Simpletai chi exercise for improving elderly postural stability via complexity index analysis
Cheng-Wei Huang1, Wei-Hsin Chen2,Heng-Hui Chu1, Bernard C. Jiang3,Maysam Abbod4, and Jiann-Shing Shieh1,5,6,
1 Department of Mechanical Engineering, Yuan Ze University, Taiwan
2Department of Industrial Engineering and Management, YuanZe University, Taiwan
3 Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan
4School of Engineering and Design, Brunel University, London UB8 3PH, UK
5Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taiwan
6 Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan.
Abstract:The main purpose of this study is to investigate twice a day simple 9 steps tai chi effects of the center of pressure (COP) and physiological signals of eldery people. Data is collected from the COP signals, electromyography (EMG), and pulse oximetry for one minute every two weeks for the period of 12 weeks. The COP signals are analyzed using multivariate empirical mode decomposition and multivariate multiscale entropy in order to work out and compare the complexity index (CI). Subjects in this experiment are over 65 years old who are divided into 11 men and 7 women,the average age is 74±8.18 years old. In conclusion, it is found that tai chi exercise can improve human body balance by just walking some simple steps in our experiment. However, we cannot find any effect or improvement in the pulse oximetry and EMG signals analysis.
Keywords:Center of pressure (COP); Electromyography (EMG); multivariate empirical mode decomposition (MEMD; Multivariate Multi-scale entropy(MMSE); complexity index (CI); Six-minute walk test; Timed up and go; tai chi.
1 INTRODUCTION
Falls arecaused by many factors, including external factors such as environment and multiple drugs. The inherent risk factors such as age, a history of fall or recurring nearly fall without fall events, diseases, cognitive impairment, depression, sensory dysfunction, lower extremity muscle strength, balance and gait problems, the use of exercise walking aids, and daily living dysfunction. There are many studies concerned with prevention of falling which are based on the multifactorial assessment tool to assess falling [1]. Elderly falling prevention intervention strategies includes fall risk assessment, multifactorial intervention, sport training, adjustment of medication, improved environment and vision, supplemental Vitamin D and calcium, pacemaker and education.
Balance exercise such as tai chi not can only reduce falling occurrences but also can get other physical and mental health benefits at the same time. Reducing elderly mental disorder treatment medication can significantly reduce falls, but not easy to change the drug effects on the elderly. Environmental improvement intervention is suitable for the elderly who has fallen in the past. Cataract surgery can reduce most of the elderly’s bad fall caused by vision problems.However, exercising Tai chi can increase leg muscle strength and effectively improves lower extremity muscle strength. Tai chiexercise can also increase balance and reduce the risk of fallingfor the elderly [2].
Tai chiis one of Chinese traditional martial arts and a treasure of Chinese culture, it emphasizes on physical, mental and spiritual cultivation and practice.Tai chiis categorized as a recreational sport of low-middle intensity and aerobic, rhythmic, uncompetitive and whole-body performing mode is suitable for various ages and can be practiced anytime, and anywhere.
Center of pressure is an indirect measurement to measure Medial-Lateral (M-L)and Anterior-Posterior (A-P) [3] plane rocking postures. An effective nonlinear analysis method can effectively describe the posture stability and characteristics. The centre of pressure (COP)signals are collected for body rocking posture using a force platform, then the displacement of center of pressure displacement is calculated for the Medial-Lateral and Anterior-Posterior directions [4]. Furthermore, the M-L and A-P displacements are analyzed for individual analysis.Therefore, the main purpose of this study is to investigate twice a day simple 9 steps tai chiexercise effects on improving the center of pressure (COP) and physiological signals.
2METHOD
There are a few analysisalgorithms, such as multivariate empirical mode decomposition (MEMD) and multivariate multiscale entropy (MMSE) that are used to analyze the center of pressure (COP), electromyography (EMG) data and other vital signs.
2.1Analysis algorithms
In this study, MEMD and MMSE are used to analyze the COP, EMG and other data.
2.1.1Multivariate empirical mode decomposition
The MEMD algorithm basically comes from empirical mode decomposition (EMD), which was proposed by Huang et al [5], andhas been widely used in nonlinear and non-stationary data analysis based on the inherent characteristics of the time series.EMD has groups of intrinsic mode functions (IMF) corresponding to the modal function within the system in different mechanism of reaction. The first step to calculate the EMD is to find the upper and lower envelope, which is based on the signal in the local maximum and local minimum value to define. The upper envelope is the cubic spline to link with the local maximum value of the continuous curve, and the lower envelope is the cubic spline to connect to the local minimum value of the continuous curve. Average upper and lower envelope is seen as a signal of a trend. The average envelope can be regarded as a possible intrinsic mode functions.
The possible intrinsic mode functions separation from the signal process is called sifting process. The intrinsic mode functions obtained from the sifting process must satisfy the following two conditions, otherwise it must run the sifting process again [6]. (1) In the whole time series, the number of all local extrema and zero-crossingdifference cannot be more than one. (2) In any one time point, the average envelope must tend to zero.
If these two conditions are satisfied, the separated signals called intrinsic mode functions (IMF) will be recorded as c1. Original signal and c1 subtraction can get residual signal. The residual signal is used as input to the decomposition of next intrinsic mode functions. Repeating this process can gradually decomposedfor different intrinsic mode functions until the residual signal is a monotonic function.
Original signalafter the n times decomposition can get different n groupsIMFs signals.The relationship between decomposed by the empirical mode decomposition methodsignals and the original signal can be expressed by equation (1)
(1)
whereX(t) is the original signal, cirepresent the number i IMF, rnis number n times residual signal.
In order to diversely extend the application of EMD, MEMDmethod is beingdeveloped to decompose diversely nonlinear and non-stationary signals [7].MEMD method not only overcomes the limit of a single EMD input, but also addresses the computing burden in calculating the noise signal residues in different channels after adding white noise.In addition, it is similar to EMD as a dyadic filter is used for multivariable input of each channel.Also it has the advantage ofcorrectionto align the corresponding IMFs from different channels to the same frequency range[8].In the MEMD method, the average value of m (t) is calculatedby multivariate enveloped K direction vectorsas illustratedin equation (2)
(2)
were the is a multivariable envelope in the vectors, along the K direction that predict multi-channel input s (t) and multivariate IMF calculations via the s (t)-m (t) and stopping criteria. This process is repeated until all predicted standard signal is satisfiedto the standard EMD stops.
Recently a study [6] has reported that the human body center of pressure signal is lower than 2 Hz.Accordingly, taking theFourier transform for each IMFcan indicate the frequency rangeof the original signal, thereforea 2 Hz reference was chosen for analysing the IMFs.
2.1.2Multivariate multiscale entropy
Entropy-based algorithms for measuring the complexity of physiologic time series have been widely used. These have proved to be useful in discriminating between healthy and disease patients [9]. Normally,healthy systems generate much more complex outputs than disease ones.Traditional algorithms are single-scale based and fail to account for the multiple time scales inherent in physiologic systems.
Similar to EMD and MEMD, multivariate multiscale entropy (MMSE) is the evolution of multiscale entropy (MSE). MSE is developed by Costa et al [10] and used to evaluate the complexity of signals over different time scales. Based on the approximate entropy, this method uses sample entropy to quantify the regularity of the finite length time series, which has been applied effectively in analysis of physiology, biology, and geosciences data [11][12][13].
Given a one-dimensional discrete time series {Xi…Xn}, MSE firstly constructs multiple coarse-grained time series using the scale factor.Each element of the time series, {y(1)}, is according to equation (3)
(3)
For scale one, the time series{y(1)}is simply the original time series. The length of each coarse-grained time series is equal to the length of the original time series divided by the scale factor τ. Then, thesample entropyis calculated for each coarse-grained time series plotted as a function of the scale factor τ [10]. Sample entropy reflects the conditional probability that two sequences of m consecutive data points which are similar to each other which remains similar when one more consecutive point is included, and being “similar” means that the value of a specific measure of distance is less than r [8].The complexity degree of different combinations in each direction is measured in terms of the complexity index (CI), which is defined as the area under the MSE curve over all scales.
MMSEcalculates the relative complexity of the multichannel signals through the plot of the multivariate sample entropy.This makes it possible to assess structural complexity of multivariate physical or physiological systems, together with more degrees of freedom and enhanced rigor in the analysis. In the MMSE, if the multivariable sample entropy value is higher than most of the other scale, multivariable time-series is considered to be more complex than the others. This is the same with the original MSE [2].
2.2Experiment
The purpose of this experiment is to improve elderly balance by performing nine steps tai chi exercise, and test whether it can improve the heart and lung function. This work is done in corporation with Taiwan Taoyuan Senior Citizens’ Home.
Subjects in this experiment are over 65 years old who are 11 men and 7 women, and average age is 74±8.18 years old. The physical condition of these subjects is able to walk by themselves, do not need to be supported, and can stand on their own.
2.2.1Measuring instruments
In order to assess the improvement of body balance, the force platform measurement instrument developed in the previous research is being used. The system is named as Center of Pressure and Complexity Monitor System (CPCMS)(Fig. 1). The pressure measurement platform is designed to receive raw data of the balance signal. When the subject stands on the balance measurement device, the pressure measurement platform can receive raw COP signal.
Fig.1. Center of Pressure and Complexity Monitor System (CPCMS)
Pulse oximeter measures oxygen saturation is a noninvasive method to monitor the oxygen saturation. This device is small, light-weight, reusable and portableas shown in Fig. 2(a). The sensor is shown in Fig.2(b).This product is developed by Tatung Company. The device is connected to a computer to record SPO2 and heart rate.
Fig. 2 (a)The TatungPulse oximeter.(b)Tatung Pulse oximeter sensor
In this study the NeXus-10 Bluetooth Biofeedback (Fig. 3.) is used to collect EMG signal. Fig. 4.shows the experiment setup which shows the subject standing on the CPCMS and wearing Tatungpulse oximeter and NeXus-10 Bluetooth Biofeedback to collect all the data.
Fig. 3. NeXus-10 Bluetooth Biofeedback
Fig.4. Experiment schematic diagram. (a) Pulse oximeter sensor (b) CPCMS (c) EMG connect to NeXus-10.
2.2.2 Experimental procedure
The experiment period for the nine stepstai chi exercise experiment is 12 weeks conducted in the Taiwan Taoyuan Senior Citizens’ Home,data is collected every two weeks. Subjects do the exercise in the morning and evening every day. Rehabilitation division staff assists with the experiments. The experiment flowchart is shown in Fig.5.
Research group Subjects
Fig. 5.Experiment flowchart
The subjects are asked to stand on the CPCMS and connect the SPO2 and EMG device at the same time to measure COP, SPO2 and EMG. Each measurement lasts for 60 seconds, then 60 seconds break, then the measurement is repeated. This procedure is repeated more than three times. After finishing the procedure above, the subjects are asked to take 60 seconds break, then do the time-up-and-go test one time. The distance of walking is 3 meters. Take another 60 seconds break for next test, 6-minute walk test where the distance is measured. Since all the subjects are elderlies, there is a person who helps them in case of any danger.
2.3Data analysis
The analysis procedure consists of two parts. During the experiment, COP, SPO2and EMG signals are recorded. SPO2 signal provides blood oxygen percentage, heart rate, where the respiration rate and psychological stress index (PSI) are inferred. From the result, the second part involves analyzing the tai chi exercise affect on the subjects’ heart and lung function.The COP and EMG analysis process is shown in Fig. 6.
Human body center of pressure can be used to assess personal balance mechanism. Since the human body center of pressure signal frequency is very low, the force platform sample rate is 500 Hz so wethe signal is resampledat 50 Hz. After the calculation, M-L and A-P signals are aquired. MEMD is used to filter out noise and then use MMSE to calculate entropy value. Finally, the complexity index (CI) is calculated to assess the difference of human body balance ability due to thetai chi exercise.
Fig. 6. COP and EMG analysis process flow chart
3 ANALYSIS RESULT
The COPdisplacements in two directions of anterior-posterior (AP) and the medial-lateral (ML)are often used to characterize the COP stabilogram.Before performing the MSE and MMSE analysis, the IMF selection has to be made. MEMD is performed first, then theFourier transform for each IMF to get the frequency range. The frequencies for each IMF are shown in Table 1.IMF 5+6are selected since these indicate less than 2 Hz signals.
Table 1. IMF frequencies
CPCMSIMF 2 / IMF 3 / IMF 5 / IMF 6
X / 9.17±1.71 / 4.22±0.84 / 1.24±0.26 / 0.73±0.18
Y / 9.53±1.59 / 4.54±1.04 / 1.22±0.24 / 0.73±0.15
Tables2 and 3 show the results of MSE of AP and ML. From the results it is obvious that CI of the 6th measurement is higher than the 1st measurement in both AP and ML directions.MMSE is also used to combine both ML and AP directions into one result. Table 4 shows the CI value of the MMSE. From the result, it can be seen that CI of 6th is higher than 1st. For thetime-up-and-go test, the subjects need to stand up from a chair and walk to a 3-meter far target then walk back to sit down. The shorter finish time means the speed and stability of walking increase. Table 5indicates that the time to finish the procedure is shorter after 6th time measurement.The same trend can be seen for the time-up-and-go test, the longer distance is the better. As illustrated in Table 6. Tables 7 and 8show the analysis results for pulse oximetry. The heart rate, blood oxygen concentration and respiration rateare extracted from the pulse oximetry. The results indicate no regular changes or improvements in these four parts. The p-value for before and after doing thetai chi exercise are bigger than 0.05.
Table 2. CI of MSE in ML
MSE_MLnth / CI / P-value
1 / 5.651±2.22
2 / 5.458±1.842 / 1 vs 2 / 0.704
3 / 5.629±2.572 / 1 vs 3 / 0.971
4 / 6.288±2.678 / 1 vs 4 / 0.265
5 / 5.823±2.171 / 1 vs 5 / 0.691
6 / 6.687±2.122 / 1 vs 6 / 0.079
Note: 1 vs 6 represents a comparison of 1st with 6th
Table 3. CI of MSE in AP
MSE_APnth / CI / P-value
1 / 7.386±2.406
2 / 8.38±2.822 / 1 vs 2 / 0.134
3 / 7.492±2.797 / 1 vs 3 / 0.885
4 / 8.973±3.679 / 1 vs 4 / 0.072
5 / 8.331±2.253 / 1 vs 5 / 0.108
6 / 9.318±2.397 / 1 vs 6 / 0.020
Note: 1 vs 6 represents a comparison of 1st with 6th
Table 4. CI of MMSE
MMSEnth / CI / P-value
1 / 13.095±1.887
2 / 14.687±1.639 / 1 vs 2 / 0.003
3 / 14.042±1.559 / 1 vs 3 / 0.132
4 / 14.409±1.93 / 1 vs 4 / 0.013
5 / 13.858±1.557 / 1 vs 5 / 0.105
6 / 14.577±1.433 / 1 vs 6 / 0.006
Note: 1 vs 6 represents a comparison of 1st with 6th
Table 5.Time-up-and-go result
Time-up-and-gonth / Time(s) / P-value
1 / 16.195±6.124
2 / 15.54±4.42 / 1 vs 2 / 0.599
3 / 14.78±3.97 / 1 vs 3 / 0.216
4 / 14.1±3.27 / 1 vs 4 / 0.184
5 / 14.45±3.25 / 1 vs 5 / 0.241
6 / 13.72±3.14 / 1 vs 6 / 0.060
Note: 1 vs 6 represents a comparison of 1st with 6th
Table 6. 6-minute walk test
6-minute walknth / distance(m) / P-value
1 / 262.756±61.603
2 / 272.806±67.3 / 1 vs 2 / 0.234
3 / 289.243±70.513 / 1 vs 3 / 0.011
4 / 297.52±70.513 / 1 vs 4 / 0.000
5 / 295.698±75.078 / 1 vs 5 / 0.005
6 / 305.052±72.974 / 1 vs 6 / 0.000
Note: 1 vs 6 represents a comparison of 1st with 6th
Table 7.Pulse oximetryanalysis result for heart rate
HR1 / 2 / 3 / 4 / 5 / 6
1 / 88.4 / 79.65 / 79.66 / 87.69 / 74.84 / 90.2
2 / 72.13 / 71.03 / 71.03 / 72.39 / 74.99 / 73.99
3 / 85.09 / 90.35 / 86.51 / 99.24 / 83.78 / 94
4 / 58.71 / 60.29 / 107.31 / 42 / 81.74 / 50.33
5 / 69.94 / 76.76 / 78.87 / 65.5 / 64.38 / 66.67
6 / 92.42 / 85.69 / 73.77 / 75.1 / 73.92 / 75.45
7 / 66.36 / 69.73 / 62.84 / 70.52 / 65.93 / 72.03
8 / 77.61 / 78.1 / 84.6 / 83.08 / 97.19 / 76.94
9 / 19.87 / 85.84 / 86.65 / 88.55 / 89 / 92.98
10 / 78.1 / 85.34 / 84.6 / 83.08 / 77.34 / 73.1
11 / 88.08 / 85.84 / 86.65 / 88.55 / 81.18 / 70.48
12 / 84.6 / 85.41 / 81.66 / 77.08 / 90.01 / 90.14
13 / 59.39 / 59.2 / 72.07 / 79.51 / 74.86 / 69.37
14 / 67.53 / 83.54 / 87.29 / 98.8 / 80.75 / 72.46
15 / 65.86 / 59.66 / 53.45 / 58.94 / 55.64 / 59.46
16 / 60.13 / 58.49 / 55.11 / 64.09 / 67.24 / 55.06
17 / 71.57 / 75.81 / 76.04 / 69.13 / 73.99 / 73.61
18 / 86.75 / 81.25 / 87.02 / 81.56 / 39.86 / 90.36
mean / 71.81 / 76.22 / 78.62 / 76.93 / 74.81 / 74.81
SD / 16.89 / 10.68 / 12.85 / 14.21 / 13.24 / 12.78
p-value / 0.272 / 0.425 / 0.685 / 0.588 / 1.000
Table 8. Pulse oximetryanalysis result for blood oxygen concentration
SPO21 / 2 / 3 / 4 / 5 / 6
1 / 96.92 / 95.41 / 95.41 / 96.73 / 99 / 97.37
2 / 94.34 / 64.43 / 64.45 / 97.04 / 95.85 / 96.56
3 / 96.04 / 94.14 / 96.39 / 95.13 / 94.55 / 92.45
4 / 90.68 / 99 / 90.94 / 97.56 / 99 / 99
5 / 96.24 / 96.18 / 95.25 / 81.99 / 90.94 / 94.7
6 / 97.69 / 97.15 / 95.13 / 95.36 / 96.21 / 99
7 / 99 / 99 / 93.89 / 93 / 98.44 / 99
8 / 91.43 / 99 / 99 / 96.78 / 98.24 / 98.24
9 / 99 / 99 / 99 / 95.42 / 94.9 / 96.37
10 / 99 / 99 / 99 / 96.78 / 98.06 / 98.97
11 / 98.34 / 99 / 99 / 95.42 / 83.55 / 83.55
12 / 99 / 91.13 / 91.45 / 94.24 / 94.73 / 93.76
13 / 98.07 / 97.16 / 94.15 / 94.65 / 95.53 / 96.59
14 / 98.93 / 95.87 / 93.86 / 96.79 / 93.64 / 92.61
15 / 98.96 / 93.05 / 93.91 / 96.56 / 95.65 / 96.3
16 / 98.32 / 95.08 / 98.28 / 95.65 / 97.3 / 96.57
17 / 95.94 / 92.68 / 92.74 / 97.43 / 98.25 / 99
18 / 94.36 / 94.43 / 97.74 / 97.07 / 98.64 / 96.84
mean / 96.79 / 94.48 / 93.87 / 95.2 / 95.69 / 95.94
SD / 2.61 / 7.92 / 7.8 / 3.51 / 3.73 / 3.75
p-value / 0.232 / 0.355 / 0.533 / 0.611 / 0.506
4CONCLUSSION AND DISCUSION
The MSE result of both COP directions and the MMSEcombination method show that the tai chi exercise can improve human body balance even when just walkingfew simple steps. However, in this experiment no,improvement due to the pulse oximetry and EMG signals analysis is found.Furhermore more rigorous study can be conducted to investigate the relation between complexity of the COP data, physical ability and tai chi.In the future, this experiment can be extended to elderlies with diseases, and then construct an evaluation index for home care with the healthy or diseased elderlies.
ACKNOWLEDGEMENT
This research is supported by National Science Council in Taiwan through Grant NSC102-2221-E-155-028-MY3. This research is also supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan which is sponsored by National Science Council (NSC102-2911-I-008-001).
REFERENCES
[1] World Health Organization (2007),WHO Global Report on Falls Prevention in Older Age, World Health Organization.
[2] Ahmed MU, Mandic DP (2012), Multivariate Multiscale Entropy Analysis, IEEE Signal Processing Letters, Vol. 19, No. 2, 91-94.
[3] SezerO,FerdjallahM(2005), Adaptive Autoregressive Model for the Analysis of Center of Pressure in HealthySubjects during Quiet Standing, IEEE 48th Midwest Symposium on Circuits and Systems, Covington, KY, USA, Aug. 7-10, 2005, Vol. 1, pp. 495-498.