Spiking neural networks (SNNs) receive trains of 1 spiking events as inputs. In order to design efficient SNN systems, 2 real-valued signals must be optimally encoded into spike trains 3 so that the task-relevant information is retained. This paper 4 provides a systematic quantitative and qualitative analysis and 5 guidelines for optimal temporal encoding. It proposes a method-6 ology of a three-step encoding workflow: method selection by 7 signal characteristics, parameter optimization by error metrics 8 between original and reconstructed signals, and validation by 9 comparison of the original signal and the encoded spike train. 10 Four encoding methods are analyzed: one stimulus estimation 11 [Ben's Spiker algorithm (BSA)] and three temporal contrast 12 [threshold-based, step-forward (SW), and moving-window (MW)] 13 encodings. A short theoretical analysis is provided, and the 14 extended quantitative analysis is carried out applying four types 15 of test signals: step-wise signal, smooth (sinusoid) signal with 16 added noise, trended smooth signal, and event-like smooth signal. 17 Various time-domain and frequency spectrum properties are 18 explored, and a comparison is provided. BSA, the only method 19 providing unipolar spikes, was shown to be ineffective for 20 step-wise signals, but it can follow smoothly changing signals 21 if filter coefficients are scaled appropriately. Producing bipolar 22 (positive and negative) spike trains, SW encoding was most 23 effective for all types of signals as it proved to be robust and easy 24 to optimize. Signal-to-noise ratio (SNR) can be recommended as 25 the error metric for parameter optimization. Currently, only a 26 visual check is available for final validation.

BackgroundStatic balancing assessment is often complemented with dynamic balancing tasks. Numerous dynamic balancing assessment methods have been developed in recent decades with their corresponding balancing devices and tasks.ObjectiveThe aim of this systematic literature review is to identify and categorize existing objective methods of standing dynamic balancing ability assessment with an emphasis on the balancing devices and tasks being used.Data SourcesThree major scientific literature databases (Science Direct, Web of Science, PLoS ONE) and additional sources were used.Study selectionStudies had to use a dynamic balancing device and a task described in detail. Evaluation had to be based on objectively measureable parameters. Functional tests without instrumentation evaluated exclusively by a clinician were excluded. A total of 63 articles were included.Data extractionThe data extracted during full-text assessment were: author and date; the balancing device with the balancing task and the measured parameters; the health conditions, size, age and sex of participant groups; and follow-up measurements.Data synthesisA variety of dynamic balancing assessment devices were identified and categorized as 1) Solid ground, 2) Balance board, 3) Rotating platform, 4) Horizontal translational platform, 5) Treadmill, 6) Computerized Dynamic Posturography, and 7) Other devices. The group discrimination ability of the methods was explored and the conclusions of the studies were briefly summarized.LimitationsDue to the wide scope of this search, it provides an overview of balancing devices and do not represent the state-of-the-art of any single method.ConclusionsThe identified dynamic balancing assessment methods are offered as a catalogue of candidate methods to complement static assessments used in studies involving postural control.

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Basketball is one of the most popular team sports in the world. A wide variety of athletic skills can be valuable indicators of a talented player. Testing these skills help trainers and scouts to make the best decisions during both youth and adult player selection of different competitive levels and field positions. However, scientific data regarding the association between field tests results and match statistics is sparse. We conducted a systematic review to logically summarize the physical field tests of athletic abilities and anthropometric measures used in basketball in different player positions at different levels, and to determine whether test results may correlate with match statistics. A comprehensive electronic literature search was performed via three electronic databases (PubMed, GoogleScholar, SportDiscus). The CASP checklist was used for checking the quality of reporting for all included articles. Based on 39 studies, it could be supported that the agility, speed and jumping test values seem to be crucial measures for basketball players. Anthropometry appeared to be the strongest discriminative factor between basketball positions, therefore, these values need to be significantly considered during selection. Moreover, the maturity status is also an important influencing factor for U-18 teams. In general, we found that successful players can be identified by their nonplanned agility and reactive power, considering that these factors affect match outcomes the most at the same competitive level.

We conducted a review to collect the validated basketball-specific physical field tests and to provide practical advice for their appropriate selection and application. A comprehensive electronic literature search was performed via three electronic databases (PubMed, GoogleScholar, and SportDiscuss). Results of 93 studies provided recommendations for seven test packages and eighteen individual tests that have already been validated for basketball players. Although there is a lack of standardized, widely, and systematically used test protocols for testing the fitness levels of basketball players, standardized, normative data from NBA Combine Testing and other basketball-specific tests have the potential to help coaches compare their players with elite basketball players. Our review indicated that agility and reactive agility are fundamental skills in basketball; however, linear sprinting ability should not be considered a determining factor of success for basketball players. Finally, the countermovement jump test can help experts monitor fatigue, loss of explosive force, and interlimb asymmetries. In general, we found that identifying and developing a talented player is a complex task and requires experts from different fields, including trainers, coaches, performance- and movement analyzers, and physiotherapists. We found that during the testing of basketball players, experts always have to normalize their data with anthropometric measures for valid results. Most importantly, although experts always need to define an aim of testing and should follow the protocol of the chosen test, they also have to be open to making adjustments if the actual circumstances require it.

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We examined the effects of side-dominance on the laterality of standing stability using ground reaction force, motion capture (MoCap), and EMG data in healthy young adults. We recruited participants with strong right (n = 15) and left (n = 9) hand and leg dominance (side-dominance). They stood on one or two legs on a pair of synchronized force platforms for 50 s with 60 s rest between three randomized stance trials. In addition to 23 CoP-related variables, we also computed six MoCap variables representing each lower-limb joint motion time series. Moreover, 39 time-and frequency-domain features of EMG data from five muscles in three muscle groups were analyzed. Data from the multitude of biosignals converged and revealed concordant patterns: no differences occurred between left-and right-side dominant participants in kinetic, kinematic, or EMG outcomes during bipedal stance. Regarding single

Background The prone plank test has been often used to assess the strength and endurance of trunk muscles. We aimed to develop a new measurement protocol to objectively monitor the changes in spinal curves and muscle activity simultaneously. Methods Eleven adolescent male basketball athletes (13–17 years) performed a one-minute plank test. Spinal curvatures (thoracic kyphosis (TK) and lumbar lordosis (LL)) were determined at each time point by optical tracking of markers placed on the spinous processes of 10 vertebrae. Eleven muscles were measured by surface electromyography to determine muscle fatigue via changes in median frequency. Results TK significantly increased (p = 0.003) from the first to the last 10 s of the plank test; changes in LL were mixed within the group. Only the rectus abdominis showed consistent and significant fatigue (p < 0.001). The increased spinal curves significantly correlated with the fatigue of biceps femoris (TK: r = -0.75, p = 0.012; LL: r = -0.71, p = 0.019) indicating a compensatory muscle activation and spinal curve changes in response to fatigue. Conclusion Our protocol may support future researches that aim to objectively evaluate the prone plank test and which posture-related muscles need strengthening for the individual.