Improving digital object handoff using the space above the table

Object handoff – that is, passing an object or tool to another person – is an extremely common activity in collaborative tabletop work. On digital tables, object handoff is typically accomplished by sliding the object on the table surface – but surface-only interactions can be slow and error-prone, particularly when there are multiple people carrying out multiple handoffs. An alternative approach is to use the space above the table for object handoff; this provides more room to move, but requires above-surface tracking. I developed two above-the-surface handoff techniques that use simple and inexpensive tracking: a force-field technique that uses a depth camera to determine hand proximity, and an electromagnetic-field technique called ElectroTouch that provides positive indication when people touch hands over the table. These new techniques were compared to three kinds of existing surface-only handoff (sliding, flicking, and surface-only Force-Fields). The study showed that the above-surface techniques significantly improved both speed and accuracy, and that ElectroTouch was the best technique overall. Also, as object interactions are moved above-the-surface of the table the representation of off-table objects becomes crucial. To address the issue of off-table digital object representation several object designs were created an evaluated. The result of the present research provides designers with practical new techniques for substantially increasing performance and interaction richness on digital tables

Long Range Automated Persistent Surveillance

This dissertation addresses long range automated persistent surveillance with focus on three topics: sensor planning, size preserving tracking, and high magnification imaging. field of view should be reserved so that camera handoff can be executed successfully before the object of interest becomes unidentifiable or untraceable. We design a sensor planning algorithm that not only maximizes coverage but also ensures uniform and sufficient overlapped camera’s field of view for an optimal handoff success rate. This algorithm works for environments with multiple dynamic targets using different types of cameras. Significantly improved handoff success rates are illustrated via experiments using floor plans of various scales. Size preserving tracking automatically adjusts the camera’s zoom for a consistent view of the object of interest. Target scale estimation is carried out based on the paraperspective projection model which compensates for the center offset and considers system latency and tracking errors. A computationally efficient foreground segmentation strategy, 3D affine shapes, is proposed. The 3D affine shapes feature direct and real-time implementation and improved flexibility in accommodating the target’s 3D motion, including off-plane rotations. The effectiveness of the scale estimation and foreground segmentation algorithms is validated via both offline and real-time tracking of pedestrians at various resolution levels. Face image quality assessment and enhancement compensate for the performance degradations in face recognition rates caused by high system magnifications and long observation distances. A class of adaptive sharpness measures is proposed to evaluate and predict this degradation. A wavelet based enhancement algorithm with automated frame selection is developed and proves efficient by a considerably elevated face recognition rate for severely blurred long range face images

Toward Effective Physical Human-Robot Interaction

With the fast advancement of technology, in recent years, robotics technology has significantly matured and produced robots that are able to operate in unstructured environments such as domestic environments, offices, hospitals and other human-inhabited locations. In this context, the interaction and cooperation between humans and robots has become an important and challenging aspect of robot development. Among the various kinds of possible interactions, in this Ph.D. thesis I am particularly interested in physical human-robot interaction (pHRI). In order to study how a robot can successfully engage in physical interaction with people and which factors are crucial during this kind of interaction, I investigated how humans and robots can hand over objects to each other. To study this specific interactive task I developed two robotic prototypes and conducted human-robot user studies. Although various aspects of human-robot handovers have been deeply investigated in the state of the art, during my studies I focused on three issues that have been rarely investigated so far: Human presence and motion analysis during the interaction in order to infer non-verbal communication cues and to synchronize the robot actions with the human motion; Development and evaluation of human-aware pro-active robot behaviors that enable robots to behave actively in the proximity of the human body in order to negotiate the handover location and to perform the transfer of the object; Consideration of objects grasp affordances during the handover in order to make the interaction more comfortable for the human

Network selection mechanism for telecardiology application in high speed environment

The existing network selection schemes biased either to cost or Quality of Service (QoS) are not efficient enough for telecardiology application in high traveling speed environment. Selection of the candidate network that is fulfilling the telecardiology service requirements as well as user preference is a challenging issue. This is because the preference of telecardiology user might change based on the patient health condition. This research proposed a novel Telecardiology-based Handover Decision Making (THODM) mechanism that consists of three closely integrated algorithms: Adaptive Service Adjustment (ASA), Dwelling Time Prediction (DTP) and Patient Health Condition-based Network Evaluation (PHCNE). The ASA algorithm guarantees the quality of telecardiology service when none of the available networks fulfils the service requirements. The DTP algorithm minimizes the probability of handover failure and unnecessary handover to Wireless Local Area Network (WLAN), while optimizing the connection time with WLAN in high traveling speed environment. The PHCNE algorithm evaluates the quality of available networks and selects the best network based on the telecardiology services requirement and the patient health condition. Simulation results show that the proposed THODM mechanism reduced the number of handover failures and unnecessary handovers up to 80.0% and 97.7%, respectively, compared with existing works. The cost of THODM mechanism is 20% and 85.3% lower than the Speed Threshold-based Handover (STHO) and Bandwidth-based Handover (BWHO) schemes, respectively. In terms of throughput, the proposed scheme is up to 75% higher than the STHO scheme and 370% greater than the BWHO scheme. For telecardiology application in high traveling speed environment, the proposed THODM mechanism has better performance than the existing network selection schemes