**Author details**

correlation and regression slope by using (*Vp*

26 Advanced Biosignal Processing and Diagnostic Methods

**Figure 26.** (a) Predicting

Predicting FEV1 with (*Vp*

**7. Conclusion**

(*Vp*

FEV1 FVC with (*Vp*

(*Δ*Amp, *Δβ*ratio); (i) Predicting FVC with (*Vp*

´ , *<sup>γ</sup><sup>V</sup>* , *<sup>Δ</sup>*Amp, *Δβ*ratio); (d) Predicting FEV1 with (*Vp*

´ , *<sup>γ</sup><sup>V</sup>* ); (b)Predicting

sensor-based gait analysis method. With the extracted features, *Vp*

´ , *<sup>γ</sup><sup>V</sup>* , *<sup>Δ</sup>*Amp, *Δβ*ratio); (g) Predicting FVC with (*Vp*

FEV1

´ , *<sup>γ</sup><sup>V</sup>* , *<sup>Δ</sup>*Amp, *Δβ*ratio).

We propose a vision sensor-based gait analysis method without wearing any sensor on human body. In our approach, the proposed gait features analyse the subjects' respiratory function. We also perform a clinical experiment on COAD patients and normal people with our vision

FVC with (*Δ*Amp, *Δβ*ratio); (c) Predicting

´ , *<sup>γ</sup><sup>V</sup>* ); (e) Predicting FEV1 with (*Δ*Amp, *Δβ*ratio); (f)

FEV1 FVC with

´ , *<sup>γ</sup><sup>V</sup>* ); (h) Predicting FVC with

´ and *<sup>γ</sup><sup>V</sup>* , the classification

radar system cannot improve the results of predicting FEV1 and FVC.

between the combined system and the pulmonary spirometer.

Radar system improves our analysis results on both SVM classification and predicting the parameter *FEV1/FVC*. With radar system's help, there is a higher correlation and accuracy

´ , *<sup>γ</sup><sup>V</sup>* , *<sup>Δ</sup>*Amp, *Δβ*ratio). Therefore, the features of

Yu Sheng Chan1 , Wen Te Liu2 and Ching Te Chiu1\*

\*Address all correspondence to: ctchiu@cs.nthu.edu.tw

1 Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan, ROC

2 School of Respiratory Therapy, Taipei Medical University, Taipei, Taiwan, ROC
