**3. Walking data preprocessing**

Data preprocessing is an important issue for data analysis, as real-world data tend to be incomplete, noisy, and inconsistent. Data preprocessing includes data cleaning, data integration, data transformation, and data reduction. Although numerous methods of data preprocessing have been developed, data preprocessing remains an active area of research, due to the huge amount inconsistent or dirty data and the complexity of the problem. Before we talk about the methods of walking data preprocessing, let us have a look at walking data measuring.

#### **3.1 Walking data measuring**

There are many measurements of human gait, such as basic data (spatial and temporal data), kinematics (displacement, velocity and acceleration data), kinetics (force and moment data), electromyography (electrical activity of lower limb muscles), and image and graphics (individual silhouette images, monocular, image sequence, video). We adopt the kinematical approach in modeling the human movements.

Two kinds of data, motion data and acceleration data are measured using two different systems. The Motion data are gotten from motion capture system (Vicon MX System by OMG Plc), while acceleration data are obtained by a 3-axis accelerometer.

The providers walk along 5m straight line in level plane 3 times at them natural normal walking speeds. 30 markers are attached to the body, as shown in Fig. 3. Fig. 4 shows a snapshot of data acquisition using a motion capture system in the University of Aizu, Fukushima, Japan. The highlights show markers on human body.

10 11

14 15

*x*

*y* 

*x*

1

2

7

13

21

24 25

16

17 18

22 23

26

9

3 4 5

12

19

27

8

6

20

11: LWRB, left outside wrist (little finger side)

12: RSHO, right shoulder

13: RELB, right elbow


18: CASI, center front waist


30: RTOE, right toe

*y* 28 30 Fig. 3. Markers attached to the body

366 Health Management – Different Approaches and Solutions

Frontal plane

*φ*y

*Y*

Transverse plane

For walking symmetry, only consider the bilateral of positions, segments and joints on both sides of human body. In FL-Model, there are 8 points, 5 segments and 5 joint angles unilateral met this condition. These attributes are all three-dimensional data. They are motion data and its velocity and acceleration of {Knee, Ankle, Heel, Toe, Shoulder, Elbow, Wrist, Hip}, Segment Angle and its velocity and acceleration of {Thigh, Shank, Foot, Upper-arm, Forearm}, Joint Angle and its velocity and acceleration of {Hip, Knee, Ankle, Shoulder, Elbow}. These

Data preprocessing is an important issue for data analysis, as real-world data tend to be incomplete, noisy, and inconsistent. Data preprocessing includes data cleaning, data integration, data transformation, and data reduction. Although numerous methods of data preprocessing have been developed, data preprocessing remains an active area of research, due to the huge amount inconsistent or dirty data and the complexity of the problem. Before we talk about the methods of walking data preprocessing, let us have a look at walking data

There are many measurements of human gait, such as basic data (spatial and temporal data), kinematics (displacement, velocity and acceleration data), kinetics (force and moment data), electromyography (electrical activity of lower limb muscles), and image and graphics (individual silhouette images, monocular, image sequence, video). We adopt the kinematical

Two kinds of data, motion data and acceleration data are measured using two different systems. The Motion data are gotten from motion capture system (Vicon MX System by

The providers walk along 5m straight line in level plane 3 times at them natural normal walking speeds. 30 markers are attached to the body, as shown in Fig. 3. Fig. 4 shows a snapshot of data acquisition using a motion capture system in the University of Aizu,

OMG Plc), while acceleration data are obtained by a 3-axis accelerometer.

Fukushima, Japan. The highlights show markers on human body.

*φ*x

attributes can also be expressed with p1~10, p13~18, φ3~6, φ9~14, θ3~12, as shown in Fig. 1.

*o*

*X*

Fig. 2. Segment angle

measuring.

**2.3 Definitions of walking symmetry** 

**3. Walking data preprocessing** 

**3.1 Walking data measuring** 

approach in modeling the human movements.

Sagittal plane

*φ*z

*Z*

The motion capture system can detect the three dimensions displacement data at tree directions: anterior-posterior, left-right, and superior-inferior. The sampling rate of motion data is 120Hz. At the same time, a tri-axial accelerometer unit is mounted with CASI, the same point as marker 18, see Fig. 3. The accelerometer is connected with a mobile phone to save the acceleration data. After that the data can be transferred to computer. Acceleration data are also collected in three dimensions as same as motion data, including the gravity acceleration. But the directions are not same. The sampling rate of acceleration data is 90Hz. To calibrate the accelerometer, before each testing session, it was placed with each of the orthogonal axes vertically, to estimate the ±1g values.

*z* 

*z* 

By the way, a movie is taken by a video camera while he/she is walking. 44 normal persons from 20 to 69 year old are measured. These subjects are classified into 5 groups (20+, 30+, 40+, 50+, and 60+) by the age.

Fig. 4. A snapshot of data acquisition using a motion capture system

Human Walking Analysis, Evaluation and Classification Based on Motion Capture System 369

And there are some noisy data in motion data caused by vibration. Since the walking signal resides in the low frequency range, it is easily affected by interference from other signal and noise sources. Butterworth low-pass filter is used to reduce noise by passing signal which frequency below twice walking cadence. Some examples are shown in Fig. 7

Fig. 7. Velocity data of left toe (No filtering & Filtering)

Fig. 8. Acceleration data of left toe (No filtering & Filtering )

must be processed by the mean methods, see Equation (4).

Because of the faults of the measure systems, one marker can be identified as two or more. In the source file, there is more than one column to store them. So these inconsistent data

**3.2.3 Inconsistent data** 

to Fig. 8.
