Distance Metrics Research Articles

Multi-angle 3D measurement of small workpieces using EDMS algorithm

Binocular vision can get the three-dimensional information of the objects according to two-dimensional images. However, when the background texture information of the workpiece to be measured is weak, or the depth information cannot be recognized due to the change of viewing angle, it will lead to poor three-dimensional measurement accuracy. To address this problem, the paper proposes a multi-view workpiece 3D measurement method based on binocular vision. First, an experimental bench with a Chessboard is designed. The corner point reconstruction is realized by extracting the corner point of the calibration plate. The checkerboard plane is fitted by the least squares method to obtain the checkerboard plane mathematical model. Then, the vertices of the workpiece are extracted at the subpixel level, and a minimum distance sparse vertex stereo matching algorithm (EDMS) based on Euclidean distance metric is proposed to achieve accurate and fast corner matching. Finally, the three-dimensional dimensions of the workpiece are calculated. Through experiments on multiple angles of the two workpieces, the results show that the average absolute error measured by the method at different angles is 0.33 mm, the total relative error is 0.90%, and the variance is less than that 0.01 mm2, realizing the more accurate measurement of multi-view three-dimensional dimensions of small workpieces. This paper provides a new binocular vision handheld mobile 3D measurement equipment method.

A unified framework for multi-lead ECG characterization using Laplacian Eigenmaps

Background. The analysis of multi-lead electrocardiographic (ECG) signals requires integrating the information derived from each lead to reach clinically relevant conclusions. This analysis could benefit from data-driven methods compacting the information in those leads into lower-dimensional representations (i.e. 2 or 3 dimensions instead of 12). Objective. We propose Laplacian Eigenmaps (LE) to create a unified framework where ECGs from different subjects can be compared and their abnormalities are enhanced. Approach. We conceive a normal reference ECG space based on LE, calculated using signals of healthy subjects in sinus rhythm. Signals from new subjects can be mapped onto this reference space creating a loop per heartbeat that captures ECG abnormalities. A set of parameters, based on distance metrics and on the shape of loops, are proposed to quantify the differences between subjects. Main results. This methodology was applied to find structural and arrhythmogenic changes in the ECG. The LE framework consistently captured the characteristics of healthy ECGs, confirming that normal signals behaved similarly in the LE space. Significant differences between normal signals, and those from patients with ischemic heart disease or dilated cardiomyopathy were detected. In contrast, LE biomarkers did not identify differences between patients with cardiomyopathy and a history of ventricular arrhythmia and their matched controls. Significance. This LE unified framework offers a new representation of multi-lead signals, reducing dimensionality while enhancing imperceptible abnormalities and enabling the comparison of signals of different subjects.

Strength Learning Particle Swarm Optimization for Multiobjective Multirobot Task Scheduling

Cooperative heterogeneous multirobot systems have attracted increasing attention in recent years. They use multiple heterogeneous robots to execute complex tasks in a coordinated way. The allocation of heterogeneous robots to cooperative tasks is a significant and challenging optimization problem. However, little work has gone into scheduling large-scale cooperative tasks with precedence constraints and multiple conflicting optimization objectives. Existing methods are insufficient to address the issue. We propose a multiobjective model and develop strength learning particle swarm optimization (SLPSO) to optimize multiple objectives. In this article, the problem is converted into a two-step problem of task permutation construction and robot subset selection. In order to coordinate with the time-extended property of the problem, SLPSO utilizes a hybrid encode scheme: an element-based representation for task permutations and a binary representation for robot coalitions. A strength learning strategy with heuristic information guides particles to enhance their best-performing objectives for improving swarm convergence. In addition, an estimation-based local search is developed to improve spare solutions for enhancing swarm diversity, which determines the search direction by estimating fitness improvements. Experimental results on thirty problem instances are elaborated to demonstrate that the proposed SLPSO significantly outperforms the state-of-the-art algorithms in terms of inverted generational distance and hypervolume metrics. The proposed SLPSO can obtain a set of high-quality and diversified solutions.

Modeling Long-term Spike Frequency Adaptation in SA-I Afferent Neurons Using an Izhikevich-based Biological Neuron Model.

To develop a biomimetic artificial tactile sensing system capable of detecting sustained mechanical touch, we propose a novel biological neuron model (BNM) for slowly adapting type I (SA-I) afferent neurons. The proposed BNM is designed by modifying the Izhikevich model to incorporate long-term spike frequency adaptation. Adjusting the parameters renders the Izhikevich model describing various neuronal firing patterns. We also search for optimal parameter values for the proposed BNM to describe firing patterns of biological SA-I afferent neurons in response to sustained pressure longer than 1-second. We obtain the firing data of SA-I afferent neurons for six different mechanical pressure ranging from 0.1 mN to 300 mN from the ex-vivo experiment on SA-I afferent neurons in rodents. Upon finding the optimal parameters, we generate spike trains using the proposed BNM and compare the resulting spike trains to those of biological SA-I afferent neurons using the spike distance metrics. We verify that the proposed BNM can generate spike trains showing long-term adaptation, which is not achievable by other conventional models. Our new model may offer an essential function to artificial tactile sensing technology to perceive sustained mechanical touch.

Towards Detecting Influential Members and Critical Topics from Dark Web Forums

Conventionally, the Internet consists of three parts: Surface, Deep, and Dark Webs. Inthe last two decades, a massive increase in illicit activities took place on the differentplatforms of the Dark Web. Moreover, social networks on Dark Web implicateextremism dissemination on a wide scale. In this paper, we propose an approach togenerate textual patterns from discussions on Dark Web terrorist forums employingData Mining techniques. The discovered patterns help identify the influential membersand extract critical topics. We describe our system modules that perform datapreprocessing, text preprocessing with TF-IDF weighting, outlier detection, clusteringevaluation, clustering, and clustering validation, implemented with the RapidMinertool. We apply K-Means as the Clustering method with different distance metrics,evaluate the clustering process using Elbow and Silhouette methods, and validate itusing Davies-Bouldin Index. Furthermore, we investigate the effects of altering thedistance metrics for outlier detection on the Clustering results.

Steel surface defect detection based on self-supervised contrastive representation learning with matching metric

Defect detection is crucial in the quality control of industrial applications. Existing supervised methods are heavily reliant on the large amounts of labeled data. However, labeled data in some specific fields are still scarce, and it requires professionals to do expensive manual annotations. In this paper, we construct a novel self-supervised steel surface defect detection model by learning better embedding feature representation of the defect on large amounts of unlabeled data, which can achieve excellent results in downstream detection tasks. Commonly used image embeddings strategies in self-supervised contrastive learning methods destroy the spatial structures of the image and are not suitable for pre-training of object detection. To address the aforementioned issue, we preserve convolutional feature maps to mine robust data structures and local features, which can enhance the representation capability of the upstream model and make it applicable for transfer to object detection tasks. Besides, in order to eliminate the effect of random augmentations of contrastive learning, which can introduce noise on multi-target coexistence datasets, the Earth Mover’s Distance (EMD) metric is employed to evaluate the contrastive matching similarity. Finally, a Self-supervised Contrastive Representation Learning framework with EMD (SCRL-EMD) is constructed through learning on large-scale unlabeled data and then transferred to Faster R-CNN and RetinaNet for detection performance validation on two public steel defect datasets. Comparative experimental results show that our method can achieve superior results than the state-of-the-art approaches. Compared to the baseline model, it achieves 4.1% and 6.8% mAP improvement on the two datasets, respectively. More importantly, a further improvement can be achieved on a smaller downstream dataset, revealing the meaningful potential of our method in exploiting more readily available unlabeled data.

METHODS FOR DETERMINING SIMILARITY OF CATEGORICAL ORDERED DATA

Context. The development of effective distance metrics and similarity measures for categorical features is an important task in data analysis, machine learning, and decision theory since a significant portion of object properties is described by non-numerical values. Typically, the dependence between categorical features may be more complex than simply comparing them for equality or inequality. Such attributes can be relatively similar, and to construct an effective model, it is necessary to consider this similarity when calculating distance or similarity measures.
 Objective. The aim of the study is to improve the efficiency of solving practical data analysis problems by developing mathematical tools for determining the similarity of objects based on categorical ordered features.
 Method. A distance based on weighted Manhattan distance and a similarity measure for determining the similarity of objects based on categorical ordinal features (i.e. a linear order with scales of preference considering the problem domain can be specified on the attribute value set) are proposed. It is proven that the distance formula satisfies the axioms of non-negativity, symmetry, triangle inequality, and upper bound, and therefore is a distance metric in the space of ranked categorical features. It is also proven that the similarity measure presented in the study satisfies the axioms of boundedness, symmetry, maximum and minimum similarity, and is described by a decreasing function.
 Results. The developed approach has been implemented in an applied problem of determining the degree of similarity between objects described by ordered categorical features.
 Conclusions. In this study, mathematical tools were developed to determine similarity between structured data described by categorical attributes that can be ordered based on a specific priority in the form of a ranking system with preferences. Their properties were analyzed. Experimental studies have shown the convenience and “intuitive understanding” of the logic of data processing in solving practical problems. The proposed approach can provide the opportunity to conduct new meaningful research in data analysis. Prospects for further research lie in the experimental use of the proposed tools in practical tasks and in studying their effectiveness.

Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning

For deep ordinal classification, learning a well-structured feature space specific to ordinal classification is helpful to properly capture the ordinal nature among classes. Intuitively, when Euclidean distance metric is used, an ideal ordinal layout in feature space would be that the sample clusters are arranged in class order along a straight line in space. However, enforcing samples to conform to a specific layout in the feature space is a challenging problem. To address this problem, in this paper, we propose a novel Constrained Proxies Learning (CPL) method, which can learn a proxy for each ordinal class and then adjusts the global layout of classes by constraining these proxies. Specifically, we propose two kinds of strategies: hard layout constraint and soft layout constraint. The hard layout constraint is realized by directly controlling the generation of proxies to force them to be placed in a strict linear layout or semicircular layout (i.e., two instantiations of strict ordinal layout). The soft layout constraint is realized by constraining that the proxy layout should always produce unimodal proxy-to-proxies similarity distribution for each proxy (i.e., to be a relaxed ordinal layout). Experiments show that the proposed CPL method outperforms previous deep ordinal classification methods under the same setting of feature extractor.

Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning (Student Abstract)

Intentionally crafted adversarial samples have effectively exploited weaknesses in deep neural networks. A standard method in adversarial robustness assumes a framework to defend against samples crafted by minimally perturbing a sample such that its corresponding model output changes. These sensitivity attacks exploit the model's sensitivity toward task-irrelevant features. Another form of adversarial sample can be crafted via invariance attacks, which exploit the model underestimating the importance of relevant features. Previous literature has indicated a tradeoff in defending against both attack types within a strictly L-p bounded defense. To promote robustness toward both types of attacks beyond Euclidean distance metrics, we use metric learning to frame adversarial regularization as an optimal transport problem. Our preliminary results indicate that regularizing over invariant perturbations in our framework improves both invariant and sensitivity defense.

Metric Nearness Made Practical

Given a square matrix with noisy dissimilarity measures between pairs of data samples, the metric nearness model computes the best approximation of the matrix from a set of valid distance metrics. Despite its wide applications in machine learning and data processing tasks, the model faces non-trivial computational requirements in seeking the solution due to the large number of metric constraints associated with the feasible region. Our work designed a practical approach in two stages to tackle the challenge and improve the model's scalability and applicability. The first stage computes a fast yet high-quality approximate solution from a set of isometrically embeddable metrics, further improved by an effective heuristic. The second stage refines the approximate solution with the Halpern-Lions-Wittmann-Bauschke projection algorithm, which converges quickly to the optimal solution. In empirical evaluations, the proposed approach runs at least an order of magnitude faster than the state-of-the-art solutions, with significantly improved scalability, complete conformity to constraints, less memory consumption, and other desirable features in real applications.

Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation

The exponential growth of unpredictable renewable power production sources in the power grid results in difficult-to-regulate reactive power. The ultimate goal of optimal reactive power dispatch (ORPD) is to find the optimal voltage level of all the generators, the transformer tap ratio, and the MVAR injection of shunt VAR compensators (SVC). More realistically, the ORPD problem is a nonlinear multi-objective optimization problem. Therefore, in this paper, the multi-objective ORPD problem is formulated and solved considering the simultaneous minimization of the active power loss, voltage deviation, emission, and the operating cost of renewable and thermal generators. Usually, renewable power generators such as wind and solar are uncertain; therefore, Weibull and lognormal probability distribution functions are considered to model wind and solar power, respectively. Due to the unavailability and uncertainty of wind and solar power, appropriate PDFs have been used to generate 1000 scenarios with the help of Monte Carlo simulation techniques. Practically, it is not possible to solve the problem considering all the scenarios. Therefore, the scenario reduction technique based on the distance metric is applied to select the 24 representative scenarios to reduce the size of the problem. Moreover, the efficient non-dominated sorting genetic algorithm II-based bidirectional co-evolutionary algorithm (BiCo), along with the constraint domination principle, is adopted to solve the multi-objective ORPD problem. Furthermore, a modified IEEE standard 30-bus system is employed to show the performance and superiority of the proposed algorithm. Simulation results indicate that the proposed algorithm finds uniformly distributed and near-global final non-dominated solutions compared to the recently available state-of-the-art multi-objective algorithms in the literature.

Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology

It is common for people working on linguistic geography, language contact and typology to make use of some type of distance metric between lects. However, most work so far has either used Euclidean distances, or geodesic distance, both of which do not represent the real separation between communities very accurately. This paper presents two datasets: one on walking distances and one on topographic distances between over 8700 lects across all macro-areas. We calculated walking distances using Open Street Maps data, and topographic distances using digital elevation data. We evaluate these distances. We evaluate these distance metrics on three case studies and show that topographic distance tends to outperform the other distance metrics, but geodesic distances can be used as an adequate approximation in some cases.

Waldeyer ring microbiome in relation to chemoradiation-induced oral mucositis in patients with nasopharyngeal carcinoma.

Waldeyer lymphatic ring surrounds the nasopharynx and oropharynx, and no study to date has correlated its microbiome with the severity of oral mucositis (OM) in patients with nasopharyngeal carcinoma (NPC) receiving chemoradiotherapy. We performed 16S rRNA sequencing to characterize bacterial microbiome in tumor-affected nasopharynx and the surrounding normal oropharynx. We plotted the abundance and diversity of bacterial taxa and their phylogenetic distance and networks to visualize and compare the differences in pretreatment overall bacterial communities between the nasopharynx and oropharynx in patients with NPC with varying degrees of chemoradiotherapy-induced OM and quality of life. We found microbial signatures in nasopharynx around NPC were not only dissimilar to those in the surrounding oropharynx but were almost unique to each patient. The genetic distance metrics further showed that different tumor microbiota distributions in the nasopharynx among patients with NPC were well-correlated with OM severity and quality of life during chemoradiotherapy. At Waldeyer ring, the tumor-associated microbiome risk profiles of the respiratory region of nasopharynx, but not commensal microbiota of the alimentary region of oropharynx, could be noninvasive biomarkers for OM susceptibility and might include drug targets for the prevention of chemoradiation-induced OM in patients with Waldeyer ring-derived NPC.

Novel Baseline Facial Muscle Database Using Statistical Shape Modeling and In Silico Trials toward Decision Support for Facial Rehabilitation.

Backgrounds and Objective: Facial palsy is a complex pathophysiological condition affecting the personal and professional lives of the involved patients. Sudden muscle weakness or paralysis needs to be rehabilitated to recover a symmetric and expressive face. Computer-aided decision support systems for facial rehabilitation have been developed. However, there is a lack of facial muscle baseline data to evaluate the patient states and guide as well as optimize the rehabilitation strategy. In this present study, we aimed to develop a novel baseline facial muscle database (static and dynamic behaviors) using the coupling between statistical shape modeling and in-silico trial approaches. Methods: 10,000 virtual subjects (5000 males and 5000 females) were generated from a statistical shape modeling (SSM) head model. Skull and muscle networks were defined so that they statistically fit with the head shapes. Two standard mimics: smiling and kissing were generated. The muscle strains of the lengths in neutral and mimic positions were computed and recorded thanks to the muscle insertion and attachment points on the animated head and skull meshes. For validation, five head and skull meshes were reconstructed from the five computed tomography (CT) image sets. Skull and muscle networks were then predicted from the reconstructed head meshes. The predicted skull meshes were compared with the reconstructed skull meshes based on the mesh-to-mesh distance metrics. The predicted muscle lengths were also compared with those manually defined on the reconstructed head and skull meshes. Moreover, the computed muscle lengths and strains were compared with those in our previous studies and the literature. Results: The skull prediction's median deviations from the CT-based models were 2.2236 mm, 2.1371 mm, and 2.1277 mm for the skull shape, skull mesh, and muscle attachment point regions, respectively. The median deviation of the muscle lengths was 4.8940 mm. The computed muscle strains were compatible with the reported values in our previous Kinect-based method and the literature. Conclusions: The development of our novel facial muscle database opens new avenues to accurately evaluate the facial muscle states of facial palsy patients. Based on the evaluated results, specific types of facial mimic rehabilitation exercises can also be selected optimally to train the target muscles. In perspective, the database of the computed muscle lengths and strains will be integrated into our available clinical decision support system for automatically detecting malfunctioning muscles and proposing patient-specific rehabilitation serious games.

COVID-19 and brain-heart-lung microbial fingerprints in Italian cadavers.

Introduction: The fact that SARS-CoV-2, the coronavirus that caused COVID-19, can translocate within days of infection to the brain and heart and that the virus can survive for months is well established. However, studies have not investigated the crosstalk between the brain, heart, and lungs regarding microbiota that simultaneously co-inhabit these organs during COVID-19 illness and subsequent death. Given the significant overlap of cause of death from or with SARS-CoV-2, we investigated the possibility of a microbial fingerprint regarding COVID-19 death. Methods: In the current study, the 16S rRNA V4 region was amplified and sequenced from 20 COVID-19-positive and 20 non-COVID-19 cases. Nonparametric statistics were used to determine the resulting microbiota profile and its association with cadaver characteristics. When comparing non-COVID-19 infected tissues versus those infected by COVID-19, there is statistical differences (p < 0.05) between organs from the infected group only. Results: When comparing the three organs, microbial richness was significantly higher in non-COVID-19-infected tissues than infected. Unifrac distance metrics showed more variance between control and COVID-19 groups in weighted analysis than unweighted; both were statistically different. Unweighted Bray-Curtis principal coordinate analyses revealed a near distinct two-community structure: one for the control and the other for the infected group. Both unweighted and weighted Bray-Curtis showed statistical differences. Deblur analyses demonstrated Firmicutes in all organs from both groups. Discussion: Data obtained from these studies facilitated the defining of microbiome signatures in COVID-19 decedents that could be identified as taxonomic biomarkers effective for predicting the occurrence, the co-infections involved in its dysbiosis, and the evolution of the virus.

GPU-based similarity metrics computation and machine learning approaches for string similarity evaluation in large datasets

AbstractThe digital era brings up on one hand massive amounts of available data and on the other hand the need of parallel computing architectures for efficient data processing. String similarity evaluation is a processing task applied on large data volumes, commonly performed by various applications such as search engines, biomedical data analysis and even software tools for defending against viruses, spyware, or spam. String similarities are also used in musical industry for matching playlist records with repertory records composed of song titles, performer artists and producers names, aiming to assure copyright protection of mass-media broadcast materials. The present paper proposes a novel GPU-based approach for parallel implementation of the Jaro–Winkler string similarity metric computation, broadly used for matching strings over large datasets. The proposed implementation is applied in musical industry for matching playlist with over 100k records with a given repertory which includes a collection of over 1 million right owner records. The global GPU RAM memory is used to store multiple string lines representing repertory records, whereas single playlist string comparisons with the raw data are performed using the maximum number of available GPU threads and the stride operations. Further on, the accuracy of the Jaro–Winkler approach for the string matching procedure is increased using both an adaptive neural network approach guided by a novelty detection classifier (aNN) and a multiple-features neural network implementation (MF-NN). Thus, the aNN approach yielded an accuracy of 92% while the MF-NN approach achieved an accuracy of 99% at the cost of increased computational complexity. Timing considerations and the computational complexity are detailed for the proposed approaches compared with both the general-purpose processor (CPU) implementation and the state-of-the-art GPU approaches. A speed-up factor of 21.6 was obtained for the GPU-based Jaro–Winkler implementation compared with the CPU one, whereas a factor of 3.72 was obtained compared with the existing GPU implementation of string matching procedure based on Levenstein distance metrics.

Ghost: A General Framework for High-Performance Online Similarity Queries over Distributed Trajectory Streams

Trajectory similarity queries, including similarity search and similarity join, offer a foundation for many geo-spatial applications. With the rapid increase of streaming trajectory data volumes, e.g., data from mobile phones, vessel monitoring, or traffic systems, many location-based services benefit from online similarity analytics over trajectory data streams, where moving objects continually emit real-time position data. However, most existing studies focus on offline settings, and thus several major challenges remain unanswered in an online setting. To this end, we describe Ghost, a distributed stream processing framework that enables generic, efficient, and scalable online trajectory similarity search and join. We propose a novel incremental online similarity computation (IOSC) mechanism to accelerate pair-wise streaming trajectory distance calculation, which supports a broad range of trajectory distance metrics. Compared with previous studies, IOSC reduces the complexity from quadratic to linear in terms of trajectory length. Building on this foundation, we propose histogram-based algorithms that exploit histogram indexes and a series of pruning bounds to enable streaming trajectory similarity search and join. Finally, we extend our methods to the distributed platform Flink for scalability, where a CostPartitioner is developed to ensure parallel processing and workload balancing. An experimental study using two real-life and one synthetic datasets shows that Ghost (i) acquires 6-20× efficiency/throughput gains and one order of magnitude memory overhead savings over state-of-the-art baselines, (ii) achieves 3--8× workload balancing gains on Flink, and (iii) exhibits low parameter sensitivity and high robustness.

Uncertainty quantification and attention-aware fusion guided multi-modal MR brain tumor segmentation

Brain tumor is one of the most aggressive cancers in the world, accurate brain tumor segmentation plays a critical role in clinical diagnosis and treatment planning. Although deep learning models have presented remarkable success in medical segmentation, they can only obtain the segmentation map without capturing the segmentation uncertainty. To achieve accurate and safe clinical results, it is necessary to produce extra uncertainty maps to assist the subsequent segmentation revision. To this end, we propose to exploit the uncertainty quantification in the deep learning model and apply it to multi-modal brain tumor segmentation. In addition, we develop an effective attention-aware multi-modal fusion method to learn the complimentary feature information from the multiple MR modalities. First, a multi-encoder-based 3D U-Net is proposed to obtain the initial segmentation results. Then, an estimated Bayesian model is presented to measure the uncertainty of the initial segmentation results. Finally, the obtained uncertainty maps are integrated into a deep learning-based segmentation network, serving as an additional constraint information to further refine the segmentation results. The proposed network is evaluated on publicly available BraTS 2018 and BraTS 2019 datasets. The experimental results demonstrate that the proposed method outperforms the previous state-of-the-art methods on Dice score, Hausdorff distance and Sensitivity metrics. Furthermore, the proposed components could be easily applied to other network architectures and other computer vision fields.

Augmented-reality swim goggles accurately and reliably measure swim performance metrics in recreational swimmers.

Swimmers commonly access performance metrics such as lap splits, distance, and pacing information between work bouts while they rest. Recently, a new category of tracking devices for swimming was introduced with the FORM Smart Swim Goggles (FORM Goggles). The goggles have a built-in see-through display and are capable of tracking and displaying distance, time splits, stroke, and pace metrics in real time using machine learning and augmented reality through a heads-up display. The purpose of this study was to assess the validity and reliability of the FORM Goggles compared with video analysis for stroke type, pool length count, pool length time, stroke rate, and stroke count in recreational swimmers and triathletes. A total of 36 participants performed mixed swimming intervals in a 25-m pool across two identical 900-m swim sessions performed at comparable intensities with 1 week interval. The participants wore FORM Goggles during their swims, which detected the following five swim metrics: stroke type, pool length time, pool length count, stroke count, and stroke rate. Four video cameras were positioned on the pool edges to capture ground truth video footage, which was then manually labeled by three trained individuals. Mean (SD) differences between FORM Goggles and ground truth were calculated for the selected metrics for both sessions. The absolute mean difference and mean absolute percentage error were used to assess the differences of the FORM Goggles relative to ground truth. The test-retest reliability of the goggles was assessed using both relative and absolute reliability metrics. Compared with video analysis, the FORM Goggles identified the correct stroke type at a rate of 99.7% (N = 2,354 pool lengths, p < 0.001), pool length count accuracy of 99.8%, and mean differences (FORM Goggles-ground truth) for pool length time: -0.10 s (1.49); stroke count: -0.63 (1.82); and stroke rate: 0.19 strokes/min (3.23). The test-retest intra-class correlation coefficient (ICC) values between the two test days were 0.793 for pool length time, 0.797 for stroke count, and 0.883 for stroke rate. Overall, for pool length time, the residuals were within ±1.0s for 65.3% of the total pool lengths, for stroke count within ±1 stroke for 62.6% of the total pool lengths, and for stroke rate within ±2 strokes/min for 66.40% of the total pool lengths. The FORM Goggles were found valid and reliable for the tracking of pool length time, pool length count, stroke count, stroke rate, and stroke type during freestyle, backstroke, and breaststroke swimming in recreational swimmers and triathletes when compared with video analysis. This opens perspectives for receiving real-time information on performance metrics during swimming.

Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods.

The identity of sensory stimuli is encoded in the spatio-temporal patterns of responses of the encoding neural population. For stimuli to be discriminated reliably, differences in population responses must be accurately decoded by downstream networks. Several methods to compare patterns of responses have been used by neurophysiologists to characterize the accuracy of the sensory responses studied. Among the most widely used analyses, we note methods based on Euclidean distances or on spike metric distances. Methods based on artificial neural networks and machine learning that recognize and/or classify specific input patterns have also gained popularity. Here, we first compare these three strategies using datasets from three different model systems: the moth olfactory system, the electrosensory system of gymnotids, and leaky-integrate-and-fire (LIF) model responses. We show that the input-weighting procedure inherent to artificial neural networks allows the efficient extraction of information relevant to stimulus discrimination. To combine the convenience of methods such as spike metric distances but leverage the advantages of weighting the inputs, we propose a measure based on geometric distances where each dimension is weighted proportionally to how informative it is. We show that the result of this Weighted Euclidian Distance (WED) analysis performs as well or better than the artificial neural network we tested and outperforms the more traditional spike distance metrics. We applied information theoretic analysis to LIF responses and compared their encoding accuracy with the discrimination accuracy quantified through this WED analysis. We show a high degree of correlation between discrimination accuracy and information content, and that our weighting procedure allowed the efficient use of information present to perform the discrimination task. We argue that our proposed measure provides the flexibility and ease of use sought by neurophysiologists while providing a more powerful way to extract relevant information than more traditional methods.