Abstract Objectives: 1) create metrics for lifting techniques and transferring mechanisms, 2) calibrate sensors for data collection 3) identify potential injurious posture among home health aides (HHAs) while transferring patients. Participants: 7 HHAs and a physical therapist. Interview and sensor data were collected. Outcome variables included improper lifting techniques and improper body mechanisms. Obesity of HHAs was associated with worse scores of body mechanics (p < 0.0001), while fear of injury with better body mechanics (p < 0.0001). GEE results identified that twisting the spine during transfers (OR = 6.3; 95% CI: 1.09–36.7) and not using a wide support base when lifting from supine to sitting (OR= 6.0, 95% CI: 2.03–17.7) were associated with improper lifting technique and body mechanics. Results show it is viable to use sensor technology to collect HHAs’ data to design intervention for injury prevention. A larger-scale study is needed to validate the results.

Background: Home health aides (HHAs) often suffer injuries as a result of improperly lifting and transferring patients from one location to another. The objective of this pilot study was to find innovative solutions to prevent work-related injuries among this population. Methods: This was a cross-sectional study. Seven HHAs were recruited and given a questionnaire about their work experiences and history of injuries. A trained physical therapist was consulted to determine what improper lifting techniques and body mechanics would lead to work-related injuries. Next, motion sensors were attached to the seven HHAs while they performed patient transfers. The extent to which home health aides followed correct procedures was assessed using the motion capture data. The lifting technique and body mechanics ratings were both analyzed with multivariate linear and logistic regression models while controlling covariates in the model. Results: Obesity was associated with a worse body mechanics score (p < 0.0001), while fear of injury was associated with better body mechanics (p < 0.0001). Generalized estimating equations identified that twisting the spine during transfers (OR = 6.3; 95% CI: 1.09–36.7) and not using a wide support base when lifting from supine to sitting (OR= 6.0, 95% CI: 2.03–17.7) were both associated with improper lifting technique and body mechanics. Conclusions: This study identified two modifiable risk factors (obesity and lacking a fear of injury) and two individual transfer items that are associated with improper transfer techniques and body mechanics. A larger study subjects with multiple sites is underway.

Patients with functional disabilities often require assistance to perform basic everyday activities, such as bathing, dressing, and getting into/out of bed. These activities typically require the direct care worker (DCW) to transfer (lift & move) the patient from one location to another. These patient transfers are a common cause of injury to health care workers. In fact, depending on the job site, on average a staggering 4% of DCWs are injured every year. Following proper lifting and transfer procedures can dramatically reduce the risk of injury. This research demonstrates that data collected from motion tracking systems, combined with computational analysis can detect risky patient transfer behavior. Testing of the system occurred as part of an exploratory study in an assisted living facility. Two common types of transfers were tested: transfers from bed to shower chair, and transfers from shower chair to wheelchair. These scenarios were tested on two types of patients, one that was completely disabled, and one that was partially disabled. Two major results were determined from this study: (1) risky patient transfer behavior is common in the assisted living facility, and (2) this behavior can be adequately detected via wearable motion tracking sensors. The longer term research goal is to extend these preliminary results to construct a fully wearable motion tracking system that can be used as a tool to reinforce proper lifting and transfer protocols to reduce work-related injuries among DCWs.

GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts of spatial and temporal information that traces a moving object's path. The exponential increase in the amount of such trajectory data has caused three major problems. First, transmission of large amounts of data is expensive and time-consuming. Second, queries on large amounts of trajectory data require computationally expensive operations to extract useful patterns and information. Third, GPS trajectories often contain large amounts of redundant data that waste storage and cause increased disk I/O time. These issues can be addressed by algorithms that reduce the size of trajectory data. A key requirement for these algorithms is to minimize the loss of information essential to location-based applications. This paper presents a new compression method called SQUISH-E (Spatial QUalIty Simplification Heuristic - Extended) that provides improved run-time performance and usability. A comprehensive comparison of SQUISH-E with other algorithms is carried out through an empirical study across three types of real-world datasets and a variety of error metrics.

Trajectory compression algorithms enable efficient transmission, storage, and processing of trajectory data by eliminating redundant information. While a large number of compression algorithms have been developed, there is no comprehensive and convenient benchmarking system for evaluating these algorithms. We will demonstrate TrajMetrix, our system that meets the above need. We will show how TrajMetrix can be used to gain insights into the benefits and drawbacks of various compression algorithms given different compression requirements. From the knowledge attained by using TrajMetrix, we developed SQUISH-E (Spatial QUalIty Simplification Heuristic - Extended). This algorithm uses a priority queue to preferentially remove points based on the error introduced by their removal. Through live demonstrations that use both synthetic and real data sets, we will show the ability of SQUISH-E to effectively bound compression error with low computational overhead.

Trajectory compression algorithms eliminate redundant information in the history of a moving object. Such compression enables efficient transmission, storage, and processing of trajectory data. Although a number of compression algorithms have been proposed in the literature, no common benchmarking platform for evaluating their effectiveness exists. This paper presents a benchmarking framework for efficiently, conveniently, and accurately comparing trajectory compression algorithms. This framework supports various compression algorithms and metrics defined in the literature, as well as three synthetic trajectory generators that have different trade-offs. It also has a highly extensible architecture that facilitates the incorporation of new compression algorithms, evaluation metrics, and trajectory data generators. This paper provides a comprehensive overview of trajectory compression algorithms, evaluation metrics and data generators in conjunction with detailed discussions on their unique benefits and relevant application scenarios. Furthermore, this paper describes challenges that arise in the design and implementation of the above framework and our approaches to tackling these challenges. Finally, this paper presents evaluation results that demonstrate the utility of the benchmarking framework.

Digital libraries have become more distributed and more diverse in their collections. It is common for digital libraries to contain information sources that are multimedia in nature. They provide access to, among others, text and image (still and moving) documents as well as audio files. While digital cameras now capture some metadata automatically, user-assigned semantic tags are popular and indispensable. This includes geotagging, a process of tagging either the latitude and longitude coordinates or the place names of the location where an image was shot. Systematic analysis of geotagged images is timely and necessary because the phenomenon of social tagging and its true potential is new and not fully understood, several million geotagged photographs are uploaded to Flickr each month, and tags are frequently criticized because they are imprecise and not well-investigated. To address this, with the help of basic level theory, we undertook an analysis of tags assigned to a sample of geotagged still and moving images on Flickr. Our findings showed that tags assigned to geotagged still and moving images are not statistically significant with respect to their level of abstraction. Implications of our findings for indexing and retrieval of still and moving images are discussed, demonstrating that tags can potentially help solve the indexing problem associated with semantic contents of multimedia documents. They also have the potential to bridge the semantic gap.

GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts spatial and temporal information that traces a moving object's path. The popularity of these devices has led to an exponential increase in the amount of GPS trajectory data generated. The size of this data makes it difficult to transmit it over a mobile network and to analyze it to extract useful patterns. Numerous compression algorithms have been proposed to reduce the size of trajectory data sets; however these methods often lose important information essential to location-based applications such as object's position, time and speed. This paper describes the Spatial QUalIty Simplification Heuristic (SQUISH) method that demonstrates improved performance when compressing up to roughly 10% of the original data size, and preserves speed information at a much higher accuracy under aggressive compression. Performance is evaluated by comparison with three competing trajectory compression algorithms: Uniform Sampling, Douglas-Peucker and Dead Reckoning.

Un procede de reduction des emissions d'une pluralite de biens mobiles consiste a recevoir des donnees de modele de voyage correspondant a des positions et des temps des biens mobiles et les donnees de preference d'une pluralite d'utilisateurs. Une base de donnees de voyages effectues par les biens mobiles est ensuite generee sur la base de donnees de modele de voyage et des opportunites de consolidation de voyage pour les biens mobiles sont identifiees sur la base de la base de donnees generee. Le procede comprend egalement le classement des opportunites de consolidation de voyage sur la base des donnees de preference et l'utilisation des opportunites de consolidation de voyage classees pour fournir des recommandations d'expedition concues pour reduire la consommation de carburant des biens mobiles.

The massive volumes of trajectory data generated by inexpensive GPS devices have led to difficulties in processing, querying, transmitting and storing such data. To overcome these difficulties, a number of algorithms for compressing trajectory data have been proposed. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. We present results from a comprehensive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Algorithm, STTrace Algorithm and Opening Window Algorithms. Our empirical study uses different types of real-world data such as pedestrian, vehicle and multimodal trajectories. The algorithms are compared using several criteria including execution times and the errors caused by compressing spatio-temporal information, across numerous real-world datasets and various error metrics.

In commercial transportation operations, one of the largest wasteful expenditures is the movement of tractor trailers with little or no cargo. Analysis of interfleet data shows many lost opportunities for identifying backhauling loads-cargo that could have been moved by an otherwise empty trailer on its return from a delivery point to its home base. Brokerage systems that facilitate matching of load-sharing and backhaul opportunities currently do not incorporate monitoring of real-time, geo-based information, analysis of historical geo-based information, and user-calibrated preferences from all brokerage participants. Future intelligent brokerage systems will need to provide a full range of services including supply, chain visibility and automated identification of potential collaborations based on historical trends. In this paper an algorithm is described for identifying load-sharing and backhaul opportunities based on the detection of patterns in large-scale, event-based telematics network data.

The distribution and management of spatial data require strategies for handling large amount of terrain data that are now available. Especially, data like LIDAR and Digital Elevation Model (DEM) which have been used in a large group of diversified users. In this paper, we propose a progressive terrain data transmission scheme based on the Over-determined Laplacian PDE (ODETLAP) which can achieve a compromise of high compression ratio and accuracy. The ODETLAP can be thought of as a compressor of original terrain data and using run length encoding as well as linear prediction we can reach a higher compression ratio. In general, this technique is capable of reducing a hilly DEM dataset to 1% of its original binary size and a mountainous, to 3%. The accuracy loss in elevation and slope are also discussed.

We present a new data structure for simplifing terrain that captures hydrology significant features using a constrained Delaunay triangulation. This triangulation preserves the hydology by using irregularsized, non-overlapping planes to model regions that flow in a uniform direction. Edges are associated with drainage and ridge networks that incorporate physically-based structure into the model without significant overhead. This allows better compression ratios the standard Triangulated Irregular Networks with highier hydrology accuracy. Standard error metrics such as root mean squared (RMS) and maximum error fail to capture whether a reconstructed terrain accurately captures the hydrology. A hydrology error metric is used to verifiy our results based on the potential energy required for the reconstructed drainage to flow on the original terrain. The results are then compared to other triangulationbased GIS compression methods.

We report on variants of the ODETLAP lossy terrain compression method where the reconstructed terrain has accurate slope as well as elevation. Slope is important for applications such as mobility, visibility and hydrology. One variant involves selecting a regular grid of points instead of selecting the most important points, requiring more points but which take less space. Another variant adds a new type of equation to the overdetermined system to force the slope of the reconstructed surface to be close to the original surface’s slope. Tests on six datasets with elevation ranges from 505m to 1040m, compressed at ratios from 146:1 to 1046:1 relative to the original binary file size, showed RMS elevation errors of 10m and slope errors of 3 to 10 degrees. The reconstructed terrain also supports planning optimal paths that avoid observers’ viewsheds. Paths planned on the reconstructed terrain were only 5% to 20% more expensive than paths planned on the original terrain. Tradeoffs between compressed data size and output accuracy are possible. Therefore storing terrain data on portable devices or transmitting over slow links and then using it in applications is more feasible.

We present a new form of terrain compression that preserves the hydrological information that is lost using standard terrain simplification techniques. Typically, terrain compression algorithms such as greedy ODETLAP and JPEG2000 seek to minimize RMS and maximum error. These metrics fail to capture whether a reconstructed terrain preserves the drainage network. A quantitative measurement of how accurately a drainage network captures the hydrology is very important for determining the effectiveness of a terrain simplification technique. Past work has been done for defining a metric for comparing how well a computed drainage compares to the real world drainage [13]. Most of the time, real world flow measurements are unavailable and flow simulations have to be used to make predictions. Having a measurement for testing and comparing different models has the potential to be widely used in numerous applications (floods, erosion, pollutants, etc). In this paper, we first describe a method for obtaining high terrain compression ratios while preserving the main structure of the drainage network. Then, the effectiveness of our approach is verified by defining a metric that maps the reconstructed drainage network onto the original terrain and computes the amount of energy required for the water to flow.