1. www.postersession.com Methods Towards Anti-Stuttering – Understand the Relation between Stuttering and Anxiety Using Emerging Engineering Methods Sarah Shamsi (srhshms@gmail.com) MS Student in Embedded Electrical & Computer Systems, School of Engineering Advisor: Dr. Xiaorong Zhang (xrzhang@sfsu.edu) Assistant Professor, School of Engineering Bibliography Background Order online at https://www.postersession.com/order/ Preliminary Results ECG EMG ACTUATOR ECG EMG Analyze ECG & EMG signal to detect anxiety and stuttering moment Alert Signal SYNC Microphone & ECG Stuttering LA-RA LL-RA Mic mV Samples ECG mV 150 70 Stutters: 1% of the World’s Population Stuttering - a speech disorder, causes still not clear Role of Stress, Anxiety & Nervousness unknown. Autonomic Nervous System(ANS) controls stress & anxiety by activating & deactivating sympathetic & parasympathetic nervous system Limitation in current research: 1) Experiments only performed in controllable environments; 2) Existing data analysis methods are relatively simple Research Goals Apply advanced engineering technologies to the stuttering research Use emerging wearable technologies and advanced signal processing methods to address the limitations above Understand the relations between stuttering, emotion, and physiological changes Propose new anti-stuttering assistive method Methods ECG Detects changes in SNS & PNS Heart Rate (BPM), Mean R- R interval, RMSSD, SDNN, pNN50, LF, HF, LF/HF parameters can be used. Brain Freeze New speech Anticip atory Fear Stuttering Sensors ECG EMG GSR ALERT Preliminary Results Prior Stuttering During Stuttering Normal Abnormal Abnormal ECG pattern in stuttering gives us opportunity to determine several other parameters in ECG apart from Heart Rate to determine the changes in the ANS. Future Work  Collect additional data from more subjects and using more sensors such as EMG, GSR  Find unique data features that distinguishes stuttering and non-stuttering and monitors stress level  The ECG, EMG & GSR patterns will be utilized in anti-stuttering assistive device design as the key signs to identify stuttering-related anxiety in real-time.  Integrated with other audio/tactile feedback or biofeedback techniques, the ultimate goal of the assistive device is to eliminate patients’ negative emotional reactions, reduce severity of stuttering, and thus improve the quality of life of people who stutter. References CCLS Mini Grant 2015 Funded By: Peters HF, Hulstijn W: Stuttering and anxiety: The difference between stutterers and nonstutterers in verbal apprehension and physiologic arousal during the anticipation of speech and non- speech tasks. Journal of Fluency Disorders 1984, 9:67-84. Alm PA: Stuttering, emotions, and heart rate during anticipatory anxiety: a critical review. Journal of Fluency Disorders 2004, 29:123- 133.  Use wearable devices to collect practical data in the subjects’ daily life.  Collect preliminary data from one stuttering subject and three non-stuttering subjects  Develop advanced signal processing, information fusion, and pattern recognition methods to analyze data No. of samples HR(bpm) ECG (mV) Mic data (mV) 120 HR (bpm) EXPERIMENT 1 Reading a book For a listener at home EXPERIMENT 2 Before a public Presentation EXPERIMENT 3 During Presentation in front of 6 people 90 120