© IMEC 2010

EMOTIONS FROM PNS SYSTEM

LINDSAY BROWN, R&D ENGINEER

© IMEC 2010

MONITORING YOUR EMOTIONSMEASURING PERIPHERAL SIGNALS

© IMEC 2010

MONITORING ANS RESPONSES

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© IMEC 2010

AROUSAL MONITORINGReal-time arousal monitor

Test subject watching movie

Input signals: ECG, respiration, SkT & GSR

Arousal profile

Instantaneous arousal

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© IMEC 2010

AROUSAL MONITORINGUse-case: chess players

Can we gauge the arousal of a chess player?▸ Test game against

a computer▸ Player

commenting his game afterwards

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0 1 2 3 4 50

100

200

300

400

500

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Time (min)

Aro

usal

Approaching chessmate

Bad move!

Fluctuations during the game

© IMEC 2010

STRESS MONITORINGANS as a predictor for stress?

Monitoring physiological responses during stressful events shine light on possible physiological markers for stress

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0.7 0.75 0.8 0.85 0.9 0.95 1 1.05

0.5

1

1.5

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HRraw

HR

raw

, std

Red: During stressor Blue: At rest

Δ

Δ

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HRraw HRVHF

HR

VLF

nu, s

td

Red: Patients Blue: Controls

Comparing patients and healthy subjects may

provide new tools for assisting diagnosis of

stress disorders

© IMEC 2010

TRAPEZIUS EMG AS A PREDICTOR OF STRESS The proposed protocol successfully induces stress▸ Visual Analog Scores (stress) are

significantly higher for stress than rest periods (p = 6E-5)

EMG features significantly differ between stress and rest periods▸ RMS EMG values are significantly higher (p

= 0.0262)▸ The number of EMG gaps is significantly

lower (p = 0.0006)▸ Mean frequency is significantly lower (p =

0.0186)

BODY AREA NETWORKS FOR EMOTION MONITORING

© IMEC 2010

EMOTIONS FROM EEGtowards real-time valence monitoring

BODY AREA NETWORKS FOR EMOTION MONITORING

Raw EEG data F3/4 or

F7/8

Frequency spectra: alpha

power 2 second windows

Real-time Valence

Ratio of alpha power right/left

High ratio = positive emotionsLow ratio = negative emotions

© IMEC 2010 BODY AREA NETWORKS FOR EMOTION MONITORING

LONGITUDINAL RECORDINGS IN NATURAL ENVIRONMENTS

Motivation

▸ Measuring and managing stress requires monitoring of trends in bio-signals over large time period (weeks, months, years)

▸ Bring technology from lab to daily-life environments

Challenges

▸ Coping with motion artifact requires an integrated approach

▸ Achieve longer autonomy through the adoption of ultra-low power electronics

▸ Achieve wearability through new integration technologies

▸ Achieve robustness in detecting then compensating motion artifact

© IMEC 2010

IMEC ECG NECKLACEWireless, connected, ‘on-the-move’

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Wearable | with adjustable electrode leads

Robust | monitoring in every-day life situations

Low-power | 24/7 recording for 1 week Smart | instantaneous RR and HRV analysis

Imec inside: bio-potential ASIC & algorithms

© IMEC 2010

IMEC CONNECTS YOUR HEALTHManage your vitals from your mobile

BAN interface to Android Phone

24/7 connectivity to public network

Instantaneous alert ▸ Emails ▸ Text messages

BAN data available globally over the internet▸ Real-time

check▸ Link to EPR

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© IMEC 2010