High intensity - Applied Digital Sensor Technology in the Analysis of Different Intensity Movem

maximum power jump of the Pre-test. The height of the Low intensity jump was calculated from two figures. The first height figure was the point where participants’

hands could reach up with arms completely vertically stretched. The second height figure was the highest point participants could reach with both hands after a High intensity jump. The first height figure was deducted from the second and the 30% of this height was defined as the Low intensity jump height. Participants had to reach this height for completing a Low intensity jump. The height was controlled with a ball hanging down from the ceiling of the lab. Participants were requested to perform each intensity jump three times.

Data Processing

The METLAB R2007b software (The MathWorks, MA, USA) was used to process the big data recorded during the tests. The 10 Hz low-pass filter was applied to reduce the noise. This four order Butterworth filtering process is similar to the method applied by Shih et al. (2014). The 10 Hz low-pass filtering was applied on the IMU to the gyro and accelerometer data. All noise was filtered except for the movement. The vibration of the IMU, especially on the intelligent shoe caused high frequency noise, which also had to be filtered. The force plate data was filtered by the 30 Hz low-pass four order Butterworth filtering technology. Five consecutive gait cycles of Run and Walk movements were analyzed. In order to gain reliable data three gait cycles would have been sufficient (Winter, 1984). Each phase was defined between the heel strikes of the left foot. The beginning and the end were marked by the peak points of deceleration on the anteroposterior axis due to the heel strikes. These two deceleration peaks determined one complete gait cycle. Other studies also defined the gait phase by measuring the acceleration data of the anteroposterior axis (Lee 2010, Zijlstra 2003).

The intensity of the Walk and Run locomotion was defined by the range of acceleration displacement in between the two peak points. The lowest figures of acceleration data were also collected between the heel strike and toe off in the stance

phase. The mean value of acceleration data was calculated. These figures enabled the assessment of intensity. The range of acceleration change in three axes, and the peak angular velocity, from all different movement was calculated. The patterns of angular velocity were observed in order to find out the differences between the movements.

The range of acceleration on all three axes, on three planes, and the resultant acceleration in each combination of the two axes (XY=horizontal plane, XZ=sagittal plane, and YZ=coronal plane, and all axes XYZ) were calculated. The range of acceleration was calculated using 3 axes and the Pythagorean theorem. Furthermore the gyroscope of the IMU recorded the maximum and minimum angular velocity in each axis and the graphic characteristics of the velocity curve variation were applied to benchmark data in order to find out the distinct characteristics of different intensity locomotion.

Statistical Method

The three-way repeated measures ANOVA was applied as the statistical method to determine the difference among movements, intensities and the locations of sensors.

The significant level was set at α=.05. There were 3 movements, 2 intensities, and 3 sensor positions. Therefore the three-way ANOVA (3 movements × 2 intensities × 3 sensors) with repeated measures on test variables was the statistical method to analyze all data. At significant main effects, Bonferroni post hoc tests were applied to identify the statistically significant mean differences. The level of significance was set at α =.

05. When the interactions among movement, intensity and sensor positions were all significant, then the movement and intensity or positions were separately measured and only two factors were compared with each other.

CHAPTER III.

Results

In the present study the Three-way ANOVA (3 movements × 2 intensities × 3 sensors) with repeated measures on all test variables was applied. The statistical and mathematical approach demonstrated significant results. There was a significant interaction effect among the movement, intensity, and the sensor positions. Most of the results of the different locomotion varied at low and high intensity levels and by IMU locations as well. After the three-way ANOVA a simple effects test was performed.

The interaction between only the intensities and the movements provided non-significant results. The waist IMU measured the least non-significant interaction among different movements and intensities. Both the shoe and the waist IMU regarding the acceleration captured non-significant interaction between the intensities and movements. There was no significant interaction on the waist IMU’s superior–inferior axis (A-z G). Furthermore the results on the A-yz (F=2.68, p=0.11) and the G-x (F=0.008, p=0.99) axis picked up by the wrist IMU; on the A-y (F=1.5, p=0.24), A-z (F=0.95, p=0.38), A-yz (F=2.99, p=0.09), A-xyz (F=5.02, p=0.14), G-z (F=0.43, p=0.66) axis picked up by the waist IMU; on the A-y (F=2.06, p=0.17), A-xz(F=1.48, p=0.25), A-xyz (F=1.62, p=0.22) axis picked up by the shoe IMU trended to show no significant interaction effects between the intensity and movement (Table1.).

Table 1. Results of interaction effects measured by the 3 IMUs Wrist

Low intensity High intensity

Walk Run Jump Walk Run Jump F P

A-x(G) 0.65±0.12^*bc 1.81±0.60^*ac 8.38±3.43^*ab 1.16±0.16^bc 2.67±0.43^ac 16.01±2.29^ab 73.062 <.001^# A-y(G) 0.41±0.14^*bc 1.02±0.41^ac 3.99±0.95^*ab 0.90±0.37^c 1.19±0.37^c 5.94±1.47^ab 167.74 <.001^# A-z(G) 0.36±0.16^*bc 3.14±0.80^*ac 5.03±0.99^*ab 1.33±0.36^bc 4.39±0.79^ac 7.46±1.82^ab 8.843 0.005^# A-xy(G) 0.70±0.11^*bc 1.52±0.60^*ac 7.65±2.90^*ab 1.13±0.16^bc 1.96±0.35^ac 15.23±1.99^ab 84.929 <.001^# A-xz(G) 0.66±0.12^*bc 3.03±0.68^*ac 8.04±2.95^*ab 1.26±0.19^bc 3.91±0.70^ac 15.68±2.26^ab 89.727 <.001^#

A-yz(G) 0.46±0.16 2.96±0.67 4.62±0.87 1.13±0.30 3.75±0.70 6.05±1.67 2.676 0.114

A-xyz(G) 0.71±0.10^*bc 3.11±0.71^*ac 8.39±2.73^*ab 1.28±0.21^bc 3.97±0.71^ac 15.96±2.03^ab 93.602 <.001^# G-x(d/s) 188.07±57.85 246.98±45.94 847.20±176.24 269.87±74.46 327.70±133.91 934.16±239.40 0.008 0.992 G-y(d/s) 237.31±53.78^*bc 361.46±101.22^*ac 1277.13±305.59^*ab 524.03±175.13^c 450.19±134.44^c 1630.89±277.49^ab 9.161 .001^# G-z(d/s) 112.26±55.58^*bc 180.61±70.86^*ac 718.67±199.40^*ab 176.92±86.90^bc 272.24±48.21^ac 942.73±181.42^ab 8.714 .008^# Waist

Low intensity High intensity

Walk Run Jump Walk Run Jump F P

A-x(G) 0.54±0.07^*c 0.48±0.09^*c 1.40±0.21^*ab 1.19±0.18^bc 0.70±0.11^ac 3.04±1.28^bc 14.904 <.001^#

A-y(G) 0.49±0.09 0.62±0.15 0.83±0.34 0.89±0.20 0.87±0.19 1.18±0.31 1.50 0.24

G-x(d/s) 53.46±17.35^* 66.54±20.36^* 56.31±18.83^* 105.49±16.22^bc 83.87±23.68^a 85.93±18.93^a 13.886 <.001^# G-y(d/s) 43.33±8.78^*bc 62.89±17.20^*ac 206.00±32.85^*ab 70.95±16.82^bc 87.20±24.27^ac 347.26±57.67^ab 33.891 <.001^# G-z(d/s) 72.81±19.35 87.70±21.22 85.02±24.67 136.92±41.70 155.09±38.61 136.95±47.57 0.429 0.655 Foot

Low intensity High intensity

Walk Run Jump Walk Run Jump F P

A-x(G) 4.57±0.76^* 4.83±0.75^* 4.56±0.81^* 8.03±0.82^b 6.39±0.88^ac 8.57±1.08^b 22.80 <.001^#

A-y(G) 1.57±0.39 1.57±0.51 2.24±0.78 2.45±0.44 2.20±0.42 3.48±1.10 2.056 0.169

A-z(G) 2.45±0.35^*c 2.43±0.47^*c 5.33±1.04^ab 3.67±0.38^bc 2.76±0.68^ac 5.60±1.05^ab 5.763 0.008^#

A-xy(G) 3.78±0.63^* 4.08±0.54^*c 3.12±0.58^*b 6.16±0.45^b 5.60±0.68^a 5.63±0.77 6.712 0.004^#

A-xz(G) 4.33±0.76 4.67±0.63 4.36±0.53 6.34±0.54 6.14±0.86 5.91±0.72 1.478 0.245

A-yz(G) 2.44±0.32^*c 2.49±0.48^*c 4.27±0.60^ab 3.67±0.34^bc 3.03±0.61^ac 4.60±0.78^ab 5.237 0.024^#

A-xyz(G) 4.50±0.74 4.81±0.64 4.47±0.64 6.42±0.50 6.11±0.77 6.05±0.69 1.617 0.217

G-x(d/s) 320.39±74.75^* 385.29±58.56^* 356.01±100.63^* 435.86±92.25^bc 561.74±71.37^a 670.34±219.46^a 8.093 0.006^# G-y(d/s) 765.96±57.48^*c 680.19±82.30^*c 868.55±135.48^ab 1105.53±66.99^bc 808.54±53.80^ac 885.16±68.00^ab 41.425 <.001^# G-z(d/s) 237.75±54.14^* 213.89±47.51^* 251.66±100.29^* 280.16±42.35^c 271.92±88.63^c 465.75±175.75^ab 17.169 <.001^#

# significant interaction effect; *significant difference between intensity levels

a denotes the significant differences in walking; ^b denotes significant differences in running; and ^c denotes significant differences in jumping

During the statistical analysis the main effects of intensity and movement showed significant difference. The main effects analysis of the acceleration of the anteroposterior direction provided significant differences among the three movements (F=59.08, p < .01). At Walk, Run, and Jump movements the same trends of acceleration change could be observed at different intensities. As the movement intensity increased the values of all parameters increased. Run and Walk patterns consistently exhibited the similar increasing trend that was picked up by the shoe IMU. The angular velocity captured by the shoe IMU in the anteroposterior X axis, and the mediolateral Y axis produced significant data. However at the Jump movement the angular velocity on the vertical Z axis did not produce significant results (F=0.64, p=0.44). At the same time the angular velocity of both the Walk and Run movements grew significantly by the increase of intensity, which produced significant results on the vertical Z axis.

When the different intensity movements were compared by acceleration significant differences trended to appear in almost all axes. However there was no significant displacement on the vertical axis either at Low Intensity Jump or at High Intensity Jump. This may have been caused due to the plantar-flexion movement during takeoff and landing. The peak angular velocity captured by the shoe IMU similarly to the acceleration results at both the High Intensity Jump and the Low Intensity Jump did not result in significant displacement either at plantar-flexion or dorsiflexion.

The post hoc tests on the angular velocity of the plantar and dorsiflexion in the frontal axis (Y axis) resulted in consistent differences among Walk, Run, and Jump movements that could identify different intensities. However the trends of movement modes varied (Fig.7.).

FIGURE 7 The result of acceleration (left) and angular velocity (right) in X and Y axis (shoe) respectively

* significant difference among different movements.

As the intensity increased, the acceleration values of all parameters proportionally increased, especially at Run movement. These results were caught up by the shoe IMU. The values at Run were greater than at Walk or Jump. Both the multi-axial and the uniaxial parameters were able to identify the different intensities of movements. However the multi-axial parameters were not as effective as the uniaxial parameters in determining the intensity of locomotion. Cut points to separate the different intensities of movements were set up based on velocities captured by the shoe IMU. The most significant increase at both the acceleration on the X axis, and the angular velocity on the Y axis were observed at the Run movement.

The gait cycle was determined by acceleration data. The time between the heel strikes of the left foot defined the epoch or the shortest observation period of each body movement. All calculations were done with the comparison of these cycles. Each peak on the anteroposterior axis was caused by the heel contact. Patterns of angular velocity could also identify one cycle on the Y axis. The first peak appeared at the plantar-flexion between the heel contact and the toe contact. The second peak could be observed from the mid-stance phase to toeoff marked with the line in the middle. In between these two peaks a flat curve trended to show up. The value was very close to zero. The combined results of accelerometer and gyro clearly identified gait cycles of walking (Fig. 8.).

FIGURE 8 Definition of one gait cycle at Slow walk from the shoe IMU

In the Walk movement patterns the acceleration of anteroposterior axis and the angular velocity of frontal axis were more representative than others due to the relatively high values and the regular changing of walk patterns. A regular sequence was measured in the occurrence of plantar and dorsiflexion (Fig. 9.).

FIGURE 9 Data of Slow walk from six axes (gyro on the left, accelerometer on the right) Heel contact

Heel contact

Stance phase Swing phase

In the comparison of different intensity walk the peak values of the Fast walk were significantly higher than the Slow walk. Significant differences were observed between the peak to peak intervals. The intervals of Fast walk were significantly shorter than the intervals of Slow walk measured by the shoe IMU (Fig. 10.).

FIGURE 10 Comparison of Slow walk and Fast walk intervals

* Significantly shorter intervals

In the comparison of Slow Run and the Fast Run movement results showed a similar trend as between the Slow and the Fast walk. However the peak values were significantly higher and the intervals were shorter at Run movement measured by the shoe IMU (Fig. 11.).

Slow Walk Fast Walk

*

FIGURE 11 Comparison of the Slow run and the Fast run

* Significantly shorter interval

At Slow walk the acceleration data of the anteroposterior axis and the gyroscope data of the foot rotation on Y-axis exhibited a regular sequence. The two patterns identified the same heel contact time at the low intensity Slow walk caught up by the shoe IMU (Fig.12.).

FIGURE 12 Shoe IMU data at the Slow walk

Slow Run Fast Run

*

Similarly to the phenomenon at Slow walk the accelerometer on the anteroposterior axis and the Gyroscope on the Y-axis of the shoe IMU at Slow run trended to have an agreement. Both clearly showed the heel strike at the same time demonstrating it with the highest range of displacement between the high peak and the low peak as shown in Fig 13.

FIGURE 13 Shoe IMU data at Slow run

At Fast walk the acceleration on the anteroposterior axis and the gyroscope data on the superior-inferior axis trended to show a regular sequence of movement cycles (Fig.14.).

FIGURE 14 Waist IMU data at Fast walk

The waist IMU at Fast Run did not trend to deliver consistent sequence of gait cycles and the displacement between the positive and negative peaks did not show significant results either. At Fast run movement neither the accelerometer nor the gyroscope discovered matching patterns (Fig. 15.).

FIGURE 15 The waist IMU patterns at Fast run

The IMU fixed on the wrist at Slow run provided a clear pattern, which demonstrates the consistent waving movement of participants arm. This phenomenon was captured on the Y axis of the gyroscope and on the X axis and the Z-axis of the accelerometer (Fig.16).

FIGURE 16 The wrist IMU patterns at Slow run

The shoe IMU at Jump with low and high intensities trended to capture the pattern shown in Fig.17. The positive peak was always followed by a negative peak representing the moment of the takeoff and the landing respectively.The peak value of the angular velocity could not clearly distinguish the Low intensity jump from the High intensity jump. However the value of acceleration and the intervals between the positive and negative peaks clearly identified both the Low and the High intensity jump. The intervals were significantly longer at High jump than at Low jump.

FIGURE 17 Comparison of the Low (left) and High (right) intensity jump

* Significantly longer intervals

The positive peak value demonstrates the toe-off at Jump movement. The negative peak value represents the dorsiflexion recovery phase from toe-off to heel contact before landing. Jumping results in Fig. 18. show that the Gyroscope of the IMU on the wrist measured a negative peak first, which demonstrates the backward swing of the arm as a preparation for the High intensity jump. The wind-up movement was trended to be followed by a significant positive peak on Y-axis. It was the moment when participants’ wrist swung forward supporting the takeoff. The higher was the positive peak the higher participants trended to jump.

*

FIGURE 18 Wrist and shoe (foot) gyro data of the High intensity jump

A Similar phenomenon was observed at the Low intensity jump. The Gyroscope of the IMU on the wrist measured a small negative peak first, which demonstrated the backward swing of the arm as a preparation for the jump. The wind-up movement was trended to be followed by a significant positive peak on Y-axis. It was the moment when participants’ wrist swung forward supporting the takeoff. The higher was the positive peak the higher participants trended to jump. At the same time the gyroscope of the shoe IMU trended to show a higher value on the mediolateral axis, which indicates that the plantar flexion of the foot was more significant (Fig.19). It may also suggest that muscles of the lower limbs were more involved in this movement.

FIGURE 19 Wrist and shoe (foot) gyro data of the Low intensity jump

The vertical acceleration of the shoe and waist sensors demonstrated a similar trend at the High intensity jump. The most representative peaks occurred at takeoff and landing, because there were two major downward forces at push off and landing (Fig.

20.). These peaks can be observed parallel to each other at the foot and the waist sensors. However the waist sensor captured both negative peaks slightly earlier compared to the shoe sensor. The shoe IMU of the Jump movement measured two representative peaks one positive and one negative, between the two there was a relatively flat curve. The positive curve shows the plantar movement at Jump while taking off from the ground, the negative curve shows the dorsiflexion movement at landing on the ground, and the flat curve represents the time spent in the air. The curve variation could be used to describe the features and the sequence of Walk, Run, and Jump movements. Furthermore characteristics of graphs could identify different body movements, while intensity levels were defined by measurement values from the accelerometer or the gyroscope.

FIGURE 20 Comparison of the IMU acceleration patterns of the shoe (up) and waist (down)

The patterns of angular velocity at Slow walk/run and at Fast walk/run, and at Low/High intensity jump were consistent. As the intensity increased parameters followed the increase. At the Fast walk/run shorter intervals were picked up compared to the Slow walk/run. The angular velocity at different intensity levels trended to show similar patterns at Slow and Fast run, and at Low and High intensity jump captured by the shoe based IMU (Fig. 21.). The positive peak values represent the plantar flexion movements of each gait cycle at Walk and Run movements. The first peak and the second peak values represent the heel strike to foot flat with toe contact and the heel-off to toe-heel-off respectively at Walk and Run.

FIGURE 21 Low (left) and High (right) intensity Walk, Run, and Jump

The patterns produced by the angular velocity on the Y axis at different movements and intensities were crucial markers of the shoe IMU. Each locomotion curve exhibited a regular pattern in the frontal axis (Y axis). The plantar and dorsiflexion movement at Walk, Run, and Jump movements trended to show a regular

sequence of occurrence. Thus the frontal Y axis could be a proper tool of distinguishing different movements.

CHAPTER IV.

Discussion

In spite of the significant interaction among intensity levels, different types of movements, and the IMU positions the correlation between movements and intensities revealed non-significant results. After the three-way ANOVA the simple effects test was performed and non-significant interaction results were found between the movements and intensities. The acceleration measured by the shoe IMU was one of them. The acceleration value range of Slow/Fast walk was higher than the one at the Slow/Fast run movements. The Fast walk speed was set at 2m/s, which is approximately the PTS. It was observed that participants trended to accelerate their limbs including the lower extremities and the arms significantly higher at the Fast walk than at the 2m/s run. The extra acceleration work exerted by participants allows the assumption of the relatively higher energy expenditure (EE). The study indicated that the energy expenditure, (EE) was higher when running below PTS than walking, and that the EE was higher when walking above PTS than running. This statement is consistent with findings of the previous study of Hreljac et al. (2002). It means that the threshold of the acceleration value of Fast walk and Fast run movements overlap. Thus simply applying the acceleration value as a criterion may result in error when differentiating between movements, it may also distort EE estimation. The shoe IMU acceleration data was crucial because it provided useful information on the intensity and movement. Unlike the Shoe IMU, the wrist IMU did not provide applicable results for the identification of the different movement types. The movements of the wrist were not in correlation with the intensities. However the waist IMU provided critical information. There was a non-significant interaction between the different intensities and the movements. The most non-significant interaction was measured at the waist IMU. Therefore figures from the waist IMU exhibited consistent patterns. Thus this information from the waist IMU could be used for indirect calorimetry and EE assessment.

Among the 3 IMU positions the shoe trended to supply the most data that could be used to identify different movements. Therefore a special identification criterion had to be set up in order to identify movements and to provide a systematic and clear picture on the results. The acceleration data and the angular velocity were applied to identify the different movements. The criterion to identify movements involved the

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