Detecting the breath and classifying breathing movements such as inhale and exhale has settled importance in manybiomedical research areas. To this end, monitoring the breathing movements for lung cancer patients tends to remain one of the breath detection applications which have received much attention. On the other hand, virtual reality (VR) revolution has a lot of implications in many fields, which could also be used as a simulation technology for healing purposes. This has been an indication to use VR to assist the lung cancer patients. In this work, a novel method is proposed to detect and classify breathing movements. In our technique, we employ Mel-Frequency Cepstral Coefficients (MFCCs) to the acoustic signal of respiration captured using a microphone to depict the differences between the inhale and the exhale in frequency domain. MFCC features are widely used in depicting the different acoustic and physical traits of voices. For each subject, the acoustic signal of breath is captured and then split into inhale and exhale durations. We have applied 13- MFCCs for each inhale and exhale timeframe, and plotted the i-th MFCC for all subjects individually. We classify the Detecting the breath and classifying breathing movements such as inhale and exhale has settled importance in many biomedical research areas. To this end, monitoring the breathing movements for lung cancer patients tends to remain one of the breath detection applications which have received much attention. On the other hand, virtual reality (VR) revolution has a lot of implications in many fields, which could also be used as a simulation technology for healing purposes. This has been an indication to use VR to assist the lung cancer patients.In this work, a novel method is proposed to detect and classify breathing movements. In our technique, we employ Mel-Frequency Cepstral Coefficients (MFCCs) to the acoustic signal of respiration captured using a microphone to depict the differences between the inhale and the exhale in frequency domain. MFCC features are widely used in depicting the different acoustic and physical traits of voices.For each subject, the acoustic signal of breath is captured and then split into inhale and exhale durations. We have applied 13-MFCCs for each inhale and exhale timeframe, and plotted the i-th MFCC for all subjects individually.