Analysis Of Acceleration Data Research Articles

The new design of cows' behavior classifier based on acceleration data and proposed feature set.

Monitor and classify behavioral activities in cows is a helpful support solution for livestock based on the analysis of data from sensors attached to the animal. Accelerometers are particularly suited for monitoring cow behaviors due to small size, lightweight and high accuracy. Nevertheless, the interpretation of the data collected by such sensors when characterizing the type of behaviors still brings major challenges to developers, related to activity complexity (i.e., certain behaviors contain similar gestures). This paper presents a new design of cows' behavior classifier based on acceleration data and proposed feature set. Analysis of cow acceleration data is used to extract features for classification using machine learning algorithms. We found that with 5 features (mean, standard deviation, root mean square, median, range) and 16-second window of data (1 sample/second), classification of seven cow behaviors (including feeding, lying, standing, lying down, standing up, normal walking, active walking) achieved the overall highest performance. We validated the results with acceleration data from a public source. Performance of our proposed classifier was evaluated and compared to existing ones in terms of the sensitivity, the accuracy, the positive predictive value, and the negative predictive value.

Integration of biosafety surveillance through Biosafety Surveillance Conceptual Data Model

The global movement of people and goods has increased the risk of biosecurity threats and their potential to induce large economic, social, and environmental harm. Integration of biosafety monitoring networks has become a top priority for addressing biosafety issues. In order to resolve the data standards and integration problems in the field of biosafety in China, the Biosafety Surveillance Conceptual Data Model (BSCDM), which is an object-oriented, hierarchically designed, flexible and scalable biosafety surveillance concept data model, is proposed in this article. This model is based on the integration of business process management and data resources of disease surveillance, animal disease surveillance and potential invasive biological monitoring. In reference to the Public Health Conceptual Data Model (PHCDM) and Federal Enterprise Architecture (FEA), BSCDM conducts a thorough analysis of biosafety monitoring activities, records basic information requirements of biosafety monitoring and provides data set standards for biosafety-related activities. It is developed with the Unified Modeling Language (UML) and could be applied as an open standard for accelerating data analysis and promoting collaboration. This study attempts to integrate biosafety monitoring data and the presented model has been tested in the detection of dengue fever in China and will be applied to other biosafety fields.

Registration and Analysis of Acceleration Data to Recognize Physical Activity.

The purpose of the article is to check whether the acceleration signals recorded by a smartphone help identify a user's physical activity type. The experiments were performed using the application installed in a smartphone, which was located on the hip of a subject. Acceleration signals were recorded for five types of physical activities (running, standing, going up the stairs, going down the stairs, and walking) for four users. The statistical parameters of the signal were used to extract features from the acceleration signal. In order to classify the type of activity, the quadratic discriminant analysis (QDA) was used. The accuracy of the user-independent classification for five types of activities was 83%. The accuracy of the user-dependent classification was in the range from 90% to 95%. The presented results indicate that the acceleration signal recorded by the device placed on the hip of a user allows us to effectively distinguish among several types of physical activity.

High-statistics and GPU Accelerated Data Analysis

We present methods to perform high statistics data analyses to investigate fundamental neutrino properties in large volume neutrino detectors, fast and with modest computational resources. The introduced measures are threefold: speeding up computations using graphics processors, evaluating the underlying physics processes on a grid instead of treating every event individually and lastly applying smoothing methods to quantities obtained from Monte Carlo simulations. We show that with our method we can get reliable analysis results using significantly less simulation than what is usually needed, and that the timing to run an analysis with our method is independent of sample size.

RPPAware: A software suite to preprocess, analyze and visualize reverse phase protein array data.

Reverse Phase Protein Arrays (RPPA) is a high-throughput technology used to profile levels of protein expression. Handling the large datasets generated by RPPA can be facilitated by appropriate software tools. Here, we describe RPPAware, a free and intuitive software suite that was developed specifically for analysis and visualization of RPPA data. RPPAware is a portable tool that requires no installation and was built using Java. Many modules of the tool invoke R to utilize the statistical features. To demonstrate the utility of RPPAware, data generated from screening brain regions of a mouse model of Down syndrome with 62 antibodies were used as a case study. The ease of use and efficiency of RPPAware can accelerate data analysis to facilitate biological discovery. RPPAware 1.0 is freely available under GNU General Public License from the project website at http://downsyndrome.ucdenver.edu/iddrc/rppaware/home.htm along with a full documentation of the tool.

Fast predictive technique for reverse power detection in synchronous generator

Reverse power relays are utilised to trip turbine generators to avoid prime mover damage and directional relay is most widely used as the main protection for these conditions. An intentional time delay is ordinarily utilised to overcome the possible maloperation of these relays. However, the intentional time delay to prevent maloperation is not an ideal solution. As this time delay increases the reverse power relay operation time, which means that the motoring action of the synchronous generator persist for a longer time, making the prime mover more vulnerable to active power drawn by the generator. This study proposes a new flux-based approach to detect reverse power condition in the synchronous generators. The proposed scheme uses the analysis of angular velocity and acceleration data that are calculated from the estimated magnetic flux at the machine stator terminals. The basic idea of the technique stems from the principle that the stator and rotor magnetic fluxes rotate together at synchronous speed and will not be affected by system disturbances for a short interval according to highly inductive characteristics of the synchronous machine. The main advantage of this predictive algorithm is its speed, security and sensitivity to detect the reverse power conditions.

Time-frequency analysis of acceleration data from ship-ice interaction events

During operations with ships in ice-infested waters, vibrations of the hull occur due to ship-ice interaction. The acting ice load and the induced vibrations depend on the ice regime and ice breaking mechanism. Especially crushing of ice against the hull causes high loads against the vessel. The objective of this study is to analyse the frequency components of ice-induced vibrations, measured with accelerometers placed in the bow section of the ship's hull. The Wigner-Ville distribution, which provides a time-frequency representation, is applied on acceleration data collected on the icebreaker Frej during transit in ice-infested Arctic waters. The resulting time-frequency representations show that the excited frequencies depend on the dominant ice breaking mechanism, on the encounter velocity, and on the position of where the ice interaction against the hull occurs. Furthermore, the natural frequencies of the ship's hull change slightly, depending on the conditions on the sea-ice around the vessel. It is concluded that a system of distributed accelerometers on a ship can provide information about the acting ice breaking mechanism, the ice conditions around the vessel, and about the location along the hull, where the ship-ice interaction occurs. This can be used as an additional tool in monitoring systems during operations in ice-infested waters.

Accelerated, untargeted metabolomics analysis of cutaneous T-cell lymphoma reveals metabolic shifts in plasma and tumor adjacent skins of xenograft mice.

Cutaneous T-cell lymphoma (CTCL) is a heterogeneous group of skin-homing T-cell neoplasms. Clinical management is stage based but diagnosis and prognosis could be extremely challenging. The presented study aims to explore the metabolic profiling of CTCL by an accelerated untargeted metabolomics data analysis tool "Mummichog" to facilitate the discoveries of potential biomarkers for clinical early stage diagnosis, prognosis, and treatments in CTCL. Ultra high-performance liquid chromatography-quadrupole time-of-flight-based untargeted metabolomics were conducted on the skin and plasma of CTCL mice. It showed that the metabolism of skin changed greatly versus control samples in the development of CTCL. Increased l-glutamate and decreased adenosine monophosphate were the most essential metabolic features of CTCL tumor and tumor adjacent skins. Unique metabolism changes in tumor adjacent non-involved skin tissues (ANIT) occurred in the progress of carcinogenesis, including upregulated cytidine-5'-triphosphate, aberrant biosynthesis of prostaglandins, pyrimidine, mevalonate pathway, and tryptophan degradation. Sharply elevated 5-phospho-α-d-ribose 1-diphosphate (PRPP) marked the final state of tumor in CTCL. In the plasma, systematic shifts in corticosterone, sphingolipid, and ceramide metabolism were found. These uncovered aberrant metabolites and metabolic pathways suggested that the metabolic reprogramming of PRPP in tumor tissues may cause the disturbance of cytidine and uridine metabolic homeostasis in ANIT. Accumulative cytidine-5'-triphosphate in ANIT may exert positive feedback on the PRPP level and leads to CTCL further development. In addition, the accelerated data analysis tool "Mummichog" showed good practicability and can be widely used in high-resolution liquid chromatography mass spectrometry-based untargeted metabolomics.

Design and evaluation of a hybrid storage system in HEP environment

Nowadays, the High Energy Physics experiments produce a large amount of data. These data are stored in mass storage systems which need to balance the cost, performance and manageability. In this paper, a hybrid storage system including SSDs (Solid-state Drive) and HDDs (Hard Disk Drive) is designed to accelerate data analysis and maintain a low cost. The performance of accessing files is a decisive factor for the HEP computing system. A new deployment model of Hybrid Storage System in High Energy Physics is proposed which is proved to have higher I/O performance. The detailed evaluation methods and the evaluations about SSD/HDD ratio, and the size of the logic block are also given. In all evaluations, sequential-read, sequential-write, random-read and random-write are all tested to get the comprehensive results. The results show the Hybrid Storage System has good performance in some fields such as accessing big files in HEP.

Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures.

Generalized tonic-clonic seizures (GTCSs) can be underestimated and can also increase mortality rates. The monitoring devices used to detect GTCS events in daily life are very helpful for early intervention and precise estimation of seizure events. Several studies have introduced methods for GTCS detection using an accelerometer (ACM), electromyography, or electroencephalography. However, these studies need to be improved with respect to accuracy and user convenience. This study proposes the use of an ACM banded to the wrist and spectral analysis of ACM data to detect GTCS in daily life. The spectral weight function dependent on GTCS was used to compute a GTCS-correlated score that can effectively discriminate between GTCS and normal movement. Compared to the performance of the previous temporal method, which used a standard deviation method, the spectral analysis method resulted in better sensitivity and fewer false positive alerts. Finally, the spectral analysis method can be implemented in a GTCS monitoring device using an ACM and can provide early alerts to caregivers to prevent risks associated with GTCS.

Force, displacement and strain nonlinear transfer function estimation.

The analysis of surface strain, displacement and acceleration data from the nonlinear response of a beam under base excitation is performed using frequency domain based reverse path identification. The uncorrelated nonlinear contributions of the dynamic response are used as inputs to produce repeatable transfer function estimates with the applied base acceleration as the output. The two stage process of displacements to strain to applied base excitation is proposed and also investigated. System identification of the reverse transfer functions is shown to be reasonably successful in quantifying the main frequency dependent contributions which occur at and around the fundamental natural frequency and harmonics due to the geometrical nonlinearity present.

Using Wearable Accelerometers in a Community Service Context to Categorize Falling Behavior

In this paper, the Multiscale Entropy (MSE) analysis of acceleration data collected from a wearable inertial sensor was compared with other features reported in the literature to observe falling behavior from the acceleration data, and traditional clinical scales to evaluate falling behavior. We use a fall risk assessment over a four-month period to examine >65 year old participants in a community service context using simple clinical tests, including the Short Form Berg Balance Scale (SFBBS), Timed Up and Go test (TUG), and the Short Portable Mental Status Questionnaire (SPMSQ), with wearable accelerometers for the TUG test. We classified participants into fallers and non-fallers to (1) compare the features extracted from the accelerometers and (2) categorize fall risk using statistics from TUG test results. Combined, TUG and SFBBS results revealed defining features were test time, Slope(A) and slope(B) in Sit(A)-to-stand(B), and range(A) and slope(B) in Stand(B)-to-sit(A). Of (1) SPMSQ; (2) TUG and SPMSQ; and (3) BBS and SPMSQ results, only range(A) in Stand(B)-to-sit(A) was a defining feature. From MSE indicators, we found that whether in the X, Y or Z direction, TUG, BBS, and the combined TUG and SFBBS are all distinguishable, showing that MSE can effectively classify participants in these clinical tests using behavioral actions. This study highlights the advantages of body-worn sensors as ordinary and low cost tools available outside the laboratory. The results indicated that MSE analysis of acceleration data can be used as an effective metric to categorize falling behavior of community-dwelling elderly. In addition to clinical application, (1) our approach requires no expert physical therapist, nurse, or doctor for evaluations and (2) fallers can be categorized irrespective of the critical value from clinical tests.

Development and validation of the Zurich chronic middle ear inventory (ZCMEI-21): an electronic questionnaire for assessing quality of life in patients with chronic otitis media.

Because existing patient-reported outcome measures (PROMs) specific for chronic otitis media (COM) are lacking certain relevant symptoms and dimensions of health-related quality of life (HRQoL), we aimed to develop and validate a new questionnaire for comprehensively measuring HRQoL in adult patients with COM. An expert panel and patients were involved in developing the first version of the Zurich chronic middle ear inventory, containing 33 items (ZCMEI-33). An electronic application was chosen not only to provide maximal data quality, but also to facilitate and accelerate data analysis. Item reduction was performed by testing the questionnaire in a first cohort (n=85). Using sequential statistical analysis, the ZCMEI-33 was reduced to 21 items (ZCMEI-21). Subsequently, the ZCMEI-21 was validated in a second cohort (n=76). Validation revealed a Cronbach's α of 0.91, indicating excellent internal consistency. Moreover, the ZCMEI-21 was able to discriminate between patients with COM and healthy participants (p<0.0001), thus possessing good discrimination validity. Assessing criterion validity, the ZCMEI-21 total score was compared to a question directly addressing HRQoL and the EQ-5D descriptive system score, a generic measure of HRQoL. Whereas the ZCMEI-21 total score and the EQ-5D descriptive system score were only moderately correlated (r=0.60, p<0.0001), the ZCMEI-21 total score and the question directly addressing HRQoL showed a strong correlation (r=0.74, p<0.0001). In conclusion, sufficient information on reliability and validity was obtained to propagate the application of the ZCMEI-21 to quantify HRQoL in patients with COM.

Gender differences in trunk acceleration and related posture during shuttle run cutting

Measurements using an accelerometer reflect the impact applied to the trunk. Measurement of trunk acceleration has the possibility of reflecting the typical characteristics of trunk motion during cutting. However, analysis of trunk acceleration data during cutting manoeuvres has not been previously conducted. This study aimed to analyse trunk acceleration during cutting manoeuvres to examine any gender differences or a relationship with posture. All participants (eight male and eight female college soccer athletes) performed a shuttle run cutting task, and trunk accelerations (medio-lateral, vertical, and antero-posterior) were calculated. The peak acceleration (G) and total magnitude during the 200 ms after foot contact were measured, and the forward trunk inclination and femoral angle were calculated from the video images taken using a sagittal plane camera. Peak vertical acceleration (mean, s) was significantly greater among female athletes than among male athletes (−2.18, s = 0.84 G; −1.15, s = 0.45 G, respectively; p < 0.01). Medio-lateral and antero-posterior peak acceleration and the total magnitude in all directions were not significantly different between genders. Moderate negative correlations were found between vertical peak acceleration and trunk forward inclination and femoral inclination (r = −0.57, p < 0.05; r = −0.69, p < 0.01, respectively). The difference in vertical acceleration between genders has the possibility to reflect a stiff cutting movement among female athletes. The acceleration of the upper trunk may be an index for evaluating cutting movements.

Acceleration Data Acquisition and Analysis Method of Shaking Table Test

Shaking table test is an important means of simulated earthquake in laboratory, slope shaking table test data provide a scientific basis for analysis of dynamic stability and instability mechanism of slopes. Sine vibration table test data processing is different from general frequency domain analysis method, need real-time data processing in time domain. Taking the sine sweep test conditions, which is one of the conditions in "Earthquake landslide and slope prevention and control technology research on shaking table test", as an example. Describes the layout of sensors in shaking table test and the reasons; Sine sweep test load and aim; and listed the steps and methods of the sine sweep test in data processing; Through the processing and analysis of test data identified the vibration frequency of model and shaking table system is 30Hz, damping ratio is 2.06%; Analysis and calculation of the different sections of the slope and position of the amplification coefficient. A methodological guidance for shaking table test and dynamic response analysis of the slope is provided.

Feature-Based Analysis of Plasma-Based Particle Acceleration Data

Plasma-based particle accelerators can produce and sustain thousands of times stronger acceleration fields than conventional particle accelerators, providing a potential solution to the problem of the growing size and cost of conventional particle accelerators. To facilitate scientific knowledge discovery from the ever growing collections of accelerator simulation data generated by accelerator physicists to investigate next-generation plasma-based particle accelerator designs, we describe a novel approach for automatic detection and classification of particle beams and beam substructures due to temporal differences in the acceleration process, here called acceleration features. The automatic feature detection in combination with a novel visualization tool for fast, intuitive, query-based exploration of acceleration features enables an effective top-down data exploration process, starting from a high-level, feature-based view down to the level of individual particles. We describe the application of our analysis in practice to analyze simulations of single pulse and dual and triple colliding pulse accelerator designs, and to study the formation and evolution of particle beams, to compare substructures of a beam, and to investigate transverse particle loss.

Broadening instructional scope with network visualization

Medical librarians can expedite biomedical research by broadening the scope of their instruction curricula to include courses on the use of network visualization tools. With the dramatic increase in the number of databases, data sets, tools, and software being used to store, retrieve, and analyze proteomic, genomic, and metabolomic data, researchers are being forced to navigate an ever-complex information environment. Medical librarians can accelerate data analysis by learning about, conducting comparative analyses for, and providing instruction on these resources. Many libraries, such as the University of Florida's Health Science Center Libraries and Washington University's Bernard Becker Medical Library, offer instruction on biomedical databases and tools such as those provided by the National Center for Biotechnology Information (NCBI) [1, 2]. Instruction on network visualization tools is not as commonly provided by libraries. Network visualization is a method of data analysis that uses nodes to represent a data point, such as a gene, and edges (lines connecting nodes) to represent a relationship between two data points, such as an interaction between two genes (Figure 1, online only). Nodes and edges can have attributes associated with them that provide additional information, such as the chromosome on which a gene is located. Network visualization is an effective method for representing biologic relationships by succinctly highlighting properties and trends in complex systems. Networks also allow the integration of multiple different kinds of data such as gene expression, ontologies, and protein structures that have traditionally been stored in their own repositories [3]. Network analysis and network modeling techniques are being applied to biological networks in order to provide new hypotheses for biological systems, and as a result, a large number of tools for visualizing networks are constantly being developed, used, and reviewed [4]. Teaching biomedical resources such as network visualization tools, in addition to those developed by NCBI, is an area where medical librarians can make a significant impact. While resources like OpenHelix provide some instruction on these types of resources, these sources are limited, providing an important opportunity for libraries to expand the scope of their instruction curricula and to use instruction methods that allow broad participation by users. This paper discusses the evolution of a successful training class on a free and useful network visualization tool and the methods used to reach a wide-ranging audience.

A Wireless Sensor Network‐Based Structural Health Monitoring System for Highway Bridges

Abstract: An integrated structural health monitoring (SHM) system for highway bridges is presented. The system is based on a customized wireless sensor network platform with a flexible design that provides a variety of sensors typical in SHM. These sensors include accelerometers, strain gauges, and temperature sensors with ultra‐low power consumption. An S‐Mote node, an acceleration sensor board, and a strain sensor board are developed to satisfy the requirements of bridge structural monitoring. Communication software components are integrated within TinyOS operating system to provide a flexible software platform whereas the data processing software performs analysis of acceleration, dynamic displacement, and dynamic strain data. The prototype system comprises a nearly linear multi‐hop topology and is deployed on an in‐service highway bridge. Data acquired from the system are used to examine network performance and to help evaluate the state of the bridge. Experimental results show that the system enables continuous or regular interval monitoring for in‐service highway bridges.

Development of Wireless Sensor Network for Data Collection and Footwork Training

The Wireless Sensor Network (WSN) is an advanced developing technology. It is applied in this research to develop a Video and Acceleration Data Synchronous Collection and Analysis System in order to collect the video sample and acceleration data synchronously to analyze them after corresponding the frame with acceleration data of table tennis and shot put players. The WSN's characteristics of networking mode, intelligence, wireless data transmission, selfconfiguration and management, supports of large number of nodes, and self healing, flexibility and mobility, node microminiaturization and low energy cost, etc, allow this system to collect many sensor nodes’ data. These sensor nodes could be deployed on many different positions of players’ body, such as arms, legs. The video clips and acceleration data could be displayed on the same windows of the system software simultaneously. Coaches and players can review information intuitively and clearly, use them to analyze their performance of technique and strength. And, a footwork movement training and data collection system which is developed by the same technology is used for table tennis player's movement training and result evaluation. This system can collect the data such as exercise duration time, each movement time. Besides the table tennis, the tennis, basketball, volleyball players could also use this kind of system to guide and monitor the movement ability training.

Theoretical analysis of acceleration data in Brain at Fall

Theoretical analysis of acceleration data in Brain at Fall