Human Technology Interaction

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 behavioural 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 (Artificial Intelligence). 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).

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 behaviour 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.

• By the end of this course, each student will have had the opportunity to:
• Understand the basic concepts of Human-Technology Interaction and remember the holistic picture to connect the dots between the concepts.
• Apply the above concepts to real-world problems outside those discussed in the class.
• Analyze the world around them to appreciate how technology has been embedded into their lives and how technological systems could be designed more effectively.
• Synthesize the knowledge from their hands-on experiences to gain confidence and willingness to tinker with and create technological prototypes that make us safer, productive, and creative. This would be the ultimate evaluation of the learning from the course.

By the end of this course, each student will have had the opportunity to:

• Engage in hands-on working with human physiological and behavioural data.
• Explore pattern recognition methods on the data for extracting “biomarkers” 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.

I know my dear students, how much you “like” reading books. Thus, here is a curated list of books that I hope you will “actually” read and find joy in.

• The Design of Everyday Things by Don Norman (ISBN: 978-0465050659)
• Universal Principles of Design by William Lidwell, Kritina Holden, and Jill Butler (ISBN: 978- 1592535873)
• Affective Computing by Rosalind W. Picard (ISBN: 978-0262661157)
• How Emotions Are Made: The Secret Life of the Brain by Lisa Feldman Barrett (ISBN: 978- 0544133310)

• Interaction Design: Beyond Human-Computer Interaction by Yvonne Rogers, Helen Sharp, and Jennifer Preece, Sixth Edition (ISBN: 978-1119901099)

  Additional resources and readings will be updated timely on the course website.

• Quiz 1 (week 5): 10%
• Project (midterm evaluation i.e., week 8): 10%
• Quiz 2 (week 9): 10%
• Quiz 3 (week 15): 10%
• Project (final evaluation i.e., week 16): 25%
• Lab Evaluation: 30%
• Attendance: 5%

The use of Generative AI platforms like ChatGPT, Google Bard, GitHub 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.

Each class will consist of about 35-40 minutes of lecture followed by demos inspired by real-world applications for the next 10-15 minutes. These demos will be shown by the instructor so that the students can visualize the practical applications of the concepts taught in the class.

Each lab will be used for two purposes. First, the students will be given a programming assignment illustrating an HTI concept and may have open-ended questions to answer. The output of this assignment and students’ understanding of the related concept will be graded and will contribute towards the grading assessment component (see below) of the lab. Most lab assignments will have deadlines of about 5-6 days since they may require installation of some programming libraries and getting familiar with them. Second, the lab will also serve as a time when the students can discuss the progress (queries/questions/received feedback) on their semester project for the course with the instructor.

This class will involve a project which will serve several purposes. First, it will give you the opportunity to explore in-depth the multidimensional (pun intended) facets of the HTI course. Second, it will support the development of your critical thinking and hands-on application skills; in my opinion, this is one of the primary goals of university education.

The students will form groups of two or three (Remember: One is a maverick, two is a pair, three is a team, and four is a crowd) to undertake a project. The students are expected to work with the instructor in the first three weeks to identify a HTI-related problem for each group, the hardware and/or software resources that would be needed, and the methodologies that may be required to work on it.

 Below are the project evaluation criteria:

• Problem Statement

The problem that students are trying to solve must be related to a problem that they empathize with in their lives. For e.g., proposing a machine learning-inspired better design approach for your laptop to increase productivity, designing an emotional assistant system for your grandparents, building a smart wearable to help the security personnel at Plaksha to track their mental health, etc.

• Background and Significance

Students will be asked to do a thorough background survey to see what related solutions are already available for the identified problem, their merits and limitations, and what are the possible strategies to overcome those limitations. The students will then choose exactly what contribution they want to make to the identified problem and what would be its significance.

• Data Collection

Students are expected to collect data themselves for the project rather than using an existing dataset unless the kind of data collection they are planning to pursue is not possible at Plaksha. In the latter case, students will be evaluated on their understanding of the niches of the dataset being used by them.

• Development of the Solution

The students will be expected to utilize statistics and machine learning to program a solution and then validate its efficiency (empirically, please provide numbers) against other solutions presently used by others.

• Understanding of fundamental concepts

Students’ understanding of the fundamental HTI concepts utilized in solving the problem and why the choices to use those models/techniques were made.

• Deployability of the Solution

The efficacy of the developed solution and its deployability at Plaksha University. Students are expected to show the feasibility.

Optional: The students will also be required to submit a video (maximum duration 2 minutes) during the final evaluation explaining the various components of the project.

Students must also submit their software code for the entire project to check for plagiarism. Any violation of the institute’s policy on plagiarism will make the students ineligible to pass the course.

Participation is not mere attendance in the class! 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 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. Students are expected to maintain at least 70% attendance in both lectures and labs, failure to do so would make them score zero towards the attendance grade (5% of the course grade).

An “Incomplete” grade will be awarded in case a student does not complete any assessment or evaluation exercise because 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, Monday 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).