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Drowsiness Detection System Using Heartbeat Rate in Android-based Handheld Devices

Drowsiness Detection System Using Heartbeat Rate in Android-based Handheld Devices. Advisor : Dr. Kai-Wei Ke Presenter : D. Jayasakthi Department of Electrical Engineering and Computer Science . Contents. Introduction Motivation Objective Methodology Results Conclusion. Introduction.

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Drowsiness Detection System Using Heartbeat Rate in Android-based Handheld Devices

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  1. Drowsiness Detection System Using Heartbeat Rate in Android-based Handheld Devices Advisor : Dr. Kai-Wei Ke Presenter : D. Jayasakthi Department of Electrical Engineering and Computer Science

  2. Contents • Introduction • Motivation • Objective • Methodology • Results • Conclusion 6/24/2013

  3. Introduction • Driver drowsiness is a major cause of traffic crashes. • Drowsy driving is a serious issue in our society not only because it affects those who are driving while drowsy, but because it puts all other road users in danger. • Therefore, the use of assisting systems that monitor a driver’s level of vigilance is important to prevent road accidents. • These systems should then alert the driver in the case of drowsiness or inattention 6/24/2013

  4. Motivation • A common activity in most people’s life is driving; therefore, making driving safe is an important issue in everyday life. • Even though the driver’s safety is improving in road and vehicle design, the total number of serious crashes is still increasing. • Most of these crashes result from impairments of the driver’s attention. 6/24/2013

  5. Motivation • Drowsiness detection can be done in various ways based on the results of different researchers. • The most accurate technique towards driver fatigue detection is dependent on physiological phenomena like brain waves, heart rate etc. • Also different techniques based on the behaviors can be used, which are natural and non-intrusive. • These techniques focus on observable visual behaviors from changes in a human’s facial features like eyes, head and face. 6/24/2013

  6. Objective • The aim of the thesis is develop a prototype for drowsiness detection system. • The application is developed using the android SDK and it will detect the heart beat signals from the i_Mami-HRM2 heart rate monitoring device. • ECG signal obtained from the sensor is analyzed in time domain and frequency domain. 6/24/2013

  7. Objective • In frequency domain, the power spectral density (PSD) is found. • From the PSD the Low Frequency(LF) to High Frequency(HF) ratio is estimated. • It is found that the LF/HF ratio decreases as the person becomes sleepy. • As a result the drowsiness of a person can be detected from this power ratio. 6/24/2013

  8. How it Works • Autonomic Nervous System (ANS) activity presents alterations during stress, extreme fatigue and drowsiness. • Wakefulness states are characterized by an increase of sympathetic activity and/or a decrease of parasympathetic activity. • Extreme relaxation states are characterized by an increase of parasympathetic activity and/or a decrease of sympathetic activity. 6/24/2013

  9. How it Works • The ANS activity can be measured non-invasively from the Heart Rate Variability (HRV) signal obtained from ECG. • Power on low frequency (LF) band (0.04-0.15Hz) is considered as a measure of sympathetic activity. • Power on high frequency (HF) band (0.15-0.4 Hz) is considered of parasympathetic origin in classical HRV analysis. • Balance between sympathetic and parasympathetic systems is measured by the LF/HF ratio. 6/24/2013

  10. Methodology • Various methods that has been implemented are: • Bluetooth module • ECG • Measuring Heart beat • Heart Rate Variability • Various Signal Processing Methods applied to the ECG signals are: • Decimation • Hamming Window • Fast Fourier Transform • Calculate the low to high frequency ratio 6/24/2013

  11. I-Mami HRM2 and Android Phone I-Mami HRM2 sensor from MicrotimeComputer Inc. Garmin Asus A50 6/24/2013

  12. Pairing the Sensor with the Mobile • First the device discovery is done in order to connect the sensor with the mobile. • If a device is discoverable, it will respond to the discovery request by sharing some information, such as the device name and its unique MAC address. • Once a connection is made with a remote device for the first time, a pairing request will be automatically presented to the user. • The user must enter a 4 digit pin number for the device to be paired. 6/24/2013

  13. Scan for Bluetooth Devices Sensor has been paired with the mobile Pairing Request 6/24/2013

  14. Bluetooth Module A Perform a lookup on the remote device in order to match the UUID No Yes UUID - Universally Unique Identifier  Initialize Bluetooth Socket A 6/24/2013

  15. 1. Main Screen with all modules 2.Bluetooth Module 3. List of paired device 4. Sensor Connected to the mobile 5. Device not connected 6/24/2013

  16. Display ECG signals • As a result of the electrical stimulation a change in potential of the order of 1mV can be measured during the cardiac cycle. • This signal is known as the electrocardiogram (ECG). • The ECG detector works mostly by detecting and amplifying the tiny electrical changes on the skin that are caused during each heartbeat. • The I-Mami HRM2 heart rate monitoring device is used to fetch the heart rate of a person and it is displayed in the android mobile with the help of programmable application, developed by using android SDK. 6/24/2013

  17. 1. ECG Module Main Screen 2. Select Sensor from menu 3. Displays the paired devices 4. Displays the ECG signals 6/24/2013

  18. Displaying the Heart Rate • The heart rate is the number of heart beats per minute. • Normal heart rate of a human being depends on the age. For example, children will have higher heart rates comparing with the adults. • This measurement can be done in various ways with respect to time. • 60 seconds (no calculation needed) - most accurate • 15 seconds (multiply by 4) • 10 seconds (multiply by 6) • Less than 10 seconds = less precise 6/24/2013

  19. 1.Heart Rate Measurement Module 2. Select a device from menu 3. Lists the paired device 4. Displays the heart rate and other values. 6/24/2013

  20. Heart Rate Variability • Heart rate variability (HRV), known as the variation of the period between consecutive heartbeats over time. • HRV refers to the variations in the beat intervals or correspondingly the instantaneous HR. • In time domain analysis, based on beat to beat or NN intervals some variables are analyzed. They are • SDNN: Standard Deviation of all normal to normal intervals index. Often calculated over a 24-hour period. • SDANN, the standard deviation of the average NN intervals calculated over short periods, usually 5 minutes. SDANN is therefore a measure of changes in heart rate due to cycles longer than 5 minutes. • NN50: Number of pairs of adjacent NN intervals differing by more than 50 ms in the entire recording • pNN50: The proportion of NN50 divided by total number of NNs. • AVNN: Average of all NN intervals. 6/24/2013

  21. 1. Heart Rate Variability 2. Select a device from menu 3. Select the sensor 4.Displays the HRV 6/24/2013

  22. Method to Detect Drowsiness Apply FFT Drowsiness Detection Calculate LF/HF ratio Obtain ECG signal from sensor Reduce the sampling rate to 50 Hz Is Ratio Decreasing No Person is not drowsy Yes Apply Hamming Window Person Becomes Drowsy 6/24/2013

  23. Decimation • Consider a band-limited discrete-time signal x(m) with a base-band spectrum X(f). • The sampling rate can be decreased by a factor of L through discarding of L–1 samples for every L samples of x(m). • Decimation by a factor of L can be achieved through a two-stage process of: (a) Low-pass filtering of the zero-inserted signal by a filter with a cutoff frequency of Fs/2L, where Fs is the sampling rate. (b) Discarding of L–1 samples for every L samples • The decimation factor is simply the ratio of the input rate to the output rate. It is usually symbolized by "M", so input rate / output rate=M. 6/24/2013

  24. Decimation • The sampling frequency of the sensor was 250 Hz which means 250 samples per second. • It was very high to process the ECG signals. • So the sampling frequency was reduced by 50 Hz which means 250/50 = 5 samples per second . • The decimation was done using a low pass filter technique. 6/24/2013

  25. Hamming Window Technique • Windowing functions, enhances the ability of an FFT to extract spectral data from signals. • Windowing functions act on raw data to reduce the effects of the leakage that occurs during an FFT of the data. • There are many window functions available. • For an ECG signal the appropriate window function is the Hamming Window. • The formula for Hamming window is w(n)=0.54−0.46cos(2πn/N−1). • If x(n) is the signal ,then we get the windowed signal by multiplying x(n) with the w(n) . 6/24/2013

  26. Fast Fourier Transform(FFT) • The FFT is a highly elegant and efficient algorithm, which is still one of the most used algorithms in speech processing, communications, frequency estimation, etc • Basic radix-2 algorithm is used which requires N to be a power of 2. • FFT is applied to the windowed ECG signal. • By applying FFT , the power spectrum was found . • LF/HF ratio is calculated every 1 minute . • If this ratio decreases then the person in becoming drowsy. 6/24/2013

  27. Results- While Awake 6/24/2013

  28. Results – While Asleep 6/24/2013

  29. Conclusion • A non-obstructive, real-time, continuous monitoring method for determining the drowsiness of the driver has been described . • From the results it is clear that the LF/HF ratio decreases when the person is sleeping. • Since ECG is one of the most easy to use physiological signals, a definite relation between drowsiness and HRV may lead to safer driving. • By applying FFT , the computational complexity is reduced. 6/24/2013

  30. Reference • S. Hu and R. Bowlds, "Pulse wave sensor for non-intrusive driver's drowsiness detection," in Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, Minneapolis, MN, 2009. • G. Furman, A. Baharav, C. Cahan and S. Akselrod, "Early detection of falling asleep at the wheel: A Heart Rate Variability approach," Computers in Cardiology, pp. 1109-1112, 2008. • S. Elsenbruch, M. Harnish, and W. C. Orr, “Heart rate variability during waking and sleep in healthy males and females,” Sleep, vol. 22, pp.1067-1071, 1999. 6/24/2013

  31. Thank You 6/24/2013

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