huashen218/convxai-cia-dataset huggingface dataset:
Title: TapGazer: Text Entry with Finger Tapping and Gaze-directed Word Selection
Authors: family: He
given: Zhenyi; family: Lutteroth
given: Christof; family: Perlin
given: Ken
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Text Entry, Typing, Virtual Reality, Eye Tracking, Input Techniques

Abstract: While using VR, efficient text entry is a challenge: users cannot easily locate standard physical keyboards, and keys are often out of reach, e.g. when standing. We present TapGazer, a text entry system where users type by tapping their fingers in place. Users can tap anywhere as long as the identity of each tapping finger can be detected with sensors. Ambiguity between different possible input words is resolved by selecting target words with gaze. If gaze tracking is unavailable, ambiguity is resolved by selecting target words with additional taps. We evaluated TapGazer for seated and standing VR: seated novice users using touchpads as tap surfaces reached 44.81 words per minute (WPM), 79.17% of their QWERTY typing speed. Standing novice users tapped on their thighs with touch-sensitive gloves, reaching 45.26 WPM (71.91%). We analyze TapGazer with a theoretical performance model and discuss its potential for text input in future AR scenarios.
Title: Passages: Interacting with Text Across Documents
Authors: family: Han
given: Han L.; family: Yu
given: Junhang; family: Bournet
given: Raphael; family: Ciorascu
given: Alexandre; family: Mackay
given: Wendy E.; family: Beaudouin-Lafon
given: Michel
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Reification, Knowledge Work, Sensemaking, Provenance, Document Management, Active Reading

Abstract: A key aspect of knowledge work is the analysis and manipulation of sets of related documents. We conducted interviews with 12 patent examiners and 12 scientists and found that all face difficulties using specialized tools for managing text from multiple documents across interconnected activities, including searching, collecting, annotating, organizing, writing and reviewing, while manually tracking their provenance. We introduce Passages, interactive objects that reify text selections and can then be manipulated, reused, and shared across multiple tools. Passages directly supports the above-listed activities as well as fluid transitions among them. Two user studies show that participants found Passages both elegant and powerful, facilitating their work practices and enabling greater reuse and novel strategies for analyzing and composing documents. We argue that Passages offers a general approach applicable to a wide variety of text-based interactions.
Title: The Sound of Hallucinations: Toward a more convincing emulation of internalized voices
Authors: family: Lee
given: Hyejin; family: Jiang
given: Ruixi; family: Yoo
given: Yongjae; family: Henry
given: Max; family: Cooperstock
given: Jeremy R.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Voice Transformation Interface, Avatar Therapy, Speech Synthesis

Abstract: The need to generate convincing simulation of voices often arises in the context of avatar therapy, a treatment approach for disorders such as schizophrenia. This treatment involves patients interacting with simulations of the entity they imagine to be responsible for the voices they hear, for which there is often no external reference available. However, in such scenarios, there is little knowledge of how to design and reproduce these voices in a convincing manner. Existing voice manipulation interfaces are often complex to use, and highly limited in their ability to modify vocal characteristics beyond small adjustments. To address these challenges, we designed a framework that allows users to explore and select from a large set of voices, and thereafter manipulate the voice(s) to converge towards an effective match for one they have in mind. We demonstrated both the usability and superior performance of this system compared to existing voice manipulation interfaces.
Title: MyDJ: Sensing Food Intakes with an Attachable on Your Eyeglass Frame
Authors: family: Shin
given: Jaemin; family: Lee
given: Seungjoo; family: Gong
given: Taesik; family: Yoon
given: Hyungjun; family: Roh
given: Hyunchul; family: Bianchi
given: Andrea; family: Lee
given: Sung-Ju
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: automated dietary monitoring, eating detection, multimodal sensing, wearable computing

Abstract: Various automated eating detection wearables have been proposed to monitor food intakes. While these systems overcome the forgetfulness of manual user journaling, they typically show low accuracy at outside-the-lab environments or have intrusive form-factors (e.g., headgear). Eyeglasses are emerging as a socially-acceptable eating detection wearable, but existing approaches require custom-built frames and consume large power. We propose MyDJ, an eating detection system that could be attached to any eyeglass frame. MyDJ achieves accurate and energy-efficient eating detection by capturing complementary chewing signals on a piezoelectric sensor and an accelerometer. We evaluated the accuracy and wearability of MyDJ with 30 subjects in uncontrolled environments, where six subjects attached MyDJ on their own eyeglasses for a week. Our study shows that MyDJ achieves 0.919 F1-score in eating episode coverage, with 4.03 × battery time over the state-of-the-art systems. In addition, participants reported wearing MyDJ was almost as comfortable (94.95%) as wearing regular eyeglasses.
Title: Towards Design Guidelines for Effective Health-Related Data Videos: An Empirical Investigation of Affect, Personality, and Video Content
Authors: family: Sallam
given: Samar; family: Sakamoto
given: Yumiko; family: Leboe-McGowan
given: Jason; family: Latulipe
given: Celine; family: Irani
given: Pourang
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: data storytelling, actionable, personality, narrative visualization, affect, persuasive technology, solution, physical activity, attitude change, Data Video

Abstract: Data Videos (DVs), or animated infographics that tell stories with data, are becoming increasingly popular. Despite their potential to induce attitude change, little is explored about how to produce effective DVs. This paper describes two studies that explored factors linked to the potential of health DVs to improve viewers’ behavioural change intentions. We investigated: 1) how viewers’ affect is linked to their behavioural change intentions; 2) how these affect are linked to the viewers’ personality traits; 3) which attributes of DVs are linked to their persuasive potential. Results from both studies indicated that viewers’ negative affect lowered their behavioural change intentions. Individuals with higher neuroticism exhibited higher negative affect and were harder to convince. Finally, Study 2 proved that providing any solutions to the health problem, presented in the DV, made the viewers perceive the videos as more actionable while lowering their negative affect, and importantly, induced higher behavioural change intentions.
Title: Investigating and Validating On-body Temperature Sensors for Personal Heat Exposure Tracking.
Authors: family: Habeeb
given: Dana; family: Clawson
given: James; family: Zakeresfahani
given: Arash; family: Holtz
given: Zebulon
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: personal heat exposures, wearable sensors, extreme heat, urban heat island

Abstract: Individuals and communities around the world are increasingly exposed to extreme heat as a result of climate change. Urban residents are particularly vulnerable to extreme heat due to the urban heat island effect. However, understanding an individual’s heat exposure and risk is difficult to assess due to variations in temperature within an urban environment. In this paper, we examine the potential for wearable temperature sensors to accurately measure personal heat exposure. We synthesize literature from fields spanning urban planning to public health and present the results of a user study validating a set of four commonly used off-the-shelf temperature sensors in two different urban settings across five on-body locations. Our investigation found that wearable temperature sensors are less reliable in highly urban areas and when worn in direct sunlight. We discuss important design considerations for wearable temperature sensors and identify actionable ways to improve future studies.
Title: Designing and Evaluating a Chatbot for Survivors of Image-Based Sexual Abuse
Authors: family: Maeng
given: Wookjae; family: Lee
given: Joonhwan
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: revenge porn, chatbot, emotional support, image-based sexual abuse, information support

Abstract: Image-based sexual abuse (IBSA) is a severe social problem that causes survivors tremendous pain. IBSA survivors may encounter a lack of information and victim blame when seeking online and offline assistance. While institutions support survivors, they cannot be available 24 hours a day. Because the immediate reaction to IBSA is crucial to remove intimate images and prevent further distribution, survivors need first responders who are always accessible and do not blame them. Chatbots are constantly available, do not judge the conversation partner, and may deliver structured information and words of comfort. Therefore, we developed a chatbot to provide information and emotional support to IBSA survivors in dealing with their abuse. We analyzed nine chatbots for sexual violence survivors to identify common design elements. In addition, we sought advice from five professional counselors about the challenges survivors have while responding to their harm. We conducted a user study with 25 participants to determine the chatbot’s effectiveness in providing information and emotional support compared to internet search. The chatbot was better than the internet search regarding information organization, accessibility, and conciseness. Furthermore, the chatbot excels in providing emotional support to survivors. We discuss the survivor-centered information structure and design consideration of emotionally supportive conversation.
Title: A Conversational Approach for Modifying Service Mashups in IoT Environments
Authors: family: Kim
given: Sanghoon; family: Ko
given: In-Young
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: IoT Service Mashup, Smart Home, Conversational Interface, Conversational Mashup Modification

Abstract: Existing conversational approaches for Internet of Things (IoT) service mashup do not support modification because of the usability challenge, although it is common for users to modify the service mashups in IoT environments. To support the modification of IoT service mashups through conversational interfaces in a usable manner, we propose the conversational mashup modification agent (CoMMA). Users can modify IoT service mashups using CoMMA through natural language conversations. CoMMA has a two-step mashup modification interaction, an implicature-based localization step, and a modification step with a disambiguation strategy. The localization step allows users to easily search for a mashup by vocalizing their expressions in the environment. The modification step supports users to modify mashups by speaking simple modification commands. We conducted a user study and the results show that CoMMA is as effective as visual approaches in terms of task completion time and perceived task workload for modifying IoT service mashups.
Title: Improving Finger Stroke Recognition Rate for Eyes-Free Mid-Air Typing in VR
Authors: family: Singhal
given: Yatharth; family: Noeske
given: Richard Huynh; family: Bhardwaj
given: Ayush; family: Kim
given: Jin Ryong
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Deep Neural Network, Eyes-free Typing, Mid-Air Typing, Keystroke Classification, Text Entry

Abstract: We examine mid-air typing data collected from touch typists to evaluate the features and classification models for recognizing finger stroke. A large number of finger movement traces have been collected using finger motion capture systems, labeled into individual finger strokes, and classified into several key features. We test finger kinematic features, including 3D position, velocity, acceleration, and temporal features, including previous fingers and keys. Based on this analysis, we assess the performance of various classifiers, including Naive Bayes, Random Forest, Support Vector Machines, and Deep Neural Networks, in terms of the accuracy for correctly classifying the keystroke. We finally incorporate a linguistic heuristic to explore the effectiveness of the character prediction model and improve the total accuracy.
Title: TriboTouch: Micro-Patterned Surfaces for Low Latency Touchscreens
Authors: family: Shultz
given: Craig; family: Kim
given: Daehwa; family: Ahuja
given: Karan; family: Harrison
given: Chris
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Input Techniques, Tribology, Touchscreens, Sensors, Latency

Abstract: Touchscreen tracking latency, often 80ms or more, creates a rubber-banding effect in everyday direct manipulation tasks such as dragging, scrolling, and drawing. This has been shown to decrease system preference, user performance, and overall realism of these interfaces. In this research, we demonstrate how the addition of a thin, 2D micro-patterned surface with 5 micron spaced features can be used to reduce motor-visual touchscreen latency. When a finger, stylus, or tangible is translated across this textured surface frictional forces induce acoustic vibrations which naturally encode sliding velocity. This acoustic signal is sampled at 192kHz using a conventional audio interface pipeline with an average latency of 28ms. When fused with conventional low-speed, but high-spatial-accuracy 2D touch position data, our machine learning model can make accurate predictions of real time touch location.
Title: HybridTrak: Adding Full-Body Tracking to VR Using an Off-the-Shelf Webcam
Authors: family: Yang
given: Jackie (Junrui); family: Chen
given: Tuochao; family: Qin
given: Fang; family: Lam
given: Monica S.; family: Landay
given: James A.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: computer vision., virtual reality, full-body tracking

Abstract: Full-body tracking in virtual reality improves presence, allows interaction via body postures, and facilitates better social expression among users. However, full-body tracking systems today require a complex setup fixed to the environment (e.g., multiple lighthouses/cameras) and a laborious calibration process, which goes against the desire to make VR systems more portable and integrated. We present HybridTrak, which provides accurate, real-time full-body tracking by augmenting inside-out1 upper-body VR tracking systems with a single external off-the-shelf RGB web camera. HybridTrak uses a full-neural solution to convert and transform users’ 2D full-body poses from the webcam to 3D poses leveraging the inside-out upper-body tracking data. We showed HybridTrak is more accurate than RGB or depth-based tracking methods on the MPI-INF-3DHP dataset. We also tested HybridTrak in the popular VRChat app and showed that body postures presented by HybridTrak are more distinguishable and more natural than a solution using an RGBD camera.
Title: Integrating Gaze and Speech for Enabling Implicit Interactions
Authors: family: Khan
given: Anam Ahmad; family: Newn
given: Joshua; family: Bailey
given: James; family: Velloso
given: Eduardo
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: voice interfaces, natural gaze, implicit annotation, semantic similarity, natural language processing

Abstract: Gaze and speech are rich contextual sources of information that, when combined, can result in effective and rich multimodal interactions. This paper proposes a machine learning-based pipeline that leverages and combines users’ natural gaze activity, the semantic knowledge from their vocal utterances and the synchronicity between gaze and speech data to facilitate users’ interaction. We evaluated our proposed approach on an existing dataset, which involved 32 participants recording voice notes while reading an academic paper. Using a Logistic Regression classifier, we demonstrate that our proposed multimodal approach maps voice notes with accurate text passages with an average F1-Score of 0.90. Our proposed pipeline motivates the design of multimodal interfaces that combines natural gaze and speech patterns to enable robust interactions.
Title: ‘Are They Doing Better In The Clinic Or At Home?’: Understanding Clinicians’ Needs When Visualizing Wearable Sensor Data Used In Remote Gait Assessments For People With Multiple Sclerosis
Authors: family: Seals
given: Ayanna; family: Pilloni
given: Giuseppina; family: Kim
given: Jin; family: Sanchez
given: Raul; family: Rizzo
given: John-Ross; family: Charvet
given: Leigh; family: Nov
given: Oded; family: Dove
given: Graham
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: wearables, transition to clinical practice, data visualization, gait assessments, remote care, multiple sclerosis

Abstract: Walking impairment is a debilitating symptom of Multiple Sclerosis (MS), a disease affecting 2.8 million people worldwide. While clinicians’ in-person observational gait assessments are important, research suggests that data from wearable sensors can indicate early onset of gait impairment, track patients’ responses to treatment, and support remote and longitudinal assessment. We present an inquiry into supporting the transition from research to clinical practice. Co-design by HCI, biomedical, neurology and rehabilitation researchers resulted in a data-rich interface prototype for augmented gait analysis based on visualized sensor data. We used this as a prompt in interviews with ten experienced clinicians from a range of MS rehabilitation roles. We find that clinicians value quantitative sensor data within a whole patient narrative, to help track specific rehabilitation goals, but identify a tension between grasping critical information quickly and more detailed understanding. Based on the findings we make design recommendations for data-rich remote rehabilitation interfaces.
Title: Investigating Clutching Interactions for Touchless Medical Imaging Systems
Authors: family: Cronin
given: Sean; family: Freeman
given: Euan; family: Doherty
given: Gavin
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Touchless Interaction, Gestures, Clutching, Medical Imaging Systems, Midas Touch, PACS

Abstract: Touchless input could transform clinical activity by allowing health professionals direct control over medical imaging systems in a sterile manner. Currently, users face the issues of being unable to directly manipulate imaging in aseptic environments, as well as needing to touch shared surfaces in other hospital areas. Unintended input is a key challenge for touchless interaction and could be especially disruptive in medical contexts. We evaluated four clutching techniques with 34 health professionals, measuring interaction performance and interviewing them to obtain insight into their views on clutching, and touchless control of medical imaging. As well as exploring the performance of the different clutching techniques, our analysis revealed an appetite for reliable touchless interfaces, a strong desire to reduce shared surface contact, and suggested potential improvements such as combined authentication and touchless control. Our findings can inform the development of novel touchless medical systems and identify challenges for future research.
Title: Designing Flexible Longitudinal Regimens: Supporting Clinician Planning for Discontinuation of Psychiatric Drugs
Authors: family: Jo
given: Eunkyung; family: Ryu
given: Myeonghan; family: Kenderova
given: Georgia; family: So
given: Samuel; family: Shapiro
given: Bryan; family: Papoutsaki
given: Alexandra; family: Epstein
given: Daniel A.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Clinical decision support systems, Psychiatric drugs, Antidepressants, Planning

Abstract: Clinical decision support tools have typically focused on one-time support for diagnosis or prognosis, but have the ability to support providers in longitudinal planning of patient care regimens amidst infrastructural challenges. We explore an opportunity for technology support for discontinuing antidepressants, where clinical guidelines increasingly recommend gradual discontinuation over abruptly stopping to avoid withdrawal symptoms, but providers have varying levels of experience and diverse strategies for supporting patients through discontinuation. We conducted two studies with 12 providers, identifying providers’ needs in developing discontinuation plans and deriving design guidelines. We then iteratively designed and implemented AT Planner, instantiating the guidelines by projecting taper schedules and providing flexibility for adjustment. Provider feedback on AT Planner highlighted that discontinuation plans required balancing interpersonal and infrastructural constraints and surfaced the need for different technological support based on clinical experience. We discuss the benefits and challenges of incorporating flexibility and advice into clinical planning tools.
Title: A State-Based Medication Routine Framework
Authors: family: Caldeira
given: Clara; family: Bhowmick
given: Pallabi; family: Komarlingam
given: Priya; family: Siek
given: Katie A.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: health, adherence, medication

Abstract: Forgetfulness is a primary factor of medication nonadherence, a problem that contributes to worse health outcomes and increased mortality among people with chronic conditions. Common strategies to address forgetfulness, such as timed reminders, have limited effectiveness. However, there is limited information about why these strategies fail. To address this gap, we conducted interviews with people who take medications daily and miss doses at least twice a month. We contribute a state-based Medication Routine Framework composed of four states (Wellness, New Task, Erratic, and Disruption) in two axes (regularity and time scale). Because most nonadherence due to forgetfulness occurs in nonroutine states (i.e., Erratic and Disruption state), we argue that improving technology for medication adherence requires designing for these states. In this paper, we describe each state in detail and discuss opportunities for adapting medication reminder strategies to overcome the challenges of nonroutine states.