Computational Analysis of Human Behavior(INF 538)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| INF 538 | Computational Analysis of Human Behavior | 1 | 3 | 0 | 0 | 3 | 6 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Elective |
| Course Level | Masters Degree |
| Course Instructor(s) | Pınar ULUER puluer@gsu.edu.tr (Email) |
| Assistant | |
| Objective | The course focuses on machine learning and pattern recognition techniques commonly used in human behavior analysis. The primary objective of the course is to introduce students to recent research and diverse applications in this field and to help them apply their theoretical knowledge to the problems and challenges encountered in human behavior analysis using modern methods and multimodal approaches. |
| Content | This course examines machine learning and pattern recognition techniques used in the computational analysis of human behavior. It introduces the most commonly used techniques and algorithms in this field and presents examples of real-world applications. These applications include gait and posture analysis, hand gesture recognition in sign language, activity recognition in image sequences, tracking social signals, multimodal behavioral analysis (based on visual, auditory, and physiological signals), and the study of social interactions. |
| Course Learning Outcomes |
1. The knowledge about existing methods for analyzing human behavior and the ability to choose which method to use depending on the goals and conditions of the presented problem. 2. The experience with how unimodal and multimodal data are processed in behavior analysis. 3. The ability to model a system for behavior analysis, and interpret the results using different problem-specific performance metrics. |
| Teaching and Learning Methods | Theory and practice, presentation, discussion, question-answer |
| References |
Salah, A. A., & Gevers, T. (Eds.). (2011). Computer analysis of human behavior. London: Springer. Uddin, M. Z. (2024). Machine Learning and Python for Human Behavior, Emotion, and Health Status Analysis. CRC Press. Yu, Z., & Wang, Z. (2020). Human behavior analysis: sensing and understanding (pp. 1-271). Singapore: Springer. Paramasivan, P., Rajest, S. S., Chinnusamy, K., Regin, R., Joseph, J., & Joe, F. (Eds.). (2024). Explainable AI applications for human behavior analysis. IGI Global. |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Capturing and interpreting human behavior using computational methods |
| 2 | Sensor-based behavior recognition |
| 3 | Device-free behavior recognition |
| 4 | Activity recognition : Gait and posture analysis |
| 5 | Activity recognition : Sign language recongition |
| 6 | Social and affective behaviors : Speech and voice analysis |
| 7 | Social and affective behaviors : Multimodal interaction in rehabilitation |
| 8 | Social and affective behaviors : Emotion recognition in social interaction |
| 9 | Midterm |
| 10 | Adaptive and personalized systems |
| 11 | Example : Activity monitoring systems in health care applications |
| 12 | Example : Human behavior analysis in ambient gaming and playful interaction |
| 13 | Challenges and open issues |
| 14 | Student presentations |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 2 | 60 |
| Contribution of final exam to overall grade | 1 | 40 |
| Toplam | 3 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 30 |
| Project | 1 | 30 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Make-up | 0 | 0 |
| Toplam | 2 | 60 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 14 | 3 | 42 |
| Working Hours out of Class | 0 | 0 | 0 |
| Assignments | 0 | 0 | 0 |
| Presentation | 0 | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 10 | 10 |
| Project | 1 | 15 | 15 |
| Laboratory | 0 | 0 | 0 |
| Other Applications | 0 | 0 | 0 |
| Final Examinations (including preparation) | 1 | 15 | 15 |
| Quiz | 0 | 0 | 0 |
| Term Paper/ Project | 0 | 0 | 0 |
| Portfolio Study | 0 | 0 | 0 |
| Reports | 0 | 0 | 0 |
| Learning Diary | 0 | 0 | 0 |
| Thesis/ Project | 0 | 0 | 0 |
| Seminar | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Make-up | 0 | 0 | 0 |
| Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Bütünleme | 0 | 0 | 0 |
| Total Workload | 82 | ||
| Total Workload / 25 | 3.28 | ||
| Credits ECTS | 3 | ||