1. Experimental Results ■ Observations:  Overall detection accuracy increases as the length of observation window increases.  An observation window of 100 seconds is sufficient to achieve a reasonable detection accuracy.  Similar detection accuracy is achieved when the smartphone is placed at different positions.  Our detection accuracy is within 0.3 bpm even if under noisy environment. System Implementation ■ Performing noise reduction to reduce the effects of background noise. Noise Detection: Estimate the noise components.  Segmenting acoustic signal into frames.  Using Bandpass filter to remove both high and low frequency sounds.  Detecting frames that only contain ambient noise by calculating the variance. Noise Subtraction: Subtract noise components from acoustic signal.  Estimating the noise magnitude spectrum from noise frames.  Subtracting it from the spectrum of the recorded acoustic data.  Obtaining the cleaned acoustic signal after taking the Inverse Fourier transform. Hearing Your Breathing: Fine-grained Sleep Monitoring Using Smartphones Yanzhi Ren 1, Chen Wang 1, Yingying Chen 1, Jie Yang 2 1 Department of Electrical and Computer Engineering Stevens Institute of Technology 2 Department of Computer Science Florida State University Motivation ■ Enabling the fine-grained sleep monitoring (e.g., breath rate detection) with minimal cost to facilitate healthcare related applications. ■ Prior low cost sleep monitoring only performs coarse- grained monitoring, such as the events detection. ■ Traditional fine-grained sleep monitoring systems involve high cost and wearable sensors -- limited to clinical usage. Contribution ■ Exploiting smartphone earphone to capture the breathing sound for fine-grained sleep monitoring. ■ Achieving continuous and noninvasive breathing rate monitoring without involving additional diagnostic devices. ■ The proposed breathing rate detection method is adaptive to different users. ■ Our approach can detect various sleep events easily. ■ Case study of fine-grained sleep monitoring supported healthcare application: sleep apnea monitoring. System Overview ■ Using smartphone’s earphone to capture breath sound. ■ Removing noise via noise reduction from acoustic signal. ■ Distinguishing the event sound from the breath sound. ■ Identifying the breath rate by performing the signal envelop detection from the breath sound. ■ Detecting sleep events (e.g., body movement, cough and snore) from the event sound. ■ Deriving the breathing rate from the envelope of the acoustic signal. Envelope Detection: Extracting the envelope e(l) to capture trend changes of the acoustic signal.  Computing the maximum absolute value of the acoustic samples in each frames.  Performing the interpolation to make the length of each sequence consistent. Breathing Rate Identification: Utilizing the correlation inherent in the user’s breath cycles to identify the breath rate.  Defining a function f(t) to measure the similarity between acoustic samples as a function of the time lag t between them.  Searching for a set of local minimums from f(t) by varying t.  The first local minimum therefore corresponds to the period of breathing. Noise Reduction Experimental Setup ■ Two iPhone 4 with their original earphones. ■ 6 volunteers over a period of 6 months. ■ Placing earphones in two different positions:  The participant wears the earphone.  The participant puts the earphone besides the pillow. ■ Conducting experiments under two different environments:  The quiet environment.  The noisy environment with the air conditioning on. Conclusion ■ Our system can perform continuous and noninvasive fine-grained sleep monitoring by using the smartphone and its earphone. ■ Our noise reduction scheme can reduce the impact of background noise while preserving the features present in the breathing sound. ■ Exploiting the correlation relationship inherent in a user’s breathing cycles to identify breathing rate accurately based on the signal envelope detection. Breathing Rate Detection