1. Emotional Intelligence Vivian Tseng, Matt Palmer, Jonathan Fouk Group #41

2. Introduction ▪Some people find it harder to communicate their emotions with others ▪Provide an interface for them to identify their emotions and communicate it clearly with others

3. Overview ▪Wearable Device on Wrist o Similar to watch ▪Software o Speech Processing and Classification ▪Hardware o Sensors o Digital Signal Processor (DSP) o Display

4. High Level Block Diagram

5. Block Diagram: Data Collection and Processing

6. Block Diagram: User Interface

7. Block Diagram: Power System

8. ▪Requirements o 3.3V ± 0.2V o 1.7V ± 0.1V o 0.1Vpp Voltage Ripple at 100 mA o 500 mA Charge Current o 100 mA maximum current o Charge LiPo Battery Power

9. Microphone and Audio Amplifier ▪Requirements o 30 dB Gain o Output Impedance of 400 Ohm o Filter transition band from 8k to 10k of -30 dB o Less than 1 ms of Time Delay for all Passband frequencies o Output Offset of 1.65V

10. Simulation Results Frequency Sweep

11. Simulation Results Outputs

12. Power, Microphone, and IO Implementation

13. Sensors ▪Temperature Sensing o Thermistor o Input to Analog-to-Digital Converter (ADC) pin of microcontroller (MCU) o Calculate temperature on MCU ▪Heart Rate Monitor o Analog Front End Chip o Use Green LED and Photodiode to collect data o Communicate to Microcontroller through Serial Peripheral Interface (SPI) o Calculate beats per minute on MCU

14. User Interface ▪Two Display methods decided by the user o LCD screen display  Connected to the Microcontroller and display the determined emotion o LED lights display option  Different LED colored lights showing your emotion ▪USB Charging

15. Feature Extraction Mel-Frequency Cepstral Coefficients ▪take Discrete Fourier Transform of signal ▪create Mel filterbanks ▪take the log of filterbank energies ▪take Discrete Cosine Transform of log filterbank energies

16. Feature Extraction

17. Pitch ▪Autocorrelation

18. Classification Support Vector Machines (SVM) ▪Finds maximum distance plane between 2 or more classes Multi-class Problem ▪More than two classes of emotions ▪One vs One strategy

19. Classification Cross correlation method ▪Split dataset into v subsets of equal size ▪test each subset against trained group of other subsets ▪For each distinct emotion, accuracy ~41% Classify by valence and arousal ▪valence = positivity of feelings ▪arousal = excitability of feelings ▪only voted for one class Problem: Unequal training sets After equalizing training set ▪valence accuracy ~ 63% ▪arousal accuray ~ 75%

20. Digital Signal Processor ▪Pulls in speech data from microphone o Analog-to-Digital Converter (ADC) o Feature Extraction ▪Sends data through Serial Peripheral Interface (SPI) to MCU o MCU performs classification on speech and biosensor data

21. Successes ▪Determine correct emotion from speech with higher than 65% confidence rate ▪Display module working o LEDs and LCD ▪Temperature Sensor (ADC to MCU) ▪Amplifying circuit for microphone ▪Power charger and converter

22. Future Steps ▪Fix SPI communication between modules ▪Replace Microcontroller Launchpad with surface mount microcontroller ▪Add feature extraction to DSP ▪Extensive testing with different users ▪Re-design IOBoard and Controller Board o Design Wearable Package ▪Implement with Sensitive Microphone o Adjust OP-AMP gain

23. Conclusion ▪Learning o Speech signal processing o Circuit design o Power system design ▪Reflections o Use less surface mount for prototyping o Have access to speech databases o Expanded soldering and design skills

24. Thank you! Questions?