Action classification and analysis during sports training session using fuzzy model and video surveillance

Activity recognition and classification are emerging fields of research that enable many human-centric applications in the sports domain. One of the most critical and challenged aspects of coaching is improving the performance of athletes. Hence, in this paper, the Adaptive Evolutionary Neuro-Fuzzy Inference System (AENFIS) has been proposed for sports person activity classification based on the biomedical signal, trial accelerator data and video surveillance. This paper obtains movement data and heart rate from the developed sensor module. This small sensor is patched onto the user’s chest to get physiological information. Based on the time and frequency domain features, this paper defines the fuzzy sets and assess the natural grouping of data via expectation-maximization of the probabilities. Sensor data feature selection and classification algorithms are applied, and a majority voting is utilized to choose the most representative features. The experimental results show that the proposed AENFIS model enhances accuracy ratio of 98.9%, prediction ratio of 98.5%, the precision ratio of 95.4, recall ratio of 96.7%, the performance ratio of 97.8%, an efficiency ratio of 98.1% and reduces the error rate of 10.2%, execution time 8.9% compared to other existing models.

Rethinking assumptions about how trial and nuisance variability impact neural task performance in a fast-processing regime

Task performance is determined not only by the amount of task-relevant signal present in our brains but also by the presence of noise, which can arise from multiple sources. Internal noise, or “trial variability,” manifests as trial-by-trial variations in neural responses under seemingly identical conditions. External factors can also translate into noise, particularly when a task requires extraction of a particular type of information from our environment amid changes in other task-irrelevant “nuisance” parameters. To better understand how signal, trial variability, and nuisance variability combine to determine neural task performance, we explored their interactions, both in simulation and when applied to recorded neural data. This exploration revealed that trial variability is typically larger than a neuron’s task-relevant signal for tasks with fast reaction times, where spike count integration windows are short. In this low signal-to-trial variability regime, nuisance variability has the counterintuitive property of having a negligible impact on single-neuron task performance, even when it dominates the task-relevant signal. The inconsequential impact of nuisance variability on individual neurons also extends to descriptions of population performance, under the assumption that both trial and nuisance variability are uncorrelated between neurons. These results demonstrate that some basic intuitions about neural coding are misguided in the context of a fast-processing, low-spike-count regime. NEW & NOTEWORTHY Many everyday tasks require us to extract specific information from our environment while ignoring other things. When the neurons in our brains that carry task-relevant signals are also modulated by task-irrelevant “nuisance” information, nuisance modulation is expected to act as performance-limiting noise. Using both simulated and recorded neural data, we demonstrate that these intuitions are misguided when the brain operates in a fast-processing, low-spike-count regime, where nuisance variability is largely inconsequential for performance.

Neural populations in human posteromedial cortex display opposing responses during memory and numerical processing

Our understanding of the human default mode network derives primarily from neuroimaging data but its electrophysiological correlates remain largely unexplored. To address this limitation, we recorded intracranially from the human posteromedial cortex (PMC), a core structure of the default mode network, during various conditions of internally directed (e.g., autobiographical memory) as opposed to externally directed focus (e.g., arithmetic calculation). We observed late-onset (>400 ms) increases in broad high γ-power (70–180 Hz) within PMC subregions during memory retrieval. High γ-power was significantly reduced or absent when subjects retrieved self-referential semantic memories or responded to self-judgment statements, respectively. Conversely, a significant deactivation of high γ-power was observed during arithmetic calculation, the duration of which correlated with reaction time at the signal-trial level. Strikingly, at each recording site, the magnitude of activation during episodic autobiographical memory retrieval predicted the degree of suppression during arithmetic calculation. These findings provide important anatomical and temporal details—at the neural population level—of PMC engagement during autobiographical memory retrieval and address how the same populations are actively suppressed during tasks, such as numerical processing, which require externally directed attention.

Role of Supplementary Eye Field in Saccade Initiation: Executive, Not Direct, Control

The goal of this study was to determine whether the activity of neurons in the supplementary eye field (SEF) is sufficient to control saccade initiation in macaque monkeys performing a saccade countermanding (stop signal) task. As previously observed, many neurons in the SEF increase the discharge rate before saccade initiation. However, when saccades are canceled in response to a stop signal, effectively no neurons with presaccadic activity display discharge rate modulation early enough to contribute to saccade cancellation. Moreover, SEF neurons do not exhibit a specific threshold discharge rate that could trigger saccade initiation. Yet, we observed more subtle relations between SEF activation and saccade production. The activity of numerous SEF neurons was correlated with response time and varied with sequential adjustments in response latency. Trials in which monkeys canceled or produced a saccade in a stop signal trial were distinguished by a modest difference in discharge rate of these SEF neurons before stop signal or target presentation. These findings indicate that neurons in the SEF, in contrast to counterparts in the frontal eye field and superior colliculus, do not contribute directly and immediately to the initiation of visually guided saccades. However the SEF may proactively regulate saccade production by biasing the balance between gaze-holding and gaze-shifting based on prior performance and anticipated task requirements.