Naveen Sendhilnathan

schoolMetabar_chart#1288 Global Rank

Total Papers

HCI Rank

#1288

Research Areas

Years Active

Recent Publications

ProMemAssist: Exploring Timely Proactive Assistance Through Working Memory Modeling in Multi-Modal Wearable Devices

Wearable AI systems aim to provide timely assistance in daily life, but existing approaches often rely on user initiation or predefined task knowledge, neglecting users' current mental states. We introduce ProMemAssist, a smart glasses system that models a user's working memory (WM) in real-time using multi-modal sens…

UIST '254th AuthorHand Gesture Recognition +2

Squiggle: Multimodal Lasso Selection in the Real World

Smart glasses devices are emerging with egocentric cameras and gaze tracking, raising the possibility of new interaction techniques that enable users to reference real-world objects they wish to digitally interact with. However, many of these devices lack a display, making precise object referencing difficult due to t…

IUI '255th+ AuthorHuman-LLM Collaboration +1

Less or More: Towards Glanceable Explanations for LLM Recommendations Using Ultra-Small Devices

Large Language Models (LLMs) have shown remarkable potential in recommending everyday actions as personal AI assistants, while Explainable AI (XAI) techniques are being increasingly utilized to help users understand why a recommendation is given. Personal AI assistants today are often located on ultra-small devices su…

CHI '253rd AuthorIn-Vehicle Haptic, Audio & Multimodal Feedback +3

Persistent Assistant: Seamless Everyday AI Interactions via Intent Grounding and Multimodal Feedback

Current AI assistants predominantly use natural language interactions, which can be time-consuming and cognitively demanding, especially for frequent, repetitive tasks in daily life. We propose Persistent Assistant, a framework for seamless and unobtrusive interactions with AI assistants. The framework has three key f…

CHI '251st AuthorSocial & Collaborative VR +2

A Multimodal Approach for Targeting Error Detection in Virtual Reality Using Implicit User Behavior

Although the point-and-select interaction method has been shown to lead to user and system-initiated errors, it is still prevalent in VR scenarios. Current solutions to facilitate selection interactions exist, however they do not address the challenges caused by targeting inaccuracy. To reduce the effort required to t…

UIST '242nd AuthorMid-Air Haptics (Ultrasonic) +2

SonoHaptics: An Audio-Haptic Cursor for Gaze-Based Object Selection in XR

We introduce SonoHaptics, an audio-haptic cursor for gaze-based 3D object selection. SonoHaptics addresses challenges around providing accurate visual feedback during gaze-based selection in Extended Reality (XR), e.g., lack of world-locked displays in no- or limited-display smart glasses and visual inconsistencies. T…

CHI '232nd AuthorVibrotactile Feedback & Skin Stimulation +2

Investigating Eyes-away Mid-air Typing in Virtual Reality using Squeeze haptics-based Postural Reinforcement

In this paper, we investigate postural reinforcement haptics for mid-air typing using squeeze actuation on the wrist. We propose and validate eye-tracking based objective metrics that capture the impact of haptics on the user's experience, which traditional performance metrics like speed and accuracy are not able to c…

UIST '221st AuthorEye Tracking & Gaze Interaction +2

Detecting Input Recognition Errors and User Errors Using Gaze Dynamics in Virtual Reality

Gesture-based recognition systems are susceptible to input recognition errors and user errors, both of which negatively affect user experiences and can be frustrating to correct. Prior work has suggested that user gaze patterns following an input event could be used to detect input recognition errors and subsequently…

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