Publications

From 2019 (Clear Search)

2019
Publication Details
  • Internet of Things: Engineering Cyber Physical Human Systems
  • Mar 15, 2019

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Recent advances on the Internet of Things (IoT) lead to an explosion of physical objects being connected to the Internet. These objects sense, compute, interpret what is occurring within themselves and the world, and preferably interact with users. In this work, we present a visible light-enabled finger tracking technique allowing users to perform freestyle multi-touch gestures on everyday object’s surface. By projecting encoded patterns onto an object’s surface (e.g. paper, display, or table) through a projector, and localizing the user’s fingers with light sensors, the proposed system offers users a richer interactive space than the device’s existing interfaces. More importantly, results from our experiments indicate that this system can localize ten fingers simultaneously with an accuracy of 1.7 millimeters and an refresh rate of 84 Hz with only 31 milliseconds delay on WiFi or 23 milliseconds delay on serial communication, easily supporting multi-finger gesture interaction on everyday ob-jects. We also develop two example applications to demonstrate possible scenarios. Finally, we conduct a pre-liminary exploration of 3D depth inference using the same setup and achieve 2.43 cm depth estimation accuracy.
Publication Details
  • IEEE 2nd International Conference on Multimedia Information Processing and Retrieval
  • Mar 14, 2019

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We present an approach to detect speech impairments from video of people with aphasia, a neurological condition that affects the ability to comprehend and produce speech. To counter inherent privacy issues, we propose a cross-media approach using only visual facial features to detect speech properties without listening to the audio content of speech. Our method uses facial landmark detections to measure facial motion over time. We show how to detect speech and pause instances based on temporal mouth shape analysis and identify repeating mouth patterns using a dynamic warping mechanism. We relate our developed features for pause frequency, mouth pattern repetitions, and pattern variety to actual symptoms of people with aphasia in the AphasiaBank dataset. Our evaluation shows that our developed features are able to reliably differentiate dysfluent speech production of people with aphasia from those without aphasia with an accuracy of 0.86. A combination of these handcrafted features and further statistical measures on talking and repetition improves classification performance to an accuracy of 0.88.
Publication Details
  • ACM Transactions on Interactive Intelligent System
  • Jan 31, 2019

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Activity recognition is a core component of many intelligent and context-aware systems. We present a solution for discreetly and unobtrusively recognizing common work activities above a work surface without using cameras.We demonstrate our approach, which utilizes an RF-radar sensor mounted under the work surface, in three domains; recognizing work activities at a convenience-store counter, recognizing common office deskwork activities, and estimating the position of customers in a showroom environment. Our examples illustrate potential benefits for both post-hoc business analytics and for real-time applications. Our solution was able to classify seven clerk activities with 94.9% accuracy using data collected in a lab environment and able to recognize six common deskwork activities collected in real offices with 95.3% accuracy. Using two sensors simultaneously, we demonstrate coarse position estimation around a large surface with 95.4% accuracy. We show that using multiple projections of RF signal leads to improved recognition accuracy. Finally, we show how smartwatches worn by users can be used to attribute an activity, recognized with the RF sensor, to a particular user in multi-user scenarios. We believe our solution can mitigate some of users’ privacy concerns associated with cameras and is useful for a wide range of intelligent systems.