The document proposes developing Android applications to sense emotions using smartphones for better health and human-machine interactions. It discusses detecting emotions through passive sensors like cameras, microphones, and accelerometers that can capture facial expressions, speech, heart rate without interpreting input. Recognition involves extracting meaningful patterns from sensor data using techniques like speech recognition, facial expression detection to produce labels or inference algorithms. Specific techniques are discussed for recognizing emotions from speech, facial expressions based on the Facial Action Coding System, and heart rate variability. The conclusion states that understanding emotions with smartphones can help people succeed and make research easier.

1. EMOTION RECOGNITION USING SMARTPHONES
-Madhusudhan (17)

2. OBJECTIVE
• To propose the development of android applications
that can be used for sensing the emotions of people
for their better health.
• To provide better services and also better Human-machine
interactions

3. INTRODUCTION
• Emotions control your thinking, behavior and
actions.
• Emotions affect your physical bodies as much as
your body affects your feelings and thinking.
• People who ignore, dismiss or repress their
emotions, are setting themselves up for physical
illness.

5. DETECTION
• Detection of emotional info can be done with
passive sensors which capture data about the user's
physical state or behavior without interpreting the
input.
• A video camera might capture facial expressions,
body posture and gestures.
• A microphone can capture speech.

6. • A pressure sensor/accelerometer can capture heart
rate.
• Other sensors detect emotional cues from skin
temperature.

7. RECOGNIZING
• Extraction of information for meaningful patterns
from the gathered data
• Speech recognition, natural language processing,
or facial expression detection
• These techniques should either produce labels or
efficient inference algorithms to extract high-level
information from the data.

8. SPEECH
• Requires the creation of a reliable database
or knowledge base as well as the selection of a
successful classifier which will allow for quick and
accurate emotion identification.
• Currently, the most frequently used classifiers are
linear discriminant classifiers (LDC), k-nearest
neighbour (k-NN), Gaussian mixture model (GMM),
support vector machines (SVM), artificial neural
networks (ANN), decision tree algorithms and
hidden Markov models (HMMs).

9. FACIAL EXPRESSIONS
• Defining expressions in terms of muscle actions A
system has been conceived in order to formally
categorize the physical expression of emotions.
• By studying the contraction or a relaxation of one or
more muscles.
• The concept of the Facial Action Coding System, or
FACS created by Paul Ekman and Wallace V.
Friesen in 1978
• Eg. Affdex

10. • By identifying different
facial cues, scientists are
able to map them to their
corresponding Action Unit
code
• They have proposed the
following classification of
the six basic emotions,
according to their Action
Units
Emotion Action Units
Happiness 6+12
Sadness 1+4+15
Surprise 1+2+5B+26
Fear
1+2+4+5+20+2
6
Anger 4+5+7+23
Disgust 9+15+16
Contempt R12A+R14A

11. HEART BEAT
• One of the most commonly used techniques is by
using pressure sensors/accelerometer.
• The heart rate can also be collected using the Optical
pulse sensor Eg. Samsung galaxy S5.
• Heart Rate Variability (HRV) signals is derived from
ECG signals through QRS detection algorithm.

12. • It is used as a statistical feature to distinguish the
emotional stress, through a nonlinear classifier (K
Nearest Neighbor (KNN))into three different classes
namely, negative emotions, positive emotions
(surprise and happy) and neutral.

13. CONCLUSION
• To understand the emotions with the help of
smartphones will help in achieving greater success
in the lives of people.
• Using smartphones to do this will make it much
more easier for researchers and scientists.

14. REFERENCES
• http://www.mkprojects.com/fa_emotions.html by By Mary
Kurus
• Emotion Recognition from Speech by Ankur Sapra, Nikhil
Panwar, Sohan Panwar -Jaypee Institute of Information
Technology, Noida
• http://en.wikipedia.org/wiki/Affective_computing#Emotional_s
peech
• Mobile Sensor Data Collector using Android Smartphone by
Won-Jae Yi, Weidi Jia, and Jafar Saniie - Department of
Electrical and Computer Engineering, Illinois Institute of
Technology
• EmotionSense: A Mobile Phones based Adaptive Platform for
Experimental Social Psychology Research

15. THANK YOU