Routine inspections for critical infrastructures such as bridges are required in most jurisdictions worldwide. Such routine inspections are largely visual in nature, which are qualitative, subjective, and not repeatable. Although robotic infrastructure inspections address such limitations, they cannot replace the superior ability of experts to make decisions in complex situations, thus making human-robot interaction systems a promising technology. This study presents a novel gaze-based human-robot interaction system, designed to augment the visual inspection performance through mixed reality. Through holograms from a mixed reality device, gaze can be utilized effectively to estimate the properties of the defect in real-time. Additionally, inspectors can monitor the inspection progress on-line, which enhances the speed of the entire inspection process. Limited controlled experiments demonstrate its effectiveness across various users and defect types. To our knowledge, this is the first demonstration of the real-time application of eye gaze in civil infrastructure inspections.

In the traditional human activity recognition field, human activity has been classified into two categories: exercise type and exercise posture. However, as the internet of things technology and wearable devices have been developed and become popular, in order to provide useful services, it is necessary to classify daily activities as well as existing activities. In this paper, we propose a novel classification model that classifies human activities into 11 different categories including activities that are highly active and less active in daily life. We collect data with an off-the-shelf smartwatch and use a deep learning model with a convolution neural network for the classification. An extensive evaluation shows that various daily human activities can be classified with 97.19% accuracy.

Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off‐the‐shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%.

Widespread deployment of infrastructure WLANs has made Wi-Fi an integral part of today's Internet access technology. Despite its crucial role in affecting end-to-end performance, past research has focused on MAC protocol enhancement, analysis, and simulation-based performance evaluation without sufficient consideration for modeling inaccuracies stemming from interlayer dependencies, including physical layer diversity, that significantly impact performance. We take a fresh look at IEEE 802.11 WLANs and using experiment, simulation, and analysis demonstrate its surprisingly agile performance traits. Our findings are two-fold. First, contention-based MAC throughput degrades gracefully under congested conditions, enabled by physical layer channel diversity that reduces the effective level of MAC contention. In contrast, fairness degrades and jitter increases significantly at a critical offered load. This duality obviates the need for link layer flow control for throughput improvement. Second, TCP-over-WLAN achieves high throughput commensurate with that of wireline TCP under saturated conditions, challenging the widely held perception that TCP throughput fares poorly over WLANs when subject to heavy contention. We show that TCP-over-WLAN prowess is facilitated by the self-regulating actions of DCF and TCP feedback control that jointly drive the shared channel at an effective load of two to three wireless stations, even when the number of active stations is large. We show that the mitigating influence of TCP extends to unfairness and adverse impact of dynamic rate shifting under multiple access contention. We use experimentation and simulation in a complementary fashion, pointing out performance characteristics where they agree and differ.

Wide-spread deployment of infrastructure WLANs has made Wi-Fi an integral part of today's Internet access technology. Despite its crucial role in affecting end-to-end performance, past research has focused on MAC protocol enhancement, analysis and simulation-based performance evaluation without sufficient consideration for modeling inaccuracies stemming from inter-layer dependencies, including physical layer diversity, that significantly impact performance. We take a fresh look at IEEE 802.11 WLANs, and using a combination of experiment, simulation, and analysis demonstrate its surprisingly agile performance traits. Our main findings are two-fold. First, contention-based MAC throughput degrades gracefully under congested conditions, enabled by physical layer channel diversity that reduces the effective level of MAC contention. In contrast, fairness and jitter significantly degrade at a critical offered load. This duality obviates the need for link layer flow control for throughput improvement but necessitates traffic control for fairness and QoS. Second, TCP-over-WLAN achieves high throughput commensurate with that of wireline TCP under saturated conditions, challenging the widely held perception that TCP throughput fares poorly over WLANs when subject to heavy contention. We show that TCP-over-WLAN prowess is facilitated by the self-regulating actions of DCF and TCP congestion control that jointly drive the shared physical channel at an effective load of 2--3 wireless stations, even when the number of active stations is very large. Our results highlight subtle inter-layer dependencies including the mitigating influence of TCP-over-WLAN on dynamic rate shifting.