Date post: | 21-Dec-2015 |
Category: |
Documents |
View: | 220 times |
Download: | 0 times |
© IMEC 2010
EMOTIONS FROM PNS SYSTEM
LINDSAY BROWN, R&D ENGINEER
© IMEC 2010
MONITORING YOUR EMOTIONSMEASURING PERIPHERAL SIGNALS
© IMEC 2010
MONITORING ANS RESPONSES
RESTRICTED
© IMEC 2010
AROUSAL MONITORINGReal-time arousal monitor
Test subject watching movie
Input signals: ECG, respiration, SkT & GSR
Arousal profile
Instantaneous arousal
RESTRICTED
© 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
RESTRICTED
0 1 2 3 4 50
100
200
300
400
500
600
Time (min)
Aro
us
al
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
RESTRICTED
0.7 0.75 0.8 0.85 0.9 0.95 1 1.05
0.5
1
1.5
2
2.5
3
HRraw
HR
raw
, std
Red: During stressor Blue: At rest
Δ
Δ
6070
8090
100110
0.10.2
0.30.4
0.50.6
0.02
0.04
0.06
0.08
0.1
HRraw
HRV
HF
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 emotions
Low 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’
RESTRICTED
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
RESTRICTED
© IMEC 2010