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Interviews from strength and conditioning coaches across all levels of athletic competition identified their two biggest concerns with the current state of wearable technology: (a) the lack of solutions that accurately capture data “from the ground up” and (b) the lack of trust due to inconsistent measurements. The purpose of this research is to investigate the use of liquid metal sensors, specifically Liquid Wire sensors, as a potential solution for accurately capturing ankle complex movements such as plantar flexion, dorsiflexion, inversion, and eversion. Sensor stretch linearity was validated using a Micro-Ohm Meter and a Wheatstone bridge circuit. Sensors made from different substrates were also tested and discovered to be linear at multiple temperatures. An ankle complex model and computing unit for measuring resistance values were developed to determine sensor output based on simulated plantar flexion movement. The sensors were found to have a significant relationship between the positional change and the resistance values for plantar flexion movement. The results of the study ultimately confirm the researchers’ hypothesis that liquid metal sensors, and Liquid Wire sensors specifically, can serve as a mitigating substitute for inertial measurement unit (IMU) based solutions that attempt to capture specific joint angles and movements. [ABSTRACT FROM AUTHOR]

A significant challenge in object detection is accurate identification of an object's position in image space, whereas one algorithm with one set of parameters is usually not enough, and the fusion of multiple algorithms and/or parameters can lead to more robust results. Herein, a new computational intelligence fusion approach based on the dynamic analysis of agreement among object detection outputs is proposed. Furthermore, we propose an online versus just in training image augmentation strategy. Experiments comparing the results both with and without fusion are presented. We demonstrate that the augmented and fused combination results are the best, with respect to higher accuracy rates and reduction of outlier influences. The approach is demonstrated in the context of cone, pedestrian and box detection for Advanced Driver Assistance Systems (ADAS) applications., Competing Interests: The authors declare no conflict of interest.

Objective: Monocortical screws are commonly employed in locking plate fixation, but specific recommendations for their placement are lacking and use of short monocortical screws in metaphyseal bone may be contraindicated. Objectives of this study were to evaluate axial pullout strength of two different lengths of monocortical screws placed in various regions of the canine humerus compared to bicortical screws, and to derive cortical thickness and bone density values for those regions using quantitative computed tomography analysis (QCT)., Methods: The QCT analysis was performed on 36 cadaveric canine humeri for six regions of interest (ROI). A bicortical, short monocortical, or 50% transcortical 3.5 mm screw was implanted in each ROI and axial pullout testing was performed., Results: Bicortical screws were stronger than monocortical screws in all ROI except the lateral epicondylar crest. Short monocortical metaphyseal screws were weaker than those placed in other regions. The 50% transcortical screws were stronger than the short monocortical screws in the condyle. A linear relationship between screw length and pullout strength was observed., Clinical Significance: Cortical thickness and bone density measurements were obtained from multiple regions of the canine humerus using QCT. Use of short monocortical screws may contribute to failure of locking plate fixation of humeral fractures, especially when placed in the condyle. When bicortical screw placement is not possible, maximizing monocortical screw length may optimize fixation stability for distal humeral fractures.

Most end-to-end Computer Aided Diagnosis (CAD) systems follow a three step approach - (1) Image enhancement and segmentation, (2) Feature extraction, and, (3) Classification. While the state of the art in image enhancement and segmentation can now very accurately identify regions of interest for feature extraction, they typically result in very high dimensional feature spaces. This adversely affects the performance of classification systems because a large feature space dimensionality necessitates a large training database to accurately model the statistics of class features (e.g. benign versus malignant classes). In this work, we present a robust multi-classifier decision fusion framework that employs a divide-and-conquer approach for alleviating the affects of high dimensionality of feature vectors. The feature space is partitioned into multiple smaller sized spaces, and a bank of classifiers (a multi-classifier system) is employed to perform classification in each of the partition. Finally, a decision fusion system merges decisions from each classifier in the bank into a single decision. The system is applied to the problem of classifying digital mammographic masses as either benign or malignant.

This letter presents an automated mammographic computer aided diagnosis (CAD) system to detect and segment spicules in digital mammograms, termed spiculation segmentation with level sets (SSLS). SSLS begins with a segmentation of the suspicious mass periphery, which is created using a previously developed adaptive level set segmentation algorithm (ALSSM) by the authors. The mammogram is then analyzed using features derived from the Dixon and Taylor Line Operator (DTLO), which is a method of linear structure enhancement. Features are extracted, optimized, and then the suspicious mass is classified as benign or malignant. To assess the system efficacy, 60 difficult mammographic images from the Digital Database of Screening Mammography (DDSM), containing 30 benign non-spiculated cases, 17 malignant spiculated cases, and 13 malignant non-spiculated cases, are analyzed. The initial spiculation detection method found 100% of the spiculated lesions with no false positive detections, and has area under the receiver operating characteristics (ROC) curve A(Z)=1.0. The values using ALSSM (periphery segmentation only) are A(Z)=0.9687 and 0.9708 for two investigated feature sets, and increases to A(Z)=0.986 2 using SSLS (spiculation segmentation). The best classification results are 93% overall accuracy (OA), with three false positives (FP) and one false negative (FN) using a 1-NN (Nearest Neighbor) or 2-NN classifier, and 92% OA with three FP and two FN using a maximum likelihood classifier.

We present a mammographic computer aided diagnosis (CAD) system, which uses an adaptive level set segmentation method (ALSSM), which segments suspicious masses in the polar domain and adaptively adjusts the border threshold at each angle to provide high-quality segmentation results. The primary contribution of this paper is the adaptive speed function for controlling level set segmentation. To assess the efficacy of the system, 60 relatively difficult cases (30 benign, 30 malignant) from the Digital Database of Screening Mammography (DDSM) are analyzed. The segmentation efficacy is analyzed qualitatively via visual inspection and quantitatively via the area under the receiver operating characteristics (ROC) curve (AZ) and classification accuracies. For the ALSSM, the best results are 87% overall accuracy, A(Z)=0.9687 with 28/30 malignant cases detected. The qualitative and quantitative results show that the ALSSM provides excellent segmentation and classification results and compares favorably to previous CAD systems in the literature which also used the DDSM database.

We present mammographic mass core segmentation, based on the Chan-Vese level set method. The proposed method is analyzed via resulting feature efficacies. Additionally, the core segmentation method is used to investigate the idea of a three stage segmentation approach, i.e. segment the mass core, periphery, and spiculations (if any exist) and use features from these three segmentations to classify the mass as either benign or malignant. The proposed core segmentation method and a proposed end-to-end computer aided detection (CAD) system using a three stage segmentation are implemented and experimentally tested with a set of 60 mammographic images from the Digital Database of Screening Mammography. Receiver operating characteristic (ROC) curve AZ values for morphological and texture features extracted from the core segmentation are shown to be on par, or better, than those extracted from a periphery segmentation. The efficacy of the core segmentation features when combined with the periphery and spiculation segmentation features are shown to be feature set dependent. The proposed end-to-end system uses stepwise linear discriminant analysis for feature selection and a maximum likelihood classifier. Using all three stages (core + periphery + spiculations) results in an overall accuracy (OA) of 90% with 2 false negatives (FN). Since many CAD systems only perform a periphery analysis, adding core features could be a benefit to potentially increase OA and reduce FN cases.