Chungkuk Yoo

E-Gesture: a collaborative architecture for energy-efficient gesture recognition with hand-worn sensor and mobile devices

Gesture is a promising mobile User Interface modality that enables eyes-free interaction without stopping or impeding movement. In this paper, we present the design, implementation, and evaluation of E-Gesture, an energy-efficient gesture recognition system using a hand-worn sensor device and a smartphone. E-gesture employs a novel gesture recognition architecture carefully crafted by studying sporadic occurrence patterns of gestures in continuous sensor data streams and analyzing the energy consumption characteristics of both sensors and smartphones. We developed a closed-loop collaborative segmentation architecture, that can (1) be implemented in resource-scarce sensor devices, (2) adaptively turn off power-hungry motion sensors without compromising recognition accuracy, and (3) reduce false segmentations generated from dynamic changes of body movement. We also developed a mobile gesture classification architecture for smartphones that enables HMM-based classification models to better fit multiple mobility situations.

SocioPhone: everyday face-to-face interaction monitoring platform using multi-phone sensor fusion

In this paper, we propose SocioPhone, a novel initiative to build a mobile platform for face-to-face interaction monitoring. Face-to-face interaction, especially conversation, is a fundamental part of everyday life. Interaction-aware applications aimed at facilitating group conversations have been proposed, but have not proliferated yet. Useful contexts to capture and support face-to-face interactions need to be explored more deeply. More important, recognizing delicate conversational contexts with commodity mobile devices requires solving a number of technical challenges. As a first step to address such challenges, we identify useful meta-linguistic contexts of conversation, such as turn-takings, prosodic features, a dominant participant, and pace. These serve as cornerstones for building a variety of interaction-aware applications. SocioPhone abstracts such useful meta-linguistic contexts as a set of intuitive APIs. Its runtime efficiently monitors registered contexts during in-progress conversations and notifies applications on-the-fly. Importantly, we have noticed that online turn monitoring is the basic building block for extracting diverse meta-linguistic contexts, and have devised a novel volume-topography-based method. We show the usefulness of SocioPhone with several interesting applications: SocioTherapist, SocioDigest, and Tug-of-War. Also, we show that our turn-monitoring technique is highly accurate and energy-efficient under diverse real-life situations.

Demo: e-gesture - a collaborative architecture for energy-efficient gesture recognition with hand-worn sensor and mobile devices

We demonstrate E-Gesture, a collaborative architecture for energy-efficient gesture recognition on a hand-worn sensor device and an off-the-shelf smartphone that greatly reduces energy consumption while achieving high accuracy recognition under dynamic mobile situations. E-gesture employs a novel gesture segmentation and classification architecture carefully crafted by studying sporadic occurrence patterns of gestures in continuous sensor data streams and analyzing energy consumption characteristics in both sensor and smartphone.

The dynamic analysis of metal transfer in pulsed current gas metal arc welding

The dynamic characteristics of metal transfer in the pulsed current gas metal arc welding are analysed using the volume of fluid method incorporating the electromagnetic force. The surface profile and the velocity distribution of fluid in the drop are computed numerically. The viscous flow and inertial force of a molten metal generated by the peak current are found to have significant effects on the metal transfer and drop detachment. The ranges of the pulsing frequency for which one drop is detached per current pulse are predicted and calculated results are in good agreement with the experimental data with some discrepancy for low load duty cycles.

TalkBetter: family-driven mobile intervention care for children with language delay

Language delay is a developmental problem of children who do not acquire language as expected for their chronological ages. Without timely intervention, language delay can act as a lifelong risk factor. Speech-language pathologists highlight that effective parent participation in everyday parent-child conversation is important to treat childrens language delay. For effective roles, however, parents need to alter their own lifelong-established conversation habits, requiring extensive period of conscious effort and staying alert. In this paper, we present new opportunities for mobile and social computing to reinforce everyday parent-child conversation with therapeutic implications for children with language delays. Specifically, we propose TalkBetter, a mobile in-situ intervention service to help parents in daily parent-child conversation through real-time meta-linguistic analysis of ongoing conversations. Through extensive field studies with speech-language pathologists and parents, we report the multilateral motivations and implications of TalkBetter. We present our development of TalkBetter prototype and report its performance evaluation.

Demo: ExerLink - enabling pervasive social exergames with heterogeneous exercise devices

We envision that diverse social exercising games, or exergames, will emerge, featuring much richer interactivity with immersive game play experiences. Further, the recent advances of mobile devices and wireless networking will make such social engagement more pervasive - people carry portable exergame devices (e.g., jump ropes) and interact with remote users anytime, anywhere. Towards this goal, we explore the potential of using heterogeneous exercise devices as game controllers for a multi-player social exergame; e.g., playing a boat paddling game with two remote exercisers (one with a jump rope, and the other with a treadmill). In this paper, we propose a novel platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. We report the design considerations and guidelines obtained from the design and development processes of game controllers. We validate the efficacy of game controllers and demonstrate the feasibility of social exergames with heterogeneous exercise devices via extensive human subject studies.

Understanding customer malling behavior in an urban shopping mall using smartphones

This paper presents a novel customer malling behavior modeling framework for an urban shopping mall. As an automated computing framework using smartphones, it is designed to provide comprehensive understanding of customer behavior. We prototype the framework in a real-world urban shopping mall. Development consists of three steps; customer data collection, customer trace extraction, and behavior model analysis. We extract customer traces from a collection of 701-hour sensor data from 195 in-situ customers who installed our logging application at Android Market. The practical behavior model is created from the real traces. It has a multi-level structure to provide the holistic understanding of customer behavior from physical movement to service semantics. As far as we know, it is the first work to understand complex customer malling behavior in offline shopping malls.

Transforming solitary exercises into social exergames

This paper discusses an approach for transforming solitary exercises into social exergames. We frame our discussion by highlighting the relation between the original exercises and game interactions, and by analyzing an example exergame which is successfully transformed from its original solitary exercise. We present a user study of the exergame that evaluates the need for holistic transformation strategies from solitary exercises into social exergames.

Dimensional analysis of metal transfer in GMA welding

Various welding parameters as well as the material properties of the electrode should be taken into consideration simultaneously for the analysis of the metal-transfer phenomena of gas-metal-arc (GMA) welding. In order to determine the dominant factors, which affect the metal transfer mode, a dimensional analysis is conducted in this study. Several dimensionless numbers are derived and the NSE number, which represents the ratio of the electromagnetic force and the surface tension, is found to have the most dominant effects on the metal-transfer mode over the whole range of welding conditions. The transition current and detaching-droplet size calculated from the analyses are compared with experimental data and resulted in reasonably good agreement.

Sinabro: opportunistic and unobtrusive mobile electrocardiogram monitoring system

In this paper, we propose Sinabro, an opportunistic and unobtrusive mobile electrocardiogram (ECG) monitoring system that monitors the users ECG opportunistically during daily smartphone use. Daily ECG monitoring will open up an unprecedented opportunity for pervasive healthcare applications. It will enable the daily detection and prevention of heart problems and also allow inferences about stress, emotion, and even sleep quality. Despite its huge potential, daily ECG monitoring still has not become reality due to its obtrusiveness. In this paper, we first study the potential opportunity to capture ECGs from daily use of smartphones, without requiring the users explicit attention. Based on such an opportunity, we present a prototype ECG sensor that allows neat integration with a smartphone and the Sinabro system to provide ECG-related physiological status. We show the basic feasibility of our approach, based on daily smartphone usage through phone usage analysis and prototype-based experiments.