibility of muscle atrophy. Furthermore, only three muscles had significant changes. We speculate that DT and TB are main muscles to maintain the stability of shoulder joint, so they are more sensitive than other muscles. Another possible reason is that these two tasks are not complex enough to detect more muscle condition changing. If there is a more complex task that needs stronger muscle coordination, the change of EMG parameters might be detected significantly in more muscles.
Fig. 7. Variations of FuzzyEn in pushing and pulling tasks.
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Lots of previous studies have stated that atrophy of lower limbs is described by an exponential function of time [46–50]. Though common agreement exists regarding the general picture, the time course of upper limbs atrophy has been still less well studied. In the present study, time-domain and frequency-domain parameters of sEMG combined with nonlinear parameter suggests a consistent non-monotonic decrease trend of upper limb muscle deconditioning in 45-day HDBR. But the specific day when the most serious atrophy occurred could not be provided, since not only the testing time points were too sparse, but also different muscles had different time responses. Furthermore, this study aims at 45 days of HDBR. However, it is still possible that there is another variation trend of upper limb muscle atrophy after 45 days in simulated or actual microgravity.
5. Conclusion The study investigated pushing and pulling tasks in 45 days HDBR and acquired MVCs and sEMG from each subject’s right arm on pre-HDBR5, HDBR15, HDBR30 and HDBR45 respectively. Significant change was not found in MVC of upper limbs, but was found in sEMG parameters. During HDBR, sEMG parameters showed significant decrease, but this kind of decrease was non-monotonic. Comparing with minimum value, a recovery rise of HDBR45 was detected. The results are expected to provide new perspective to the study of upper limbs affected by actual or simulated microgravity.
Acknowledgments The authors express gratitude to all the subjects who participated in the study and endured the long term HDBR, the staff of the China Astronaut Research and Training Center, and the nurses who cared for the subjects. It is impossible to finish the work without their efforts. This work is supported by the National Basic Research Program of China (973 Program no.2011CB711000), the National Natural Science Foundation of China (Grant no. 31400843) and the Advanced Space Medico-engineering Research Project of China (no.2013SY54B0701). None of the authors have potential conflicts of interest to be disclosed.
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