Non-contact Force Sensing for Projected Mixed Reality Interactions

Siyou Pei, Pradyumna Chari, Xue Wang, Xiaoying Yang, Achuta Kadambi, Yang Zhang (UIST 2022)

A new sensing technology that enables non-contact force sensing on ubiquitous surfaces. No hardware components need to be instrumented on surfaces. No devices need to be worn on users' body. To investigate the feasibility of our approach, we conducted studies on metal, plastic, wood, along with a wide array of materials. We also included supplemental tests to fully tease out the performance, followed by several example applications.

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Hand Interfaces: Using Hands to Imitate Objects in AR/VR for Expressive Interactions

Siyou Pei, Alexander Chen, Jaewook Lee, Yang Zhang (CHI 2022)

Honorable Mention

A new interaction technique that lets users' hands become virtual objects by imitating the objects themselves. For example, a thumbs-up hand pose is used to mimic a joystick. We created a wide array of interaction designs around this idea to demonstrate its applicability in object retrieval and interactive control tasks. Collectively, we call these interaction designs Hand Interfaces.

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AURITUS: An Open-Source Optimization Toolkit for Training and Development of Human Movement Models and Filters Using Earables

Swapnil Sayan Saha, Mr. Sandeep Singh Sandha, Siyou Pei, Vivek Jain, Mr. Ziqi Wang, Yuchen Li, Ankur Sarker, Prof. Mani Srivastava (IMWUT 2021)

AURITUS is an extendable and open-source optimization toolkit designed to enhance and replicate earable applications. AURITUS handles data collection, pre-processing, and labeling tasks using graphical tools and provides a hardware-in-the-loop (HIL) optimizer and TinyML interface to develop lightweight and real-time machine-learning models for activity detection and filters for head-pose tracking.

 

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Quick Question: Interrupting Users for Microtasks with Reinforcement Learning

Bo-Jhang Ho, Bharathan Balaji, Mehmet Koseoglu, Sandeep Sandha, Siyou Pei, Mani Srivastava (ICML 2021 Workshop on Human in the Loop Learning)

Human attention is a scarce resource in modern computing. Quick Question explores use of reinforcement learning (RL) to schedule microtasks while minimizing user annoyance and compare its performance with supervised learning. We model the problem as a Markov decision process and use Advantage Actor Critic algorithm to identify interruptible moments.

 

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