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Updated: Fri 22 June 2012

Spring 2012 Semester Projects

The HCI group had four projects allocated to Master students for the spring 2012 semester.

If you are interested to know more or work on related ideas, don't hesitate and come and talk to us (Dr. Pearl Pu - BC 107, George Popescu - BC 144, Yu Chen and Ronh Hu - BC 145).

Emotional Kineticons in Online Social Environment

kineticons

Date: 22nd of June 2012

Duration: One semester (February - June 2012)

Student name: Alfredo Cerezo Luna

Responsible TA: Yu Chen

Goal: Visualize and express emotions in online social environments.

Solution: Embed emotion in Facebook profile pictures.

Description: : We started by defining a library of 9 kineticons according to music related emotions, such as: transcendence, joyful, wonder, tenderness, nostalgia, pacefulness, energy, sadness and tension. We then implemented the kineticons in GroupFun, a group music recommender system that suggests a common playlist for users. Our evaluation was based on a live user study. Aiming at proving that our kineticons are well understood we showed to 15 users our design together with 3 words description of emotion and asked them to vote for the words that best describe each of the 9 kineticons. Additionally, we prepared a video of 9 emotions, each corresponding to a song. All 15 users watched the video and rated (from 1 to 5) the appropriateness of how the kineticons describe the emotions evoked by the song.


Wellness Sensing Using Wearable SensorsWellness

Date: 22nd of June 2012

Duration: One semester (February - June 2012)

Student name: Javier Martin De Valmaseda

Responsible TA: Dr. Zerrin Yumak

Goal: Develop a stable platform for mobile sensor data collection, labeling and post visualization method.

Solution: Android mobile application for activity labelling and accelerometer data collection.

Description: Activity recognition of every day activities can be used for many different purposes: preventive medicine, promotion of health-enhancing physical activities and a developing a healthier lifestyle. It has a high industrial interest and a high impact on society. In the same time wearable sensors are becoming less and less intrusive and more accurate. As well, mobile devices are becoming increasingly sophisticated and smartphones with accelerometer and other sensors are widely popular nowadays. In our study data have been collected by 8 users while performing normal daily activities along 2-3 days. They used two different types of sensors: BodyMedia and Affectiva together with an Android phone. We relied on the SAX method to process the data. Afterwards, it can be analyzed by machine learning algorithms to detect certain activity types. SAX is the first method for the symbolic representation of time series that allows dimensionality reduction and indexing with a lower-bounding distancemeasure. This symbolic approach allows a time series of length n to be reduced to a string length w (w < n).