David Kotz is the International Paper Professor in the Department of Computer Science and, presently, Visiting Professor in the Center for Digital Health Interventions at ETH Zurich. He previously served as Interim Provost, as Associate Dean of the Faculty for the Sciences, as the Executive Director of the Institute for Security Technology Studies, and on the US Healthcare IT Policy Committee. His research interests include security and privacy, pervasive computing for healthcare, and wireless networks. He has published over 200 refereed papers, obtained over $67m in grant funding, and mentored nearly 100 research students. He is a Fellow of the IEEE, a Distinguished Member of the ACM, a 2008 Fulbright Fellow to India, and an elected member of Phi Beta Kappa.
After receiving his A.B. in Computer Science and Physics from Dartmouth in 1986, he completed his Ph.D in Computer Science from Duke University in 1991 and returned to Dartmouth to join the faculty. For more information see http://www.cs.dartmouth.edu/~dfk/.
Abstract: We present Tessutivo, a contact-based inductive sensing technique for contextual interactions on interactive fabrics. Our technique recognizes conductive objects (mainly metallic) that are commonly found in households and workplaces, such as keys, coins, and electronic devices. We built a prototype containing six by six spiral-shaped coils made of conductive thread, sewn onto a four-layer fabric structure. We carefully designed the coil shape parameters to maximize the sensitivity based on a new inductance approximation formula. Through a ten-participant study, we evaluated the performance of our proposed sensing technique across 27 common objects. We yielded 93.9% real-time accuracy for object recognition. We conclude by presenting several applications to demonstrate the unique interactions enabled by our technique.
David Kotz recently presented an invited webinar lecture that provides an overview of the Auracle and results of some of our experiments in validating Auracle.
David Kotz recently presented an invited webinar lecture in the Mobile Data to Knowledge (MD2K) program. The second half of that lecture (starting at 23:29) provides an overview of the Auracle and results of some of our experiments in validating Auracle. (The first half describes the Amulet project – also worth checking out!)
The Auracle team presented an innovative sensing technology for interacting with wearable devices, during the UIST 2018 in Berlin, Germany.
Abstract: We present Indutivo, a contact-based inductive sensing technique for contextual interactions. Our technique recognizes conductive objects (metallic primarily) that are commonly found in households and daily environments, as well as their individual movements when placed against the sensor. These movements include sliding, hinging, and rotation. We describe our sensing principle and how we designed the size, shape, and layout of our sensor coils to optimize sensitivity, sensing range, recognition and tracking accuracy. Through several studies, we also demonstrated the performance of our proposed sensing technique in environments with varying levels of noise and interference conditions. We conclude by presenting demo applications on a smartwatch, as well as insights and lessons we learned from our experience.
Read the full paper in ACM digital library:
Jun Gong, Xin Yang, Teddy Seyed, Josh Urban Davis, and Xing-Dong Yang. 2018. Indutivo: Contact-Based, Object-Driven Interactions with Inductive Sensing. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology (UIST ’18). ACM, pp.321-333. DOI: https://doi.org/10.1145/3242587.3242662
Paper to appear in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (Ubicomp’18).
Abstract: In this paper, we propose Auracle, a wearable earpiece that can automatically recognize eating behavior. More specifically, in free-living conditions, we can recognize when and for how long a person is eating. Using an off-the-shelf contact microphone placed behind the ear, Auracle captures the sound of a person chewing as it passes through the bone and tissue of the head. This audio data is then processed by a custom analog/digital circuit board. To ensure reliable (yet comfortable) contact between microphone and skin, all hardware components are incorporated into a 3D-printed behind-the-head framework. We collected field data with 14 participants for 32 hours in free-living conditions and additional eating data with 10 participants for 2 hours in a laboratory setting. We achieved accuracy exceeding 92.8% and F1 score exceeding 77.5% for eating detection. Moreover, Auracle successfully detected 20-24 eating episodes (depending on the metrics) out of 26 in free-living conditions. We demonstrate that our custom device could sense, process, and classify audio data in real time. Additionally, we estimate Auracle can last 28.1 hours with a 110 mAh battery while communicating its observations of eating behavior to a smartphone over Bluetooth.
Shengjie Bi, Tao Wang, Nicole Tobias, Josephine Nordrum, Shang Wang, George Halvorsen, Sougata Sen, Ronald Peterson, Kofi Odame, Kelly Caine, Ryan Halter, Jacob Sorber, and David Kotz. Auracle: Detecting Eating Episodes with an Ear-Mounted Sensor. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (Ubicomp), 2(3), September 2018. DOI 10.1145/3264902.
The Auracle team presented an innovative new technique for interacting with wearable devices, during the UIST conference last week.
Abstract: We present Pyro, a micro thumb-tip gesture recognition technique based on thermal infrared signals radiating from the fingers. Pyro uses a compact, low-power passive sensor, making it suitable for wearable and mobile applications. To demonstrate the feasibility of Pyro, we developed a self-contained prototype consisting of the infrared pyroelectric sensor, a custom sensing circuit, and software for signal processing and machine learning. A ten-participant user study yielded a 93.9% cross-validation accuracy and 84.9% leave-one-session-out accuracy on six thumb-tip gestures. Subsequent lab studies demonstrated Pyro’s robustness to varying light conditions, hand temperatures, and background motion. We conclude by discussing the insights we gained from this work and future research questions.
J. Gong, Y. Zhang, X. Zhou, and X.-D. Yang, “Pyro: Thumb-Tip gesture recognition using pyroelectric infrared sensing,” in Proceedings of the Annual ACM Symposium on User Interface Software and Technology (UIST). ACM Press, Oct. 2017, pp. 553-563. Available: http://dx.doi.org/10.1145/3126594.3126615
A large subset of the Auracle team gathered in Hanover for a very productive full-day retreat, mapping out the coming year’s research agenda and making plans for new prototypes and papers. Great to have everyone (almost everyone) in one place!