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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

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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 simpleResearch 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

FreezeNew speech

Anticipatory 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

CCLSMini 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

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HR(bpm)HR (bpm)

ECG (mV) ECG (mV) ECG (mV)

Mic data (mV) Mic data (mV) Mic data (mV)

120

HR (bpm)

EXPERIMENT 1Reading a book

For a listener at home

EXPERIMENT 2Before a public Presentation

EXPERIMENT 3During Presentation in

front of 6 people

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