TOWARDS AN AUTOMATED FEEDBACK COACHING SUPPORT SYSTEM FOR SPRINT PERFORMANCE MONITORING

The purpose of this study was to investigate the feasibility of developing a cost-effective, automated performance feedback system to support sprint coaching. The proposed system is designed to deliver step length, step frequency, contact time and 10 m split time information of multiple athletes training on an indoor track. An integrated systems approach was chosen combining the novel Pisa Light-Gate (PLG) and Step Information Monitoring Systems (SIMS). Current results indicate data accuracy of RMS 1.662 cm for step length, RMS 0.977 ms for foot contact time and a split time detection accuracy of 8.45 ± 6.85 ms. These results suggest that the proposed integrated system, using off-the-shelf equipment, would go beyond currently available coaching tools by providing automated and highly accurate sprint performance information for multiple athletes

Generation and Handling of Hard Drive Duplicates as Piece of Evidence

An important area in digital forensics is images of hard disks. The correct production of the images as well as the integrity and authenticity of each hard disk image is essential for the probative force of the image to be used at court. Integrity and authenticity are under suspicion as digital evidence is stored and used by software based systems. Modifications to digital objects are hard or even impossible to track and can occur even accidentally. Even worse, vulnerabilities occur for all current computing systems. Therefore, it is difficult to guarantee a secure environment for forensic investigations. But intended deletions of dedicated data of disk images are often required because of legal issues in many countries. This article provides a technical framework on the protection of the probative force of hard disk images by ensuring the integrity and authenticity using state of the art technology. It combines hardware-based security, cryptographic hash functions and digital signatures to achieve a continuous protection of the image together with a reliable documentation of the status of the device that was used for image creation. The framework presented allows to detect modifications and to pinpoint the exact area of the modification to the digital evidence protecting the probative force of the evidence at a whole. In addition, it also supports the deletion of parts of images without invalidating the retained data blocks. Keywords: digital evidence, probative force hard disk image, verifiable deletion of image data, trusted imaging softwar

Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation

Many successful methods developed for medical image analysis that are based on machine learning use supervised learning approaches, which often require large datasets annotated by experts to achieve high accuracy. However, medical data annotation is time-consuming and expensive, especially for segmentation tasks. To solve the problem of learning with limited labeled medical image data, an alternative deep learning training strategy based on self-supervised pretraining on unlabeled MRI scans is proposed in this work. Our pretraining approach first, randomly applies different distortions to random areas of unlabeled images and then predicts the type of distortions and loss of information. To this aim, an improved version of Mask-RCNN architecture has been adapted to localize the distortion location and recover the original image pixels. The effectiveness of the proposed method for segmentation tasks in different pre-training and fine-tuning scenarios is evaluated based on the Osteoarthritis Initiative dataset. Using this self-supervised pretraining method improved the Dice score by 20% compared to training from scratch. The proposed self-supervised learning is simple, effective, and suitable for different ranges of medical image analysis tasks including anomaly detection, segmentation, and classification

Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss

Recent advances in deep learning algorithms have led to significant benefits for solving many medical image analysis problems. Training deep learning models commonly requires large datasets with expert-labeled annotations. However, acquiring expert-labeled annotation is not only expensive but also is subjective, error-prone, and inter-/intra- observer variability introduces noise to labels. This is particularly a problem when using deep learning models for segmenting medical images due to the ambiguous anatomical boundaries. Image-based medical diagnosis tools using deep learning models trained with incorrect segmentation labels can lead to false diagnoses and treatment suggestions. Multi-rater annotations might be better suited to train deep learning models with small training sets compared to single-rater annotations. The aim of this paper was to develop and evaluate a method to generate probabilistic labels based on multi-rater annotations and anatomical knowledge of the lesion features in MRI and a method to train segmentation models using probabilistic labels using normalized active-passive loss as a "noise-tolerant loss" function. The model was evaluated by comparing it to binary ground truth for 17 knees MRI scans for clinical segmentation and detection of bone marrow lesions (BML). The proposed method successfully improved precision 14, recall 22, and Dice score 8 percent compared to a binary cross-entropy loss function. Overall, the results of this work suggest that the proposed normalized active-passive loss using soft labels successfully mitigated the effects of noisy labels

Evaluation of optimal control formulations for predicting swing-through axillary crutch-assisted gait

Crutches are widely used to assist gait in individuals with lower limb impairment. Walking with crutches alters both upper and lower body loading, potentially leading to discomfort. As such, it is important to study how crutch walking affects upper and lower extremity movement patterns. Computer modelling and simulation can provide answers that motion analysis cannot. For this reason, the availability of an algorithm that allows the prediction of different crutch walking patterns could be useful in order to study the impact of changing conditions on crutch walking, and could overcome some limitations of experimental studies, such as difficulty in recruiting subjects or limitation in the number of tests that can be performedPeer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version

PROCESS VALIDATION IN CALCULATING MEDIAN PROXIMITY IN TIBIOFEMORAL CARTILAGE DEFORMATION UNDER FULL BODY LOADING

INTRODUCTION Knee osteoarthritis (OA) is characterized by progressive and irreversible degradation of tibiofemoral (TF) cartilages. Anterior cruciate ligament (ACL) rupture is a known risk factor for post-traumatic OA (PTOA) [1]. However, there are currently no in-vivo tests to diagnose pre-radiographic PTOA. Following injury, the cartilage macromolecular matrix weakens, cartilage swells and consequently cartilage softness increases [2]. This research investigates the in-vivo effects of ACL injury on cartilage deformation magnitude and rate under full body loading. The objective of this project was to determine the consequences of cartilage model mesh types and incremental mesh simplifications on the accuracy of resultant TF cartilage proximities. METHODS The affected knee of a 37 year old male PTOA subject (ACL deficient for 6 years) was imaged using Magnetic Resonance Imaging (FIESTA sequence; 3T GE Discovery 750). 3D TF bone and cartilage models were generated in Amira (VSG, Germany). The subject performed a 10 minute standing task in the Dual Fluoroscopic (DF) laboratory. DF images (32LP/mm) were collected at 6Hz. Bone alignments were reconstructed from DF images using AutoScoper (Brown University, USA) and cartilage models were co-registered. TF cartilage surface proximity was determined as the surface normal distance from each triangular mesh face onto the opposing cartilage. (Matlab, v2014b, The MathWorks, USA). The effects on surface proximities of three types of triangular cartilage surface meshes, generated in Amira, were analysed: 1) Basic Simplification - reducing face numbers with variable mesh size; 2) Remeshed Surface – isotropic mesh; 3) Iteratively Smoothed Remeshed Surface. Face numbers were reduced at 10% increments from the original surface for each surface type. RESULTS Median proximity errors for the Remeshed Surface were consistently smaller than the other mesh types across all four cartilage surface compartments. The medial tibial plateau displayed a rapid increase in error (Figure 1) indicating a high sensitivity to model simplification. This may have been due to its more complex surface geometry. The maximum acceptable error was chosen to match the minimum detectable displacement of 0.05mm for this DF system [3]. DISCUSSION AND CONCLUSIONS The findings of this investigation identified differences in the error of cartilage surface proximities under loading due to the use of different mesh types and simplifications. The smoothing technique used by Amira did not consistently converge to a surface and the variable triangle size in Basic Simplification affected the computation of proximity, resulting in unpredictable error spikes in cartilage surface proximity calculations. The results suggest that surface modeling parameters are surface geometry specific. The limiting case of the medial tibial plateau showed the optimal simplification was 0.594mm triangle mesh side length (40% of the original faces). These results inform ongoing work toward an in-vivo pre-radiographic diagnostic of PTOA

Agriculture's prominence in the INDCs

Analysis of agriculture in countries’ climate change mitigation and adaptation strategies finds: Most Parties to the UNFCCC include agriculture in their mitigation targets (80%) and adaptation strategies (64%); Non-annex 1 Parties note the need for international financial support to implement their INDCs and raise the ambition of their contributions; For countries to meet their targets, climate finance will need to address agriculture

Deschampsia eminens (J. Presl) Saarela var. eminens

TunaripublishedVersio

Concurrent validity and reliability of a semi-automated approach to measuring the magnetic resonance imaging morphology of the knee joint in active youth

Post-traumatic knee osteoarthritis is attributed to alterations in joint morphology, alignment, and biomechanics triggered by injury. While magnetic resonance (MR) imaging-based measures of joint morphology and alignment are relevant to understanding osteoarthritis risk, time consuming manual data extraction and measurement limit the number of outcomes that can be considered and deter widespread use. This paper describes the development and evaluation of a semi-automated software for measuring tibiofemoral and patellofemoral joint architecture using MR images from youth with and without a previous sport-related knee injury. After prompting users to identify and select key anatomical landmarks, the software can calculate 37 (14 tibiofemoral, 23 patellofemoral) relevant geometric features (morphology and alignment) based on established methods. To assess validity and reliability, 11 common geometric features were calculated from the knee MR images (proton density and proton density fat saturation sequences; 1.5 T) of 76 individuals with a 3-10-year history of youth sport-related knee injury and 76 uninjured controls. Spearman's or Pearson's correlation coefficients (95% CI) and Bland-Altman plots were used to assess the concurrent validity of the semi-automated software (novice rater) versus expert manual measurements, while intra-class correlation coefficients (ICC 2,1; 95%CI), standard error of measurement (95%CI), 95% minimal detectable change, and Bland-Altman plots were used to assess the inter-rater reliability of the semi-automated software (novice vs resident radiologist rater). Correlation coefficients ranged between 0.89 (0.84, 0.92; Lateral Trochlear Inclination) and 0.97 (0.96, 0.98; Patellar Tilt Angle). ICC estimates ranged between 0.79 (0.63, 0.88; Lateral Patellar Tilt Angle) and 0.98 (0.95, 0.99; Bisect Offset). Bland-Altman plots did not reveal systematic bias. These measurement properties estimates are equal, if not better than previously reported methods suggesting that this novel semi-automated software is an accurate, reliable, and efficient alternative method for measuring large numbers of geometric features of the tibiofemoral and patellofemoral joints from MR studies. </p

DYNAMIC VALIDATION OF TIBIOFEMORAL KINEMATICS MEASURED USING A DUAL FLUOROSCOPY SYSTEM: A MARKER-BASED APPROACH

INTRODUCTION Knee joint cartilage degeneration in post-traumatic osteoarthritis is initiated at the point of injury and progresses through abnormal movement mechanics [1]. Anterior cruciate ligament rupture influences the development and progression of osteoarthritis [1], however the specific in vivo effects of abnormal bone and joint kinematics and kinetics on human cartilage health remain largely unknown.  Quantifying in vivo knee kinematics with submillimeter accuracies may elucidate injurious movement alterations.  Dual Fluoroscopy (DF) allows for accurate, high-speed, and non-invasive skeletal kinematics assessment, but requires validation.  The aim of this project was to quantify the in vitro accuracy and precision of a high-speed dual fluoroscopy system for measuring 6 degree of freedom (DOF) knee kinematics obtained from a marker-less 2D-3D registration approach as compared to the gold standard marker-based method. For this preliminary work, we hypothesized that the precision of inter-bead 3D Euclidean distance measurement is less than or equal to 0.10 mm [2]. METHODS Upon approval by the local ethics committee, one female cadaveric human leg was obtained through the local body donation program. Four 3mm metal beads were surgically implanted in the distal femur and proximal tibia.  Thereafter, the limb was scanned using computed tomography (CT). Following imaging, the soft tissues of the proximal shaft of the femur were dissected to expose the bone and the femoral head was removed. The proximal shaft of the femur was then fixed in a custom-made metal cylinder using fixation screws and potted using polymethyl methacrylate (PMMA). The free end of the metal cylinder was in turn fixed to an articulated 6 DOF tripod mount (Manfrotto, Italy).  In the DF laboratory the limb was suspended in the DF field of view using a custom steel frame. A rope pulley system, fixed around the ankle joint, was used to manipulate the limb. DF images were acquired at 60 Hz during manipulation of the limb into knee flexion. All images were distortion corrected and calibrated using established procedures. Marker-based tracking was conducted on 75 DF frames using in-house software to determine the 2D coordinates of the bead centroids in each image pair.  Subsequently, a modified direct linear transform was applied to obtain the 3D bead centroid coordinates. Matlab (MathWorks, v2014b, USA) code was written in order to determine the Euclidean distance between beads. RESULTSTable 1: The mean distance between beads in the femur and tibia ± SD (mm) calculated over 75 DF frames.  Right: Camera 1 DF image demonstrating the numbering of beads.DISCUSSION AND CONCLUSIONS The data indicated inter-bead distance variabilities consistent with previously observed system errors (for static imaging), when investigating a moving limb (Table 1). The observed variations could be due to multiple contributors. A lack of bead sphericity and bead deformation, as a result of surgical bead implantation, may have caused erroneous bead centroid estimates. Further, DF image distortions may have persisted even after distortion correction, contributing to observed error. Future steps include improved image calibration using a sophisticated bundle adjustment algorithm to further reduce system errors [3]