**6.1 Methodology and data**

For this study, 25 patients (aged 45–87 years) and 25 controls (aged 46–88 years) were selected, and like in the gait analysis, PD patients were in an early stage of the disease. All participants with PD were under a dopaminergic agonist and were evaluated while in the "on" state. The absence of dementia and any other related to neurological conditions that affect gait was confirmed by an expert neurologist. All PD subjects were completely independent mobility and did not require a walking aid.

## **6.2 Noise reduction using wavelet**

Since the original signals had fluctuations that could affect the analysis and processing, it was necessary to apply wavelet techniques to remove alterations and clean the signal. As showed in **Figure 5**, we apply three-level wavelet decomposition using Daubechies wavelet with eight vanishing moments. From this step, the approximation coefficients at level 3 were used as clean signal.

As a result of the wavelet decomposition, we obtain a clean signal to determine the relative displacement of the wrist, which allows to observe conditions such as rigidity and asymmetry in upper limbs. For the next step, we use the *a*3 signal.

### **Figure 5.**

*Approximation coefficient and detail coefficient for wrist signal, the sum of these coefficient level generates original signals (s = a 3 + d 1 + d 2 + d 3).*

**13**

**Table 4.**

*Using Wavelets for Gait and Arm Swing Analysis DOI: http://dx.doi.org/10.5772/intechopen.84962*

try; these variables are defined as follows:

cycle, in the anterior/posterior plane

et al. [44], is the outcome of the next equation:

\_\_\_\_\_\_\_\_\_\_\_\_ *ArmSwingmore ArmSwingless* )] \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

<sup>90</sup>° *<sup>x</sup>* 100%

**Left wrist (n:50) p-Value** 

**Healthy subjects**

0.26 (IQR 0.17–0.33)

1.09 (IQR 0.94–1.15)

0.25 (IQR 0.18–0.29)

*Arm swing differences between PD patients and the healthy subject group.*

**(left wrist)**

ASA 0.16 (IQR 0.09–0.23) 0.063 (IQR 0.03–0.08) <0.001

**wrist) PD** 

0.002 0.16 (IQR

0.171 0.98 (IQR

0.002 0.14 (IQR

PD patients Healthy subjects p-Value

**PD patients**

0.09–0.24)

0.90–1.03)

0.09–0.21)

**Right wrist (n:50) p-Value** 

**Healthy subjects**

0.26 (IQR 0.20–0.34)

1.05 (IQR 0.96–1.12)

0.26 (IQR 0.18–0.31)

**(right** 

0.006

0.177

0.004

The arm swing variables calculated using the signal provided by eMotion were arm swing magnitude, arm swing time, arm swing speed, and arm swing asymme-

• Arm swing magnitude: the average distance traveled by the wrist in the anterior/

• Arm swing time: duration that took the displacement of a wrist, during a swing

• Arm swing speed: the ratio between the arm swing magnitude and the arm swing

• Arm swing asymmetry (ASA): proposed by Zifchock et al. and used by Lewek

**Table 4** shows a comparison of arm swing variables obtained for each limb with the eMotion. This shows that arm swing magnitude (left p = 0.002, right p = 0.006) and arm swing speed (left p = 0.002, right p = 0.004) were significantly reduced in the PD group for both limbs. The control group shows a lowest arm swing asymmetry than the patient group (p < 0.001). Based on the side, the variables that show significant differences for the left side were arm swing magnitude, speed, and ASA and for the right side were arm swing magnitude, speed, and ASA. Also, the most affected side determined with Kinect and the one with the highest score of the pondered items of the MDS-UPDRS-III were compared. These comparisons suggest that our device is to recognize the most affected side in the 80% of cases. Due to the limited sample size, differences in the symmetri-

posterior plane, normalized accord the hip center joint [10]

**6.3 Swing variables**

time

• *ASA* <sup>=</sup> [

**6.4 Results**

45°

cal group were not evaluated.

**patients**

0.16 (IQR 0.08–0.2)

0.99 (IQR 0.93–1.12)

0.16 (IQR 0.08–0.2)

**Arm swing variables**

Arm swing magnitude

Arm swing time

Arm swing speed

<sup>−</sup> *arct*(
