Evaluation of electromechanical impedance based structural health monitoring for detection of loosening in total knee arthroplasty

Total Knee Arthroplasty (TKA) continues to be a common and important orthopedic procedure for many in the United States. Despite recent medical advancements and increasing knowledge in the orthopedic community, it has been determined that 20% of TKA patients are still dissatisfied with their knee replacements. Causes of this failure include septic loosening and wear on the bearing component of the implant. Another cause of failure that has received specific attention from the mechanical community is aseptic loosening, which has been attributed to unbalanced ligaments or misalignment of the implant components. Previous efforts have been made to detect loosening by using passive force sensors such as piezoelectric transducers or strain gauges to detect misalignment. An alternative to this is to perform active sensing or structural health monitoring to evaluate possible loosening before it becomes a critical concern to the patient. One method of structural health monitoring, called the electromechanical impedance (EMI) method, is particularly attractive as it can use a single, compact piezoelectric transducer to determine the state of the host structure. This work is intended to evaluate the ability of the EMI method in sensing loosening between the cement and bone of a TKA tibial tray. This work will utilize real tibial trays implanted into synthetic bone (Sawbone) to evaluate the feasibility of detecting loosening using the EMI method. The intention of this work is to serve as a foundation for further in-vivo and intraoperative studies

Design and Evaluation of an In-Pipe Leak Detection Sensing Technique Based on Force Transduction

Leakage is the major factor for unaccounted fluid losses in almost every pipe network. In most cases the deleterious effects associated with the occurrence of leaks may present serious economical and health problems and therefore, leaks must be quickly detected, located and repaired. The problem of leakage becomes even more serious when it is concerned with the vital supply of fresh water to the community. Leaking water pipelines can develop large health threats to people mostly because of the infiltration of contaminants into the water network. Such possibilities of environmental health disasters have spurred research into the development of methods for pipeline leakage detection. Most state of the art leak detection techniques have limited applicability, while some of them are not reliable enough and sometimes depend on user experience. Our goal in this work is to design and develop a reliable leak detection sensing system. The proposed technology utilizes the highly localized pressure gradient in the vicinity of a small opening due to leakage in a pressurized pipeline. In this paper we study this local phenomenon in detail and try to understand it with the help of numerical simulations in leaking pipelines (CFD studies). Finally a new system for leak detection is presented. The proposed system is designed in order to reduce the number of sensing elements required for detection. The main concept and detailed design are laid out. A prototype is fabricated and presented as a proof of concept. The prototype is tested in a simple experimental setup with artificial leakages for experimental evaluation. The sensing technique discussed in this work can be deployed in water, oil and gas pipelines without significant changes in the design, since the concepts remain the same in all cases.King Fahd University of Petroleum and Minerals (Project Number R7-DMN-08

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena

Potential applications of geospatial information systems for planning and managing aged care services in Australia

[Abstract]: This paper discusses the potential applications of Geospatial Information Technology (GITs) to assist in planning and managing aged care programs in Australia. Aged care is complex due to the numbers of participants at all levels of including planning of services, investing in capacity, funding, providing services, auditing, monitoring quality, and in accessing and using facilities and services. There is a vast array of data spread across the entities that are joined to aged care. The decision-making process for investment in capacity and service provision might be aided by technology including GIT. This is also expected to assist in managing and analysing the vast amount of demographic, geographic, socio-economic and behavioral data that might indicate current and future demand for services the aged and frail-aged population. Mapping spatio-temporal changes in near real time can assist in the successful planning and management of aged care programs. Accurate information on the location of aged care services centres and mapping the special needs of clients and their service needs may assist in monitoring access to services and assist in identifying areas where there are logistic challenges for accessing services to meet needs. GIT can also identifying migrations of aged people and of the cohorts of the population who are likely to be the next wave of clients for aged care services. GITs include remote sensing, geographic information systems (GIS) and global positioning systems (GPS) technologies, which can be used to develop a user friendly digital system for monitoring, evaluating and planning aged care and community care in Australia. Whilst remote sensing data can provide current spatiotemporal inventory of features such as locations of carer services, infrastructure, on a consistent and continuous coordinate system, a GIS can assist in storing, cross analysing, modeling and mapping of spatial data pertaining to the needs of the older people. GITs can assist in the development of a single one-stop digital database which will prove a better model for managing aged care in Australia. GIT will also be a component of technologies such as activity monitors to provide tracking functionality. This will assist in tracking dementia sufferers who may be prone to wandering and be exposed to risk

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Development of a Network of Accurate Ozone Sensing Nodes for Parallel Monitoring in a Site Relocation Study

Recent technological advances in both air sensing technology and Internet of Things (IoT) connectivity have enabled the development and deployment of remote monitoring networks of air quality sensors. The compact size and low power requirements of both sensors and IoT data loggers allow for the development of remote sensing nodes with power and connectivity versatility. With these technological advancements, sensor networks can be developed and deployed for various ambient air monitoring applications. This paper describes the development and deployment of a monitoring network of accurate ozone (O3) sensor nodes to provide parallel monitoring in an air monitoring site relocation study. The reference O3 analyzer at the station along with a network of three O3 sensing nodes was used to evaluate the spatial and temporal variability of O3 across four Southern California communities in the San Bernardino Mountains which are currently represented by a single reference station in Crestline, CA. The motivation for developing and deploying the sensor network in the region was that the single reference station potentially needed to be relocated due to uncertainty that the lease agreement would be renewed. With the implication of siting a new reference station that is also a high O3 site, the project required the development of an accurate and precise sensing node for establishing a parallel monitoring network at potential relocation sites. The deployment methodology included a pre-deployment co-location calibration to the reference analyzer at the air monitoring station with post-deployment co-location results indicating a mean absolute error (MAE) < 2 ppb for 1-h mean O3 concentrations. Ordinary least squares regression statistics between reference and sensor nodes during post-deployment co-location testing indicate that the nodes are accurate and highly correlated to reference instrumentation with R2 values > 0.98, slope offsets < 0.02, and intercept offsets < 0.6 for hourly O3 concentrations with a mean concentration value of 39.7 ± 16.5 ppb and a maximum 1-h value of 94 ppb. Spatial variability for diurnal O3 trends was found between locations within 5 km of each other with spatial variability between sites more pronounced during nighttime hours. The parallel monitoring was successful in providing the data to develop a relocation strategy with only one relocation site providing a 95% confidence that concentrations would be higher there than at the current site

Remote sensing utility in a disaster struck urban environment

Six major public health areas which might be affected by a natural disaster were identified. The functions and tasks associated with each area following a disaster, potential ways remote sensing could aid these functions, and the baseline data which would expedite problem solving associated with these functions are discussed

New Hampshire University Research and Industry Plan: A Roadmap for Collaboration and Innovation

This University Research and Industry plan for New Hampshire is focused on accelerating innovation-led development in the state by partnering academia’s strengths with the state’s substantial base of existing and emerging advanced industries. These advanced industries are defined by their deep investment and connections to research and development and the high-quality jobs they generate across production, new product development and administrative positions involving skills in science, technology, engineering and math (STEM)