**3.4.2 Aligning the acceleration data with the motion data**

372 Health Management – Different Approaches and Solutions

(a) The acceleration data before adjusting

(b) The acceleration data after adjusting

Data integration combines data from multiple sources to form a coherent data store. Metadata, correlation analysis, data conflict detection, and the resolution of semantic

The coordinate of acceleration data is different from that of motion data, so we should match the acceleration data and motion data in the same coordinates. We define the

Fig. 12. An example of adjusting result

**3.4.1 Converting the coordinates** 

Fig. 13. Converting the coordinates

heterogeneity contribute toward smooth data integration.

coordinates, as shown in Fig. 13, x: anterior, y: left, z: superior.

*z*

Motion Capturer

*y x*

*x*

Accelerometer

*y*

*<sup>x</sup> <sup>y</sup>*

Walking Model

*z*

*z*

**3.4 Data integration** 

The acceleration data and motion data come from different systems, the sampling rates are different, the time of starting measurement are not same, as shown in Fig. 14

Fig. 14. Title of figure, left justified

so we must find which point of motion data is correlative with which point in acceleration data. The aligning method has two steps.

1. Computing the acceleration with motion data by Equation (7), as shown in Fig. 15.

Fig. 15. Computing acceleration with motion data

Human Walking Analysis, Evaluation and Classification Based on Motion Capture System 375

Although there is now a wealth of literature pertaining to the maintenance of stability when standing, there is a relative paucity of information regarding the biomechanics and physiology of walking stability. Various models are currently in development, but a unified model of walking stability does not exist yet. Unlike standing, the balance of walking is a

Standard deviation is used to define the variability or randomness of the walking pattern. Less the amount of variability means better neuromuscular control and walking stability. We extract a set of hierarchical features from FL model, such as walking cycle features,

Left step length

Right toe out

(8)

Footprint analysis is a typical method of walking research as shown in Fig. 18.

Stride length

DEFINITION 2. (Footprint Stability) Footprint stability is described by the variability of the footprint stability features. We extract some of the stability features of footprint *FS (F),* such as the variability of cycle time f1, left step length f2, right step length f3, speed f4, walking

7

1 ( ) ( )/ ( ) *S ii i FF f f* 

where *δ(fi)* is the standard deviation of the feature *fi*, and *μ(fi)* is the mathematical

Now, the variability of the footprint features per decade of age is calculated, as shown in

The footprint variability in last row of Table 1 is sum of the above items. It can be seen that the variability is increasing with the age, see Fig. 19. That is to say, the footprint stability is declined with the age, and especially there is a dramatic increasing over 50 years old. But the twenties are exceptional, maybe because the twenties walk more springily than the

 

Walking base

base f5, left toe out f6, and right toe out f7, as shown in Equation (8).

Right step length

**4. Walking stability analysis** 

kind of dynamic balance (Menz, 2000).

Fig. 18. Features of footprint stability

**4.1.1 Definition of footprint stability** 

expectation of the feature *fi*.

Table 1.

elders.

**4.1.2 Effects of aging on footprint stability** 

footprint features.

**4.1 Footprint stability** 

$$\begin{cases} a\_i(\mathbf{x}) = \frac{\mathbf{x}\_{i+2} - 2\mathbf{x}\_{i+1} + \mathbf{x}\_i}{\Delta t^2} \\ a\_i(\mathbf{y}) = \frac{y\_{i+2} - 2y\_{i+1} + y\_i}{\Delta t^2} & (i = 1, N - 2) \\ a\_i(\mathbf{z}) = \frac{z\_{i+2} - 2z\_{i+1} + z\_i}{\Delta t^2} \end{cases} \tag{7}$$

2. Comparing the acceleration between the computing data and accelerometer data by minimum of relativity. The result of aligning is shown in Fig. 16.

Fig. 16. The result of aligning motion data and acceleration data

#### **3.5 Data reduction**

Data reduction techniques can be used to obtain a reduced representation of the data while minimizing the loss of information content. To obtain a reduced representation of the data set, such as speed, average span, frequency, and so on, data reduction techniques can be applied, for examples, aggregation operations and conception hierarchy generation. We propose a hierarchical structure shown in Fig. 17.

Fig. 17. The hierarchical structure of walking data
