37 citations · 102 across the 13 of their papers we have counts for
14 papers
Guided Reflection for Personal Sleep Insight in Everyday Sleep Tracking
Bokyung Kim, Amama Mahmood, Honghao Zhao +4
Digital sleep technologies make tracking accessible, yet users often struggle to interpret what changes in their sleep mean. Behavioral sleep medicine addresses this through guided…
ERR@HRI 3.0 Challenge: Multimodal Detection of Errors and Anticipation in Human-Robot Interactions
Maria Teresa Parreira, Micol Spitale, Maia Stiber +5
As robots become increasingly integrated into human environments, their ability to detect and respond to errors remains critical for maintaining user trust and interaction quality.…
Better Adherence, Richer Context: A Field Evaluation of LLM-Powered Conversational Voice Diaries for Sleep
Amama Mahmood, Bokyung Kim, Honghao Zhao +4
Sleep diaries are central to behavioral sleep medicine and cognitive behavioral therapy for insomnia, yet daily completion is difficult to sustain, and static forms often provide l…
Conversational Agents in Behavioral Sleep Medicine: Designing Self-Report and Analytics Tools
Amama Mahmood, Bokyung Kim, Honghao Zhao +4
The sleep diary is a widely used clinical tool for understanding and treating sleep disorders in Behavioral Sleep Medicine (BSM); however, low patient compliance and limited captur…
ERR@HRI 2.0 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Conversations
Shiye Cao, Maia Stiber, Amama Mahmood +5
The integration of large language models (LLMs) into conversational robots has made human-robot conversations more dynamic. Yet, LLM-powered conversational robots remain prone to e…
Interruption Handling for Conversational Robots
Shiye Cao, Jiwon Moon, Amama Mahmood +4
Interruptions, a fundamental component of human communication, can enhance the dynamism and effectiveness of conversations, but only when effectively managed by all parties involve…
From Our Lab to Their Homes: Learnings from Longitudinal Field Research with Older Adults
Amama Mahmood, Chien-Ming Huang
Conducting research with older adults in their home environments presents unique opportunities and challenges that differ significantly from traditional lab-based studies. In this…
Voice Assistants for Health Self-Management: Designing for and with Older Adults
Amama Mahmood, Shiye Cao, Maia Stiber +2
Supporting older adults in health self-management is crucial for promoting independent aging, particularly given the growing strain on healthcare systems. While voice assistants (V…
Situated Understanding of Errors in Older Adults' Interactions with Voice Assistants: A Month-Long, In-Home Study
Amama Mahmood, Junxiang Wang, Chien-Ming Huang
Our work addresses the challenges older adults face with commercial Voice Assistants (VAs), notably in conversation breakdowns and error handling. Traditional methods of collecting…
"You Might Like It": How People Respond to Small Talk During Human-Robot Collaboration
Kaitlynn Taylor Pineda, Amama Mahmood, Juo-Tung Chen +1
Social communication between people and social robots has been studied extensively and found to have various notable benefits, including the enhancement of human-robot team cohesio…
Gender Biases in Error Mitigation by Voice Assistants
Amama Mahmood, Chien-Ming Huang
Commercial voice assistants are largely feminized and associated with stereotypically feminine traits such as warmth and submissiveness. As these assistants continue to be adopted…
"Mango Mango, How to Let The Lettuce Dry Without A Spinner?": Exploring User Perceptions of Using An LLM-Based Conversational Assistant Toward Cooking Partner
Szeyi Chan, Jiachen Li, Bingsheng Yao +5
The rapid advancement of Large Language Models (LLMs) has created numerous potentials for integration with conversational assistants (CAs) assisting people in their daily tasks, pa…
User Interaction Patterns and Breakdowns in Conversing with LLM-Powered Voice Assistants
Amama Mahmood, Junxiang Wang, Bingsheng Yao +2
Conventional Voice Assistants (VAs) rely on traditional language models to discern user intent and respond to their queries, leading to interactions that often lack a broader conte…
How Mock Model Training Enhances User Perceptions of AI Systems
Amama Mahmood, Gopika Ajaykumar, Chien-Ming Huang
Artificial Intelligence (AI) is an integral part of our daily technology use and will likely be a critical component of emerging technologies. However, negative user preconceptions…