Architecture and applications of the FingerMouse: a smart stereo camera for wearable computing HCI

In this paper we present a visual input HCI system for wearable computers, the FingerMouse. It is a fully integrated stereo camera and vision processing system, with a specifically designed ASIC performing stereo block matching at 5 Mpixel/s (e.g. QVGA 320 × 240 at 30 fps) and a disparity range of 47, consuming 187 mW (78 mW in the ASIC). It is button-sized (43 mm × 18 mm) and can be worn on the body, capturing the user’s hand and processing in real-time its coordinates as well as a 1-bit image of the hand segmented from the background. Alternatively, the system serves as a smart depth camera, delivering foreground segmentation and tracking, depth maps and standard images, with a processing latency smaller than 1 ms. This paper describes the FingerMouse functionality and its applications, and how the specific architecture outperforms other systems in size, latency and power consumption.     

Issues in the design and use of wearable computers

Wearable computers are fully functional, self-powered, self-contained computers that allow the user to access information anywhere and at any time. In this paper, design issues for wearable computers are discussed, including power considerations, use of input devices, image registration, and the use of wearable computers for the design of smart spaces. Application areas for wearable computers are presented, including medicine, manufacturing, maintenance, and as personal assistants. Finally, future research directions for wearable computers are indicated.     

Facial configuration and BMI based personalized face and upper body modeling for customer-oriented wearable product design

To realize truly customer-oriented wearable products, individual users’ unique characteristics and features should be properly captured and represented. This research focuses on an efficient methodology to generate low polygonal virtual human face models, which overcome the limitation of existing high polygonal models. To determine individuals’ characteristics in the conceptual design stage of wearable products, a computerized and personalized 3D face model should be efficiently generated and be able to interact with wearable products. This research formulates a computerized 3D face via a 3D feature-based transformation. The developed algorithm is able to concisely and efficiently create a 3D face by using frontal and lateral pictures of users. The performance of this algorithm is well adapted both to typical PCs and to mobile devices. The generated virtual face models can serve as communication media in a multi-device based collaborative design environment. Through experiments, the validity of the proposed modeling method is considerably acceptable with respect to the quality of the similarity between 3D faces and individual pictures. Finally, this paper discusses how the developed personalized face modeling can be successfully utilized for customer-oriented wearable product design by showing compatible matching of a hairstyle product as a user study.     

Machine learning-based identification and classification of physical fatigue levels: A novel method based on a wearable insole device

Construction is known for being a labor-intensive and risky industry. Within various occupational settings such as construction, physical fatigue is an underlying health condition that may lead to musculoskeletal disorders and fall-related injuries. Identifying a worker's physical fatigue could enable safety managers to mitigate fatigue-related injuries and improve workplace operations. However, current physical fatigue assessment and identification methods include subjective, physiological, biomechanical, and computer vision approaches, which may be unreliable, intrusive, and require extensive post-processing, thus, rendering them impractical for continuous monitoring of workers' movements and automated identification of physical fatigue. Given the above, this study aims to utilize a wearable insole device to identify and classify physical fatigue levels in construction workers. Ten asymptomatic subjects were recruited to perform a fatiguing manual rebar tying activity in a laboratory setting. Borg's rating of perceived exertion (RPE) was applied as a subjective measure for collecting the levels of physical fatigue of each subject. Three sub-classification problems for identifying physical fatigue levels (i.e., PFL1, PFL2, and PFL3) were assessed. Numerous features were evaluated from the collected data samples after data segmentation. The classification performance of supervised machine learning algorithms was evaluated at a sliding window of 2.56 s. Our results from 10-fold cross-validation show an accuracy of 86% for the Random Forest (RF) algorithm, indicating the best performance among other algorithms. In addition, precision, recall, specificity, and F1-score metrics of the RF algorithm were between 52.63% and 82.62%, 52.63%–84.32%, 89.60%–92.33%, and 52.63%–83.46%, respectively. These results indicate that data samples such as acceleration and plantar pressure acquired from a wearable insole device are reliable for identifying and classifying physical fatigue levels in construction workers. In summary, this study would contribute to providing a proactive physical fatigue assessment method and guidelines for early identification of physical fatigue in construction.     

Improving the controllability of tilt interaction for mobile map-based applications

This article proposes enhanced techniques for improving the controllability of tilt interaction. Tilt interaction offers an intuitive, one-handed form of interaction for mobile applications. Tilt interaction is particularly well-suited to mobile map-based applications, where traditional mobile interaction techniques suffer from several shortcomings. Current implementations of tilt interaction, however, suffer from several shortcomings. Existing zooming techniques used in conjunction with tilt interaction are often difficult to control. Tilt-controlled panning can also be difficult to control in a mobile context of use, where the user's walking motion affects the accuracy of tilt interaction. The use of rate-controlled tilt interaction to perform zooming is proposed. Two different approaches to sensitivity adaptation are investigated, where the sensitivity of tilt-controlled panning is automatically adjusted to compensate for the user's current context of use. A usability study was conducted with thirty participants to evaluate the proposed techniques. The results of the usability study showed that tilt zooming offered better efficiency, user satisfaction and perceived workload than gesture zooming. The use of a static dampening factor to compensate for walking motion was found to provide actual and perceived controllability improvements.     

Improving the energy efficiency of data-intensive applications running on clusters

Abstract

As an alternative to traditional computing architecture, cloud computing now is rapidly growing. However, it is based on models like cluster computing in general. Now supercomputers are getting more and more powerful, helping scientists have more indepth understanding of the world. At the same time, clusters of commodity servers have been mainstream in the IT industry, powering not only large Internet services but also a growing number of data-intensive scientific applications, such as MPI based deep learning applications. In order to reduce the energy cost, more and more efforts are made to improve the energy consumption of HPC systems. Because I/O accesses account for a large portion of the execution time for data intensive applications, it is critical to design energy-aware parallel I/O functions for addressing challenges related to HPC energy efficiency. As the de facto standard for designing parallel applications in cluster environment, the Message Passing Interface has been widely used in high performance computing, therefore, getting the energy consumption information of MPI applications is critical for improving the energy efficiency of HPC systems. In this work we first present our energy measurement tool, a software framework that eases the energy collection in cluster environment. And then we present an approach which can optimise the parallel I/O operation’s energy efficiency. The energy scheduling algorithm is evaluated in a cluster.     

Applying a fusion of wearable sensors and a cognitive inspired architecture to real-time ergonomics analysis of manual assembly tasks

High value manufacturing systems still require ergonomically intensive manual activities. Examples include the aerospace industry where the fitting of pipes and wiring into confined spaces in aircraft wings is still a manual operation. In these environments, workers are subjected to ergonomically awkward forces and postures for long periods of time. This leads to musculoskeletal injuries that severely limit the output of a shopfloor leading to loss of productivity. The use of tools such as wearable sensors could provide a way to track the ergonomics of workers in real time. However, an information processing architecture is required in order to ensure that data is processed in real time and in a manner that meaningful action points are retrieved for use by workers.In this work, based on the Adaptive Control of Thought—Rational (ACT-R) cognitive framework, we propose a Cognitive Architecture for Wearable Sensors (CAWES); a wearable sensor system and cognitive architecture that is capable of taking data streams from multiple wearable sensors on a worker’s body and fusing them to enable digitisation, tracking and analysis of human ergonomics in real time on a shopfloor. Furthermore, through tactile feedback, the architecture is able to inform workers in real time when ergonomics rules are broken. The architecture is validated through the use of an aerospace case study undertaken in laboratory conditions. The results from the validation are encouraging and in the future, further tests will be performed in an actual working environment.     

Improving robustness of P2P applications in mobile environments

Mobile P2P networks possess particular characteristics which make accessibility of services deployed on peers a challenge. This has to be taken into account when considering robustness of applications that depend on successfully accessing a set of services. While ensuring robustness is traditionally handled through replication or redundancy, those solutions are not readily applicable to decentralized and dynamic networks. Instead, current solutions are based on efficient P2P structure maintenance or unstructured network search algorithms. A novel and alternative method proposed in this paper is based on the observation that some redundancy may exist between services offered on the network, a fact which could be used to recreate an unavailable service from services accessible to a peer. Instead of adding redundancy to the system, our solution exploits the already existing redundancy to improve robustness of mobile P2P applications. We model the interaction with services as finite-state transducers and propose a heuristic to obtain redundancy between any pair of services. Then, a set of algorithms that uses this inter-service redundancy to recreate the interaction with one service from the other is discussed. The computational cost is polynomial with respect to services’ size, and in practice, the non-redundant functionality and related control need to be implemented locally.

The role of wearable devices in meeting the needs of cloud manufacturing: A case study

Cloud manufacturing is a service-oriented, customer-centric and demand-driven process with well-established industrial automation. Even though, it does not necessarily mean the absence of human beings. Due to products and their corresponding manufacturing processes becoming increasingly complex, operators' daily working lives are also becoming more difficult. Enhanced human–machine interaction is one of the core areas for the success of the next generation of manufacturing. However, the current research only focuses on the automation and flexibility features of cloud manufacturing, the interaction between human and machine and the value co-creation among operators is missing. Therefore, a new method is needed for operators to support their work, with the objective of reducing the time and cost of machine control and maintenance. This paper describes a practical demonstration that uses the technologies of the Internet of things (IoT), wearable technologies, augmented reality, and cloud storage to support operators' activities and communication in discrete factories. This case study exhibits the capabilities and user experience of smart glasses in a cloud manufacturing environment, and shows that smart glasses help users stay productive and engaged.     

Fall risk assessment of construction workers based on biomechanical gait stability parameters using wearable insole pressure system

Falls on the same level are a leading cause of non-fatal injuries in the construction industry, and loss of balance events are the primarily contributory risk factors associated with workers’ fall injuries. Previous studies have indicated that changes in biomechanical gait stability parameters provide substantial safety gait metrics for assessing workers’ fall risks. However, scant research has been conducted on changes in biomechanical gait stability parameters based on foot plantar pressure patterns to assess workers’ fall risks. This research examined the changes in spatial foot regions and loss of balance events associated with biomechanical gait stability parameters based on foot plantar pressure patterns measured by wearable insole pressure system. To test the hypotheses of this study, ten asymptomatic participants conducted laboratory simulated loss of balance events which are often initiated by extrinsic fall risk factors. Our results found: (1) statistically significant differences in biomechanical gait stability parameters between spatial foot regions, especially with the peak pressure parameter; and (2) statistically significant differences in biomechanical gait stability parameters between loss of balance events when compared to normal gait (baseline), especially with the pressure-time integral parameter. Overall, the findings of this study not only provide useful safety gait metrics for early detection of specific spatial foot regions but also allow safety managers to understand the mechanism of loss of balance events in order to implement proactive fall-prevention strategies.     

Performances study of compact printed diversity antenna in the presence of user's body for LTE mobile phone applications

In this article, three kinds of user effect are considered, namely, “specific anthropomorphic mannequin (SAM) head and personal digital assistants (PDA) hand (talk mode),” “PDA hand (data mode),” and “Dual hands (read mode)” for the performance study of the compact LTE MIMO antenna. This is in continuation of the previously published work which has been investigated in the free space. However, the designed antenna covers LTE700 (0.747‐0.787 GHz) and WWAN (1.7‐3.04 GHz) based on ?6 dB reflection coefficient. Further, the effect of users' body is investigated by placing the planar monopole antenna (PMA) on the top and the bottom portion of the mobile printed circuit board (PCB). The performances like S‐parameters, radiation performances, diversity performances, and channel capacity loss are studied and optimal location of PMA is assigned either top or bottom portion of mobile phone PCB based on the results obtained from the respective positions. Finally, the specific absorption rate is estimated and found well below the standard limit of FCC that is, less than 1.6 W/kg over 1 g tissue and European that is, 2 W/kg over 10 g tissue. In present work, it is observed that top positioned PMA provides optimal performances in the user proximity over PCB.     

Hybrid and Adaptive Cryptographic-based secure authentication approach in IoT based applications using hybrid encryption

The Internet of Things (IoT) in communication networks has gained major interest in recent decades. Accordingly, secure authentication of such individuals results in a major challenge due to the weakness in the authentication process. Hence, an effective Hybrid and Adaptive Cryptographic (HAC)-based secure authentication framework is designed in this research to perform an authentication process in IoT. The proposed approach uses cryptographic operations, like exclusive-or (Ex-or) operation, a hashing function, and hybrid encryption to accomplish the authentication process. However, the hybrid encryption function is carried out in two different ways: one depends on Advanced Encryption Standard (AES) as well as Elliptic Curve Cryptography (ECC), while other is based on Rivest Shamir Adleman (RSA) and AES. With a hybrid encryption function, security flaws can be effectively dealt through the cryptographic system. Moreover, the proposed approach provides high robustness with low complexity. The proposed HAC-based secure authentication approach obtained a minimum communication cost of 0.017sec, less computation time of 0.060sec, and minimum memory usage of 2.502MB, respectively.     

A duality based framework for integrating reliability and precision for sensor network design

In sensor network design literature, requirements such as maximization of the network reliability [Y. Ali, S. Narasimhan, Sensor network design for maximizing reliability of linear processes, AIChE J. 39 (1993) 820–828; Y. Ali, S. Narasimhan, Redundant sensor network design for linear processes, AIChE J. 41 (1995) 2237–2249] and minimization of cost subject to precision constraints [M. Bagajewicz, Design and retrofit of sensor networks in process plants, AIChE J. 43 (1997) 2300–2306; M. Bagajewicz, E. Cabrera, New MILP formulation for instrumentation network design and upgrade, AIChE J. 48 (2002) 2271–2282] have been proposed as a criteria for optimally locating sensors. In this article, we show that the problems of maximizing reliability and maximizing precision (or minimizing variance) for linear processes are dual of each other. To achieve this duality, we propose transformations which can be used to convert sensor failure probabilities into equivalent sensor variances and vice versa. Thus, the duality enables working in a single framework with specified criteria on reliability as well as precision. As an application of this duality, we propose two formulations for the sensor network design problem viz., maximization of the network reliability subject to precision constraints and minimization of the network variance subject to reliability constraints. We also show the utility of these formulations to determine the pareto-front for the combinatorial sensor network design problem. Hydrodealkylation and steam-metering case studies are used to illustrate the proposed ideas.     

Factors influencing the perceived usability of wearable chair exoskeleton with market segmentation: A structural equation modeling and K-Means Clustering approach

The sudden implementation of work-from-home setup has caused the rise of sedentary position among employees which led to an increase in development or worsening of musculoskeletal disorders. To help mitigate this problem, wearable assistive devices such as wearable chairs have been argued to be beneficial. Several studies have expounded on the benefit of using this, however little to no available device is yet present in the Philippines. To which, businesses and developers could capitalize on this aspect. This study examined the perceived usability of wearable chair exoskeleton in the Philippines by integrating the Unified Theory of Acceptance and Use of Technology (UTAUT2) and System Usability Scale (SUS). A questionnaire was developed and distributed to 365 residents in the Philippines to measure the factors and their relationship to perceived usability accurately. Indicators and measures used in the questionnaire were derived from existing literature on technology acceptance and usability evaluation. Partial least squares structural equation modeling (PLS-SEM) was used for this study. Results revealed that perceived usability was significantly influenced by usage behavior. At the same time, habit, hedonic motivation, and performance expectancy significantly influenced the behavioral intention of the wearable chair exoskeleton. K-Means clustering was also utilized to identify clusters of target users of the wearable chair in the country, such as high-value customers, core customers, and low-value customers. Results indicated that demographic factors such as females, 30 years old and below, earning Php45,000 and above monthly, and living in a city are highly likely to utilize the product. The findings of this study can be applied and extended as a theoretical framework for future researchers of consumer behavior and exoskeleton developers for enhancing the innovation and usability of this new technology. This study may also be used and capitalized by investors to strategize the development and marketing of the exoskeletons among industrial and teleworkers worldwide.     

Improving the efficiency of functional verification based on test prioritization

Functional verification has become the key bottleneck that delays time-to-market during the embedded system design process. And simulation-based verification is the mainstream practice in functional verification due to its flexibility and scalability. In practice, the success of the simulation-based verification highly depends on the quality of functional tests in use which is usually evaluated by coverage metrics. Since test prioritization can provide a way to simulate the more important tests which can improve the coverage metrics evidently earlier, we propose a test prioritization approach based on the clustering algorithm to obtain a high coverage level earlier in the simulation process. The k-means algorithm, which is one of the most popular clustering algorithms and usually used for the test prioritization, has some shortcomings which have an effect on the effectiveness of test prioritization. Thus we propose three enhanced k-means algorithms to overcome these shortcomings and improve the effectiveness of the test prioritization. Then the functional tests in the simulation environment can be ordered with the test prioritization based on the enhanced k-means algorithms. Finally, the more important tests, which can improve the coverage metrics evidently, can be selected and simulated early within the limited simulation time. Experimental results show that the enhanced k-means algorithms are more accurate and efficient than the standard k-means algorithm for the test prioritization, especially the third enhanced k-means algorithm. In comparison with simulating all the tests randomly, the more important tests, which are selected with the test prioritization based on the third enhanced k-means algorithm, achieve almost the same coverage metrics in a shorter time, which achieves a 90% simulation time saving.     

Improving reliability and performances in large scale distributed applications with erasure codes and replication

Replication of Data Blocks is one of the main technologies on which Storage Systems in Cloud Computing and Big Data Applications are based. With the heterogeneity of nodes, and an always-changing topology, keeping the reliability of the data contained in the common large-scale distributed file system is an important research challenge. Common approaches are based either on replication of data or erasure codes. The former stores each data block several times in different nodes of the considered infrastructures: the drawback is that this can lead to large overhead and non-optimal resources utilization. Erasure coding instead exploits Maximum Distance Separable codes that minimize the information required to restore blocks in case of node failure: this approach can lead to increased complexity and transfer time due to the fact that several blocks, coming from different sources, are required to reconstruct lost information. In this paper we study, by means of discrete event simulation, the performances that can be obtained by combining both techniques, with the goal of minimizing the overhead and increasing the reliability while keeping the performances. The analysis proves that a careful balance between the application of replication and erasure codes significantly improves reliability and performances avoiding large overheads with respect to the isolated use of replication and redundancy.     

Human Pacman: a mobile,wide-area entertainment system based on physical,social, and ubiquitous computing

Human Pacman is a novel interactive entertainment system that ventures to embed the natural physical world seamlessly with a fantasy virtual playground by capitalizing on mobile computing, wireless LAN, ubiquitous computing, and motion-tracking technologies. Our human Pacman research is a physical role-playing augmented-reality computer fantasy together with real human–social and mobile gaming. It emphasizes collaboration and competition between players in a wide outdoor physical area which allows natural wide-area human–physical movements. Pacmen and Ghosts are now real human players in the real world, experiencing mixed computer graphics fantasy–reality provided by using the wearable computers. Virtual cookies and actual tangible physical objects are incorporated into the game play to provide novel experiences of seamless transitions between real and virtual worlds. We believe human Pacman is pioneering a new form of gaming that anchors on physicality, mobility, social interaction, and ubiquitous computing.

Annotation based personalized adaptation and presentation of videos for mobile applications

Personalized multimedia content which suits user preferences and the usage environment, and as a result improves the user experience, gains more importance. In this paper, we describe an architecture for personalized video adaptation and presentation for mobile applications which is guided by automatically generated annotations. By including this annotation information, more intelligent adaptation techniques can be realized which primarily reduce the quality of unimportant regions in case a bit rate reduction is necessary. Furthermore, a presentation layer is added to enable advanced multimedia viewers to adequately present the interesting parts of a video in case the user wants to zoom in. This architecture is the result of collaborative research done in the EU FP6 IST INTERMEDIA project.     

无线网络测试系统的设计与实现

4G通信技术是是集3G与WLAN于一体并能够传输高质量视频图像以及图像传输质量与高清晰度电视不相上下的技术产品。LTE作为4G通信的标准技术,提供了极高的数据传输率,同时也极大地改进了核心网性能。同时,LTE小区具有极大的网络覆盖范围,网络结构简洁,并能与IP网很好地融合,具有广泛的应用前景。LTE对于未来高速铁路信息化建设极其重要。本文设计实现的无线网络测试系统将无线通信测量数据与地理信息数据相结合,为铁路无线通信网络的规划与部署提供参考。