Academic Research
Human Robot Interaction
As a Master of Science student at NC State, I was also a Research Assistant working on a Human Robot Interaction project. The abstract is as follows:
The objective of this study was to understand the effect of different features of service robots on elderly people’s perceptions and emotional responses in a simulated medicine delivery task. Twenty four participants sat in a simulated patient room and a service robot entered and delivered a bag of “medicine” to them. Repeated trials were used to present variations on three robot features, including facial configuration, voice messaging and interactivity. Participant ratings of robot humanness (perceived anthropomorphism (PA)) were collected using a subjective rating scale along with emotional responses on arousal (excited – bored) and valence (happy – unhappy) using the self-assessment manikin (SAM) questionnaire after each trial. Results indicated the three types of robot features all promoted higher PA, arousal and valence, compared to a control condition (a robot configuration without any of the features). It was also found that the three types of features had different utility for stimulating participant emotional responses in terms of arousal and valence. In general, the results indicated that adding anthropomorphic features (face and voice) to service robots not only lead to higher perceptions of humanness, but also promote more positive emotional responses in elderly users. It is expected that results from this study could be used as a basis for developing affective robot interface design guidelines to promote user emotional experiences.
The research on HRI can be seen in the following publications:
1. Zhang, T., Kaber, D. B., Zhu, B., Swangnetr, M., Mosaly, P., Hodge, L. (Accepted). Service Robot Feature Design Effects on User Perceptions and Emotional Responses. Submitted to Intelligent Service Robotics.
2. Zhang, T., Zhu, B., Lee, L., Swangnetr, M., Mosaly, P., & Kaber, D. B. (2009). Service Robot Feature and Interface Design Effects on User Emotional Responses. In Proc. of the 17th Triennial Congress of the International Ergonomics Association (IEA), Beijing, China.
3. Zhang, T., Zhu, B., Lee, L., & Kaber, D. B. (2008). Service Robot Anthropomorphism and Interface Design for Emotion in Human-Robot Interaction. In Proc. of the 4th IEEE Conference on Automation Science and Engineering. Washington D.C., Aug., 23-26, 2008.
The objective of this study was to understand the effect of different features of service robots on elderly people’s perceptions and emotional responses in a simulated medicine delivery task. Twenty four participants sat in a simulated patient room and a service robot entered and delivered a bag of “medicine” to them. Repeated trials were used to present variations on three robot features, including facial configuration, voice messaging and interactivity. Participant ratings of robot humanness (perceived anthropomorphism (PA)) were collected using a subjective rating scale along with emotional responses on arousal (excited – bored) and valence (happy – unhappy) using the self-assessment manikin (SAM) questionnaire after each trial. Results indicated the three types of robot features all promoted higher PA, arousal and valence, compared to a control condition (a robot configuration without any of the features). It was also found that the three types of features had different utility for stimulating participant emotional responses in terms of arousal and valence. In general, the results indicated that adding anthropomorphic features (face and voice) to service robots not only lead to higher perceptions of humanness, but also promote more positive emotional responses in elderly users. It is expected that results from this study could be used as a basis for developing affective robot interface design guidelines to promote user emotional experiences.
The research on HRI can be seen in the following publications:
1. Zhang, T., Kaber, D. B., Zhu, B., Swangnetr, M., Mosaly, P., Hodge, L. (Accepted). Service Robot Feature Design Effects on User Perceptions and Emotional Responses. Submitted to Intelligent Service Robotics.
2. Zhang, T., Zhu, B., Lee, L., Swangnetr, M., Mosaly, P., & Kaber, D. B. (2009). Service Robot Feature and Interface Design Effects on User Emotional Responses. In Proc. of the 17th Triennial Congress of the International Ergonomics Association (IEA), Beijing, China.
3. Zhang, T., Zhu, B., Lee, L., & Kaber, D. B. (2008). Service Robot Anthropomorphism and Interface Design for Emotion in Human-Robot Interaction. In Proc. of the 4th IEEE Conference on Automation Science and Engineering. Washington D.C., Aug., 23-26, 2008.
Usability Measures
As a Master of Science student at NC State, I was also required to create a thesis. The summary of the thesis can be read below.
The purpose of this research was to create a new measure of usability to aid researchers in determining whether the intent of system designers is realized and to objectively assess user behavior as a basis for interface design recommendations. The present research utilized an existing HCI framework developed by Dix et al. and a mathematical model of the average number of user actions at an interface (e.g., mouse clicks) required for task performance in order to determine an overall system usability effectiveness score. Part of the score involves usability ratings by users, while considering designer interests in interface development. The components of the Dix et al. framework include the user, input, the system and output, which are interconnected by links that affect system effectiveness. Four designers were asked to rank the importance of each link in the framework, based on their design intentions for the two online ordering interfaces. Twenty users, divided into two equal groups, were asked to complete the task of buying a certain type of computer using an existing and new prototype web interface. The new prototype was designed to increase usability with consolidated pages, more pronounced buttons and a multi-level menu structure. The users then considered the links in the Dix et al. framework and rated the specific design alternatives to provide a subjective assessment of whether the designer's intent was achieved. In web applications, the average number of clicks can be used as a measure of system performance efficiency. Markov Chain models can be used to predict human motor behavios at an interface, such as the average number of mouse clicks in a task, based on small samples of actual performance data. In the present study, online interface state transitions elected by users were recorded by a java script and used to establish probabilities for each system state. The probabilities were included in a Markov model to predict the average number of clicks in task performance. Once the average number of clicks was predicted, the subjective usability rations were divided by the Markov model output to determine a ratio of 'usability per interface action' for the system (i.e., the overall effectiveness score). The interface alternative resulting in the higher score (ratio of usability per interface action) can be said to have higher usability
The research on HRI can be seen in the following publication:
1. Lee, L., & Kaber, D.B. (2008). Assessing Interactive System Effectiveness with Usability Design Heuristics and Markov Models of User Behavior. In Proc. of the 52nd Annual Meeting of the Human Factors and Ergonomics Society, New York, New York.
The purpose of this research was to create a new measure of usability to aid researchers in determining whether the intent of system designers is realized and to objectively assess user behavior as a basis for interface design recommendations. The present research utilized an existing HCI framework developed by Dix et al. and a mathematical model of the average number of user actions at an interface (e.g., mouse clicks) required for task performance in order to determine an overall system usability effectiveness score. Part of the score involves usability ratings by users, while considering designer interests in interface development. The components of the Dix et al. framework include the user, input, the system and output, which are interconnected by links that affect system effectiveness. Four designers were asked to rank the importance of each link in the framework, based on their design intentions for the two online ordering interfaces. Twenty users, divided into two equal groups, were asked to complete the task of buying a certain type of computer using an existing and new prototype web interface. The new prototype was designed to increase usability with consolidated pages, more pronounced buttons and a multi-level menu structure. The users then considered the links in the Dix et al. framework and rated the specific design alternatives to provide a subjective assessment of whether the designer's intent was achieved. In web applications, the average number of clicks can be used as a measure of system performance efficiency. Markov Chain models can be used to predict human motor behavios at an interface, such as the average number of mouse clicks in a task, based on small samples of actual performance data. In the present study, online interface state transitions elected by users were recorded by a java script and used to establish probabilities for each system state. The probabilities were included in a Markov model to predict the average number of clicks in task performance. Once the average number of clicks was predicted, the subjective usability rations were divided by the Markov model output to determine a ratio of 'usability per interface action' for the system (i.e., the overall effectiveness score). The interface alternative resulting in the higher score (ratio of usability per interface action) can be said to have higher usability
The research on HRI can be seen in the following publication:
1. Lee, L., & Kaber, D.B. (2008). Assessing Interactive System Effectiveness with Usability Design Heuristics and Markov Models of User Behavior. In Proc. of the 52nd Annual Meeting of the Human Factors and Ergonomics Society, New York, New York.
Great UX designers have a desire to innovate and gather knowledge about potential users and customers, and the humility to know that their first design iterations will rarely be great. - Kara Pernice