**2. Related work**

#### **2.1. Chronic obstructive pulmonary disease**

COPD is one condition of COAD and we introduce some studies of COPD disease. The research of the World Health Organization (WHO) and the global initiative for chronic obstructive lung disease revealed that nowadays the COPD is the fourth cause of death in the United States. For the entire world, the COPD can be considered the fifth cause of death [4]. In another study, the COPD can be considered the fourth leading cause of death in the world [5] and is projected to be the fifth by 2020 as a worldwide burden of disease [6].

Generally speaking, chronic disease is gradually rising because of the ageing population and changing habits. The treatment for COPD patients is pulmonary rehabilitation programmes, including when patients are discharged at home [7]. Economic analyses have shown that over 70% of COPD-related health-care expenditures result from emergency room visits and hospital care for exacerbations; this translates to >\$10 billion annually in the Unites States [8]. COPD patients have a lower physical activity level than healthy peers [9]. The reduced level of physical activity also related to an increased risk [10]. Liao et al. [11] proposed a review that concentrated on describing wearable devices for measuring physical activity level in COPD patients. In [12], the authors evaluated a method for detecting an exacerbation onset in COPD patients. They used data collected through a pulse oximeter, permitting an easy way to the cohort of patients composed of elderly people affected by COPD. The study [13] provided a system, which offers an effective platform for the satisfaction of the clinical and the patient's needs in the area of early diagnosis of patient's health status. The portable system aims at the effective management in the health status of the patients who are suffering from COPD.

#### **2.2. Gait analysis**

People increase the respiratory ventilation when they are walking or exercising. However, those people who suffer from chronic obstructive airway disease (COAD) cannot increase their respiratory ventilation quick enough to maintain the exercising behaviour. Consequently, they change their behaviour such as walking slowly so that they can maintain their respiratory ventilation. We can perform gait analysis on COAD patients because their walking behaviours are different with normal people when they are exercising because of their respiratory function. Chronic obstructive pulmonary disease (COPD) is one condition of COAD. Nowadays, there are many chronic diseases in our daily life. COPD is one of them. COPD is a chronic airway disease characterized by progressive going downhill of the breathing functions [2]. One characteristic of COPD disease is the decreasing of forced expiratory volume in 1 s (FEV1) because of the obstructive airway [2]. Depending on their disease severity, they have different walking behaviours. Therefore, gait analysis can be used to judge the COAD patients' airway condition by observing their walking behaviour. However, it is difficult to collect the data of COAD patients without medical staff. Without clinical data, we cannot verify the correctness

By cooperating with Shuang-Ho Hospital in New Taipei, Taiwan, we set up an experiment. We film the side view of the subjects when they are performing a 6-min brisk walking test. By gait analysis, we can extract the features from walking behaviour such as pace distance and walking speed variation. However, in order to obtain that physiological information, we may need to wear sensors or markers on the subjects. Our method does not need to wear any sensors

In gait analysis, it is common to wear markers or sensors to record walking behaviour. In the experiment [1], the subjects need to wear a recorder on the ankle so that it could record the stride interval. In another experiment [3], the subjects also need to place a designed insole with

However, there are some drawbacks of using makers or sensors. Firstly, it is inconvenient and uncomfortable of attaching them on human body and might affect the normal walking. Secondly, some sensors are heavy or hard to use for the elderly. Thirdly, some sensors have the electromagnetic interference that might affect and harm human body. In addition to the sensor problem, it is hard to tell a subject suffering from COAD disease or not by a single experiment. Without a complete examination, it is hard to judge whether the subjects are COAD patients or not. Consequently, we decide to access the respiratory function. By the pulmonary spirometer, we can obtain the tested subjects' respiratory data. Nowadays, the parameters from pulmonary spirometer are the standard to access the respiratory function.

COPD is one condition of COAD and we introduce some studies of COPD disease. The research of the World Health Organization (WHO) and the global initiative for chronic obstructive lung

of our gait analysis algorithm.

2 Advanced Biosignal Processing and Diagnostic Methods

12 sensors into their own shoes.

on the subjects.

**2. Related work**

**2.1. Chronic obstructive pulmonary disease**

Gait analysis plays an important role in accessing human's walking behaviour and it aims to extract biomechanics information. The obvious disease on lower limbs is Parkinson's disorder. In [14], they provided a feasible method image marker to measure gait with little skin move‐ ment. Then, they performed quantitative analysis to extract gait parameter. According to their analysis, the joint angle, rotation angle of lower limbs, stride velocity and stride length have significant difference between Parkinson's disease patients and non-diseased subjects. The study [1] demonstrated that the gait variability in terms of statistical parameters of stride interval such as STC*γ*, would be increased in Parkinson's disorder. In addition to Parkinson's disease, Alzheimer's disease (AD) also can be detected by gait analysis. The study [15] presents an inertial-sensor-based wearable device and its associated stride detection algorithm to analyse gait information for patients with Alzheimer's disease.

The above methods all use markers or sensors to get interest points and then use gait analysis. With the advances in smart phones, Susu Jiang et al. [16] used a smart phone with an acceler‐ ometer and a gyroscope to collect human walking gait data in daily life. However, one smart phone only records one feature data. If we want to acquire many interest point data, testers need to tie many smart phones on the body, which might be inconvenient for walking.

Now, we proposed a gait analysis algorithm without any wearable marker or sensor. In addition, we also collect clinical COAD and control data to verify the correlation between pulmonary spirometer and our gait analysis method.
