Intra- and inter-rater reliability of joint range of motion tests using tape measure, digital inclinometer and inertial motion capturing

Background In clinical practice range of motion (RoM) is usually assessed with low-cost devices such as a tape measure (TM) or a digital inclinometer (DI). However, the intra- and inter-rater reliability of typical RoM tests differ, which impairs the evaluation of therapy progress. More objective and reliable kinematic data can be obtained with the inertial motion capture system (IMC) by Xsens. The aim of this study was to obtain the intra- and inter-rater reliability of the TM, DI and IMC methods in five RoM tests: modified Thomas test (DI), shoulder test modified after Janda (DI), retroflexion of the trunk modified after Janda (DI), lateral inclination (TM) and fingertip-to-floor test (TM). Methods Two raters executed the RoM tests (TM or DI) in a randomized order on 22 healthy individuals while, simultaneously, the IMC data (Xsens MVN) was collected. After 15 warm-up repetitions, each rater recorded five measurements. Findings Intra-rater reliabilities were (almost) perfect for tests in all three devices (ICCs 0.886–0.996). Inter-rater reliability was substantial to (almost) perfect in the DI (ICCs 0.71–0.87) and the IMC methods (ICCs 0.61–0.993) and (almost) perfect in the TM methods (ICCs 0.923–0.961). The measurement error (ME) for the tests measured in degree (°) was 0.9–3.3° for the DI methods and 0.5–1.2° for the IMC approaches. In the tests measured in centimeters the ME was 0.5–1.3cm for the TM methods and 0.6–2.7cm for the IMC methods. Pearson correlations between the results of the DI or the TM respectively with the IMC results were significant in all tests except for the shoulder test on the right body side (r = 0.41–0.81). Interpretation Measurement repetitions of either one or multiple trained raters can be considered reliable in all three devices

Immobility-associated thromboprotection is conserved across mammalian species from bear to human

Venous thromboembolism (VTE) comprising deep venous thrombosis and pulmonary embolism is a major cause of morbidity and mortality. Short-term immobility-related conditions are a major risk factor for the development of VTE. Paradoxically, long-term immobilized free-ranging hibernating brown bears and paralyzed spinal cord injury (SCI) patients are protected from VTE. Here we aimed to identify mechanisms of immobility-associated VTE protection in a cross-species approach. Mass spectrometry-based proteomics revealed an antithrombotic signature in platelets of hibernating brown bears with heat shock protein 47 (HSP47) as most substantially reduced protein. HSP47 downregulation or ablation attenuated immune cell activation and NET formation, contributing to thromboprotection in bears, SCI patients and mice. This cross-species conserved platelet signature may give rise to antithrombotic therapeutics and prognostic markers beyond immobility-associated VTE

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Timing and Schedulability Analysis of Real-Time Systems using Hidden Markov Models

In real-time systems functional requirements are coupled to timing requirements, a specified event needs to occur at the appropriate time.  In order to ensure that timing requirements are fulfilled, there are two main approaches, static and measurement-based. The static approach relies on modeling the hardware and software and calculating upper bounds for the timing behavior. On the other hand, measurement-based approaches use timing data collected from the system to estimate the timing behavior. The usability of static and measurement-based approaches is limited in many modern systems due to the increased complexity of hardware and software architectures. Static approaches to timing and schedulability analysis are often infeasible due to their complexity. Measurement-based approaches require that design-time measurements are representative of the timing behavior at runtime, which is problematic to ensure in many cases. Designing systems that guarantee the timing requirements without excessive resource overprovisioning is a challenge. A Hidden Markov Model (HMM) describes a system where the behavior is state-dependent.  In this thesis, we model the execution time distribution of a periodic task as an HMM where the states are associated with continuous emission distributions. By modeling the execution times in this manner with a limited number of parameters, a step is taken on the path toward tracking and controlling timing properties at runtime.  We present a framework for parameter identification of an HMM with Gaussian emission distributions from timing traces, and validation of the identified models. In evaluated cases, the parameterized models are valid in relation to timing traces. For cases where design-time measurements are not representative of the system at runtime we present a method for the online adaptive update of the emission distributions of an HMM. Evaluation with synthetic data shows that the estimate tracks the ground truth distribution.  A method for estimating the deadline miss probability for a task with execution times modeled by an HMM with Gaussian emission distributions, in a Constant Bandwidth Server (CBS) is proposed. The method is evaluated with simulation and for a synthetic task with a known Markov Chain structure running on real hardware.I realtidssystem är funktionella krav kopplade till tidskrav – en viss händelse måste inträffa vid rätt tid. För att försäkra sig om att tidskrav är uppfyllda finns två huvudsakliga metoder – statisk eller mätningsbaserad. En statisk analys baseras på modeller av hårdvara och mjukvara, och beräknar en övre gräns för tidsbeteendet. Mätningsbaserade analyser använder insamlat data från systemet för att uppskatta tidsbeteendet. Användbarheten av både statiska och mätningsbaserade metoder är begränsad i många moderna system eftersom komplexiteten hos hårdvara och mjukvara ökat. Statiska metoder är ofta omöjliga att genomföra på grund av komplexiteten. För mätningsbaserade metoder krävs att mätningarna som insamlats vid design är representativa för tidsbeteendet i drift, vilket är svårt att garantera i många fall. Att designa system som garanterar tidskraven utan överdriven resurstilldelning är en utmaning. En Hidden Markov Model (HMM) beskriver ett system med beteende som är tillståndsberoende. I denna avhandling modellerar vi exekveringstidens fördelning hos en periodisk task (uppgift) som en HMM där tillstånden är kopplade till kontinuerliga emissionsfördelningar. Genom att modellera exekveringstiderna på detta vis med ett begränsat antal parametrar, tar vi ett steg på vägen mot att följa och kontrollera tidsbeteendet i drift. Vi presenterar ett ramverk för parameteridentifiering för en HMM med Gaussiska emissionsfördelningar från tidsdata, och validering av de identifierade modellerna. De parametriserade modellerna är giltiga i relation till tidsdata i de fall som utvärderats. För fall när mätningar vid design inte är representativa för systemet i drift presenterar vi en metod för direkt adaptiv uppdatering av emissionsfördelningarna i en HMM. Utvärdering med syntetiska data visar att uppskattningen följer den sanna fördelningen. En metod föreslås för att uppskatta sannolikheten för att missa en deadline när exekveringstiden modelleras som en HMM med Gaussiska emissionsfördelningar hos en task i en Constant Bandwidth Server (CBS). Metoden utvärderas med simulering och med syntetiska program med känd Markov-struktur som körs på verklig hårdvara

Timing and Schedulability Analysis of Real-Time Systems using Hidden Markov Models

In real-time systems functional requirements are coupled to timing requirements, a specified event needs to occur at the appropriate time.  In order to ensure that timing requirements are fulfilled, there are two main approaches, static and measurement-based. The static approach relies on modeling the hardware and software and calculating upper bounds for the timing behavior. On the other hand, measurement-based approaches use timing data collected from the system to estimate the timing behavior. The usability of static and measurement-based approaches is limited in many modern systems due to the increased complexity of hardware and software architectures. Static approaches to timing and schedulability analysis are often infeasible due to their complexity. Measurement-based approaches require that design-time measurements are representative of the timing behavior at runtime, which is problematic to ensure in many cases. Designing systems that guarantee the timing requirements without excessive resource overprovisioning is a challenge. A Hidden Markov Model (HMM) describes a system where the behavior is state-dependent.  In this thesis, we model the execution time distribution of a periodic task as an HMM where the states are associated with continuous emission distributions. By modeling the execution times in this manner with a limited number of parameters, a step is taken on the path toward tracking and controlling timing properties at runtime.  We present a framework for parameter identification of an HMM with Gaussian emission distributions from timing traces, and validation of the identified models. In evaluated cases, the parameterized models are valid in relation to timing traces. For cases where design-time measurements are not representative of the system at runtime we present a method for the online adaptive update of the emission distributions of an HMM. Evaluation with synthetic data shows that the estimate tracks the ground truth distribution.  A method for estimating the deadline miss probability for a task with execution times modeled by an HMM with Gaussian emission distributions, in a Constant Bandwidth Server (CBS) is proposed. The method is evaluated with simulation and for a synthetic task with a known Markov Chain structure running on real hardware.I realtidssystem är funktionella krav kopplade till tidskrav – en viss händelse måste inträffa vid rätt tid. För att försäkra sig om att tidskrav är uppfyllda finns två huvudsakliga metoder – statisk eller mätningsbaserad. En statisk analys baseras på modeller av hårdvara och mjukvara, och beräknar en övre gräns för tidsbeteendet. Mätningsbaserade analyser använder insamlat data från systemet för att uppskatta tidsbeteendet. Användbarheten av både statiska och mätningsbaserade metoder är begränsad i många moderna system eftersom komplexiteten hos hårdvara och mjukvara ökat. Statiska metoder är ofta omöjliga att genomföra på grund av komplexiteten. För mätningsbaserade metoder krävs att mätningarna som insamlats vid design är representativa för tidsbeteendet i drift, vilket är svårt att garantera i många fall. Att designa system som garanterar tidskraven utan överdriven resurstilldelning är en utmaning. En Hidden Markov Model (HMM) beskriver ett system med beteende som är tillståndsberoende. I denna avhandling modellerar vi exekveringstidens fördelning hos en periodisk task (uppgift) som en HMM där tillstånden är kopplade till kontinuerliga emissionsfördelningar. Genom att modellera exekveringstiderna på detta vis med ett begränsat antal parametrar, tar vi ett steg på vägen mot att följa och kontrollera tidsbeteendet i drift. Vi presenterar ett ramverk för parameteridentifiering för en HMM med Gaussiska emissionsfördelningar från tidsdata, och validering av de identifierade modellerna. De parametriserade modellerna är giltiga i relation till tidsdata i de fall som utvärderats. För fall när mätningar vid design inte är representativa för systemet i drift presenterar vi en metod för direkt adaptiv uppdatering av emissionsfördelningarna i en HMM. Utvärdering med syntetiska data visar att uppskattningen följer den sanna fördelningen. En metod föreslås för att uppskatta sannolikheten för att missa en deadline när exekveringstiden modelleras som en HMM med Gaussiska emissionsfördelningar hos en task i en Constant Bandwidth Server (CBS). Metoden utvärderas med simulering och med syntetiska program med känd Markov-struktur som körs på verklig hårdvara

8th International Robotic Sailing Conference

This book presents the cutting edge developments within a broad field related to robotic sailing. The contributions were presented during the 8th International Robotic Sailing Conference, which has taken place as a part of the 2015 World Robotic Sailing Championships in Mariehamn, Åland (Finland), August 31st – September 4th 2015. Since more than a decade, a series of competitions such as the World Robotic Sailing Championship have stimulated a variety of groups to work on research and development around autonomous sailing robots, which involves boat designers, naval architects, electrical engineers and computer scientists. While many of the challenges in building a truly autonomous sailboat are still unsolved, the books presents the state of the art of research and development within platform optimization, route and stability planning, collision avoidance, power management and boat control

Surgical audio information as base for haptic feedback in robotic-assisted procedures

This work aims to demonstrate the feasibility that haptic information can be acquired from a da Vinci robotic tool using audio sensing according to sensor placement requirements in a real clinical scenario. For that, two potential audio sensor locations were studied using an experimental setup for performing, in a repeatable way, interactions of a da Vinci forceps with three different tissues. The obtained audio signals were assessed in terms of their resulting signal-to-noise-ratio (SNR) and their capability to distinguish between different tissues. A spectral energy distribution analysis using Discrete Wavelet Transformation was performed to extract signal signatures from the tested tissues. Results show that a high SNR was obtained in most of the audio recordings acquired from both studied positions. Additionally, evident spectral energy-related patterns could be extracted from the audio signals allowing us to distinguish between different palpated tissues

Surgical Audio Guidance: Feasibility Check for Robotic Surgery Procedures

In robot-assisted procedures, the surgeon controls the surgical instruments from a remote console, while visually monitoring the procedure through the endoscope. There is no haptic feedback available to the surgeon, which impedes the assessment of diseased tissue and the detection of hidden structures beneath the tissue, such as vessels. Only visual clues are available to the surgeon to control the force applied to the tissue by the instruments, which poses a risk for iatrogenic injuries. Additional information on haptic interactions of the employed instruments and the treated tissue that is provided to the surgeon during robotic surgery could compensate for this deficit. Acoustic emissions (AE) from the instrument/tissue interactions, transmitted by the instrument are a potential source of this information. AE can be recorded by audio sensors that do not have to be integrated into the instruments, but that can be modularly attached to the outside of the instruments shaft or enclosure. The location of the sensor on a robotic system is essential for the applicability of the concept in real situations. While the signal strength of the acoustic emissions decreases with distance from the point of interaction, an installation close to the patient would require sterilization measures. The aim of this work is to investigate whether it is feasible to install the audio sensor in non-sterile areas far away from the patient and still be able to receive useful AE signals. To determine whether signals can be recorded at different potential mounting locations, instrument/tissue interactions with different textures were simulated in an experimental setup. The results showed that meaningful and valuable AE can be recorded in the non-sterile area of a robotic surgical system despite the expected signal losses