This course is dedicated to providing engineering students with a comprehensive understanding of how humans
interact with technology. The course borrows its name from “Human Computer Interaction,” but, technology is not
just limited to traditional computers anymore. In fact, it is embedded in our lives through wearables,
smartphones, advanced driver assistance systems, social media, etc. Thus, the course takes a multi-modal approach
involving the use of bio-sensors, computer vision, and electro-mechanical sensors to detect and model changes
in human physiological and behavioral responses (such as but not limited to neural activity, facial expressions,
heart-rate variability, pupillometry, and galvanic skin response), human ergonomics, and human cognition as humans
interact with technology around us.
Students will explore critical topics such as human factors, ergonomics, cognition, affective computing,
and human-centered AI. The course will explore practical applications including wearables, autonomous vehicles,
and robotics, emphasizing design principles and engineering solutions. Through case studies and real-world examples,
students will learn to design technologies that enhance usability, safety, and user experience. Additionally, the
course will address the ethical and technical challenges in developing human-centric technologies, providing strategies
for innovative solutions. The course will be closely aligned with the requirements of Industry by studying strategies
to enhance safety, productivity, and creativity for individuals in their environments (spanning the full spectrum from
blue-collar workers to information workers).
Course Overview
This course is divided into three modules. The first module of the course (Week 1-4) narrates the fundamental concepts around
HTI such as Human Factors and Ergonomics, HCI, Design Thinking, UX, etc. An understanding of these fundamental concepts is needed
to understand the parameters that should be accounted for while designing HTI interfaces. The second module of the course
(Week 5-11) revolves around Affective Computing. This module dives into the fundamental theory of emotions and affects,
data collection through various kinds of sensors, sentiment analysis, multimodal data fusion, and generation & expressions of
human emotions. It is critical to understand from the user’s subjective perspective about how they are “actually feeling” when
interacting with technology. This module will include working with text, speech, facial expressions, gestures, and physiological
data to interpret human emotions and affects. Finally, the last module (Week 12-15) will weave the first two modules together to
first discuss the role social media plays in human lives and shaping our online behavior and collective intelligence. It will
then discuss the ethical considerations as we develop Human-AI interactive tools which are also changing the way we work and our
workplaces. Thus, we will study safety, productivity, and creativity in industrial settings as well as for information workers
using tools studied in the first two modules.
Learning Outcomes
By the end of this course, each student will have had the opportunity to:
Engage in hands-on working with human physiological and behavioral data.
Explore pattern recognition methods on the data for extracting “bio-markers” and applying signal processing and machine learning models on them.
Demonstrate the ability to find real-world problems where they could use the above methods to build robust solutions.
Apply the above statistical techniques to build real-world applications.
Evaluate the efficacy of the developed solutions to make them more robust and scalable.
Create a prototype that utilizes the concepts from the course to solve a real-world problem.
Articulate the characteristics and efficiency of their prototype as to how it works better than existing solutions.
The use of Generative AI platforms like ChatGPT, Claude, Copilot, etc. is permitted and encouraged.
The instructor has accepted that these tools have become a fact of life in engineering education. Thus, students can
use them in any way they would like to, but the instructor reserves the right to accordingly tune the assignments and exams.
Participation is not mere attendance in the class! In order to effectively participate in the course, it is critical
that each member of the team read the course assignments and participate in class discussions and simulations and in
group work. The participation grade will be based on your participation both in the class as a whole and in small
groups. This grade is a “value added” assessment; in other words, sheer frequency or volume of verbal activity is not
necessarily the goal of class participation. The grade is derived from meaningful dialogue based on reading and
thinking reflectively.
To participate in class more fully, you might consider, for example, commenting on specific issues raised in the class
readings; illustrating specific issues from the readings with examples from your personal experience; raising questions
not covered in the readings; comparing or contrasting ideas of various theorists from the readings; or supporting or
debating the insight or conclusions of a classmate (or the instructor!) by referencing concepts, experiences or logical
reasoning.
Part of participation also includes setting the tone of collegiality, whether that is through contributing to a snack table,
engaging in conversation with classmates during breaks, or making fellow students feel welcome. Participation is not
merely an intellectual exercise; it is also a community building experience.
Regular attendance is expected in this course in order to achieve maximum learning for all participants. Unforeseen
circumstances do sometimes arise, so periodic absences may occur. If you find that you must miss or be late to a class meeting,
please contact the instructor’s teaching fellow prior to the start of class. At least 75% attendance is mandatory to receive a full attendance
grade (10% of the course grade), and at least 70% attendance is mandatory for a half attendance grade (5% of the course grade). Less than 70% attendance would lead to no attendance grade.
An “Incomplete” grade will be awarded in case a student does not complete any assessment or evaluation exercise as a result of which
they do not meet the passing criterion. This is only for medical/social emergencies beyond the control of students or cases of
pending disciplinary investigation and must be approved by the Dean, Academic Affairs.
Situations involving academic integrity are governed by the UG academic policy. Here are the specifics: the instructor shall report
case to the Academic Integrity Committee, which, after taking into due consideration the nature of the evaluation component and the
intensity of the offence, as well as the number of times the student has committed prior offenses, will prescribe the appropriate
corrective action.
My goal is to be as available as possible to meet your needs during the semester. To reach me:
E-mail me at siddharth.s@plaksha.edu.in; this is the best way to contact me. I check e-mail frequently and,
unless I am out of town, I will usually respond to your e-mail within 24 hours.
In Person: Although I will try to make myself available to you if you “drop by”, please do not expect a
substantive conversation; I may have other commitments. However, I will be available every week during
office hours, Friday 2-3 PM, Office No. A2-411.
To make a phone or in-person appointment, please contact my teaching fellow - Mr. Pushpinder Singh
(pushpinder.singh@plaksha.edu.in).
