A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives.

Predictive sports data analytics can be revolutionary for sports performance. Existing literature discusses players' or teams' performance, independently or in tandem. Using Machine Learning (ML), this paper aims to holistically evaluate player-, team-, and conference (season)-level performances in Division-1 Women's basketball. The players were monitored and tested through a full competitive year. The performance was quantified at the player level using the reactive strength index modified (RSImod), at the team level by the game score (GS) metric, and finally at the conference level through Player Efficiency Rating (PER). The data includes parameters from training, subjective stress, sleep, and recovery (WHOOP straps), in-game statistics (Polar monitors), and countermovement jumps. We used data balancing techniques and an Extreme Gradient Boosting (XGB) classifier to predict RSI and GS with greater than 90% accuracy and a 0.9 F1 score. The XGB regressor predicted PER with an MSE of 0.026 and an R2 of 0.680. Ensemble of Random Forest, XGB, and correlation finds feature importance at all levels. We used Partial Dependence Plots to understand the impact of each feature on the target variable. Quantifying and predicting performance at all levels will allow coaches to monitor athlete readiness and help improve training.

Sleep and Physical Performance: A Case Study of Collegiate Women's Division 1 Basketball Players.

In this work, we present a case study to evaluate the connections between sleep, training load, and the perceptions of physical/emotional state of a collegiate, division 1 Women's basketball team. The study took place during the off- (3 weeks) and pre-season (6 weeks) while sleep was tracked using WHOOP wearable straps. Training load was recorded by the strength coach and athletes. Short Recovery and Short Stress (SRSS) questionnaire was used to evaluate the perceptions of athletes on their own emotional and physical states. Our results showed that heart rate measurements are associated with stress levels and recovery perception. We also discovered that the training load was not linked to the sleep variables without the considerations of athletic performance. However, training load may alter perceived stress and recovery which requires further exploration.

Gait Study of Parkinson's Disease Subjects Using Haptic Cues with A Motorized Walker.

Gait abnormalities are one of the distinguishing symptoms of patients with Parkinson's disease (PD) that contribute to fall risk. Our study compares the gait parameters of people with PD when they walk through a predefined course under different haptic speed cue conditions (1) without assistance, (2) pushing a conventional rolling walker, and (3) holding onto a self-navigating motorized walker under different speed cues. Six people with PD were recruited at the New York Institute of Technology College of Osteopathic Medicine to participate in this study. Spatial posture and gait data of the test subjects were collected via a VICON motion capture system. We developed a framework to process and extract gait features and applied statistical analysis on these features to examine the significance of the findings. The results showed that the motorized walker providing a robust haptic cue significantly improved gait symmetry of PD subjects. Specifically, the asymmetry index of the gait cycle time was reduced from 6.7% when walking without assistance to 0.56% and below when using a walker. Furthermore, the double support time of a gait cycle was reduced by 4.88% compared to walking without assistance.

A low-cost, scalable, current-sensing digital headstage for high channel count µECoG.

OBJECTIVE: High channel count electrode arrays allow for the monitoring of large-scale neural activity at high spatial resolution. Implantable arrays featuring many recording sites require compact, high bandwidth front-end electronics. In the present study, we investigated the use of a small, light weight, and low cost digital current-sensing integrated circuit for acquiring cortical surface signals from a 61-channel micro-electrocorticographic (µECoG) array. APPROACH: We recorded both acute and chronic µECoG signal from rat auditory cortex using our novel digital current-sensing headstage. For direct comparison, separate recordings were made in the same anesthetized preparations using an analog voltage headstage. A model of electrode impedance explained the transformation between current- and voltage-sensed signals, and was used to reconstruct cortical potential. We evaluated the digital headstage using several metrics of the baseline and response signals. MAIN RESULTS: The digital current headstage recorded neural signal with similar spatiotemporal statistics and auditory frequency tuning compared to the voltage signal. The signal-to-noise ratio of auditory evoked responses (AERs) was significantly stronger in the current signal. Stimulus decoding based on true and reconstructed voltage signals were not significantly different. Recordings from an implanted system showed AERs that were detectable and decodable for 52 d. The reconstruction filter mitigated the thermal current noise of the electrode impedance and enhanced overall SNR. SIGNIFICANCE: We developed and validated a novel approach to headstage acquisition that used current-input circuits to independently digitize 61 channels of µECoG measurements of the cortical field. These low-cost circuits, intended to measure photo-currents in digital imaging, not only provided a signal representing the local cortical field with virtually the same sensitivity and specificity as a traditional voltage headstage but also resulted in a small, light headstage that can easily be scaled to record from hundreds of channels.

Classification and visualization tool for gait analysis of Parkinson's disease.

There is no standard diagnostic test for Parkinson's Disease. In this paper, we propose a data mining based statistical diagnostic method towards a standard and accurate diagnostic test. The result shows the proposed regression formula, which only requires patient's stride data, can provide a 90% accuracy to diagnose PD patients.

EEG analysis via multiscale Lempel-Ziv complexity for seizure detection.

Robust seizure detection and seizure prediction continues to be a challenge. Lempel-Ziv Complexity (LZC) is one of the features that has shown to be relevant in seizure detection. Recent work has shown that augmenting LZC can be beneficial to emphasize variations in amplitude or frequency when analyzing biomedical signals. In this paper, we present a first look into evaluating the feasibility of using a recently proposed feature stemmed from LZC, namely the Multiscale Lempel-Ziv Complexity (MLZC) for seizure detection. MLZC does not allow the high-frequency signal components to be overwhelmed by the low frequency signal components when calculating complexity values. We compare MLZC and LZC for identifying seizures for three cases and show MLZC can provide a clear separation between non-ictal and ictal periods for all three cases using a single threshold over 7 recordings and 7 seizures per patient, whereas LZC provided such a clear separation for only one of the patients.

Seizure detection methods using a cascade architecture for real-time implantable devices.

Implantable high-accuracy, and low-power seizure detection is a challenge. In this paper, we propose a cascade architecture to combine different seizure detection algorithms to optimize power and accuracy of the overall seizure detection system. The proposed architecture consists of a cascade of two seizure detection stages. In the first-stage detector, a lightweight (low-power) algorithm is used to detect seizure candidates with the understanding that there will be a high number of false positives. In the second-stage detector-and only for the seizure candidates detected in the first detector-a high-accuracy algorithm is used to eliminate the false positives. We show that the proposed cascade architecture can reduce power consumption of seizure detection by 80% with high accuracy, offering a suitable option for real-time implantable seizure detectors.

Compression-ratio-based seizure detection.

For wireless seizure monitoring devices seizure detection and data compression are two critical tasks that need to be carefully designed against a very tight power budget to maximize the battery life. These two tasks are usually considered separately and algorithms for each are developed separately. In this paper, we consider having a single low-power algorithm for implementing both seizure detection and data compression. Towards that end, we investigated compression ratio (CR) as a seizure marker and show that the seizure detection can be achieved as a by-product of compression with no additional cost, and thus overall system power can be reduced. We show that the proposed method, the CR-based seizure detection has promising performance with 88% seizure detection accuracy, and 5.5 false positives per hour (FPh) without any computation overhead.

Spatiotemporal compression for efficient storage and transmission of high-resolution electrocorticography data.

High-resolution Electrocorticography (HR-ECoG) has emerged as a key strategic technology for recording localized neural activity with high temporal and spatial resolution with potential applications in brain-computer interfaces (BCI), and seizure detection for epilepsy. However, HR-ECoG has 400 times the resolution of conventional ECoG, making it a challenge to process, transmit and store the HR-ECoG data. Therefore, simple and efficient compression algorithms are vital for the feasibility of implantable wireless medical devices for HR-ECoG recordings. In this paper, following the observation that HR-ECoG signals have both high spatial and temporal correlations similar to video/image signals, various compression methods suitable for video/image- compression based on motion estimation, discrete cosine transform (DCT) and discrete wavelet transform (DWT)- are investigated for compressing HR-ECoG data. We first simplify these methods to satisfy the low-power requirements for implantable devices. Then, we demonstrate that spatiotemporal compression methods produce up to 46% more data reduction on HR-ECoG data than compression methods using only spatial compression do. We further show that this data reduction can be achieved with low hardware complexity. In particular, among the methods investigated, spatiotemporal compression using DCT-based methods provide the best trade-off between hardware complexity and compression performance, and thus we conclude that DCT-based compression is a promising solution for ultralow-power implantable devices for HR-ECoG.

Optimizing analog-to-digital converters for sampling extracellular potentials.

In neural implants, an analog-to-digital converter (ADC) provides the delicate interface between the analog signals generated by neurological processes and the digital signal processor that is tasked to interpret these signals for instance for epileptic seizure detection or limb control. In this paper, we propose a low-power ADC architecture for neural implants that process extracellular potentials. The proposed architecture uses the spike detector that is readily available on most of these implants in a closed-loop with an ADC. The spike detector determines whether the current input signal is part of a spike or it is part of noise to adaptively determine the instantaneous sampling rate of the ADC. The proposed architecture can reduce the power consumption of a traditional ADC by 62% when sampling extracellular potentials without any significant impact on spike detection accuracy.

High-efficiency wireless power delivery for medical implants using hybrid coils.

With the exciting developments in the implant technology allowing sophisticated signal processing, stimulation, and drug delivery capabilities, there is new hope for many patients of epilepsy, Parkinson's disease, and stroke to improve their quality of life. Such implants require high power to deliver the promised rich functionality. Yet, delivering high power to implants without damaging the tissue due to heating while keeping the implant footprint small is a challenge. In this paper, we propose a hybrid multi-layer coil as the secondary coil to provide a power and space-efficient solution. The proposed coils can deliver power to an implant for long durations without increasing the skin temperature over 1C.

Experimental evaluation and model assessment of coexistence of PAOs and GAOs.

The study evaluated the competition and co-existence of PAOs and GAOs in sequencing batch reactor (SBR) systems sustaining enhanced biological phosphorus removal (EBPR). The SBR operation used acetate as the sole external carbon source and covered a wide range of initial COD/P ratios between 6.5 to 25.9 g COD/g P. A mechanistic model, ASMGG, was adopted for this purpose, which basically incorporated model components and processes associated with GAO metabolism and glycogen metabolism of PAOs. Model calibration was successfully performed with the same set of stoichiometric and kinetic coefficients for all the acetate, phosphate, glycogen and PHA profiles obtained in different experiments. Interpretation of experimental results by means of model simulation indicated competition and co-existence of PAOs and GAOs within the EBPR process, numerically assessing the composition of the microbial community sustained and identifying the respective role and function of PAOs and GAOs on the fate of glycogen and PHA.

Transmeningeal muscimol can prevent focal EEG seizures in the rat neocortex without stopping multineuronal activity in the treated area.

Muscimol has potent antiepileptic efficacy after transmeningeal administration in animals. However, it is unknown whether this compound stops local neuronal firing at concentrations that prevent seizures. The purpose of this study was to test the hypothesis that epidurally administered muscimol can prevent acetylcholine (Ach)-induced focal seizures in the rat neocortex without causing cessation of multineuronal activity. Rats were chronically implanted with a modified epidural cup over the right frontal cortex, with microelectrodes positioned underneath the cup. In each postsurgical experimental day, either saline or 0.005-, 0.05-, 0.5- or 5.0-mM muscimol was delivered through the cup, followed by a 20-min monitoring of the multineuronal activity and the subsequent delivery of Ach in the same way. Saline and muscimol pretreatment in the concentration range of 0.005-0.05 mM did not prevent EEG seizures. In contrast, 0.5-mM muscimol reduced the average EEG Seizure Duration Ratio value from 0.30±0.04 to 0. At this muscimol concentration, the average baseline multineuronal firing rate of 10.9±4.4 spikes/s did not change significantly throughout the 20-min pretreatment. Muscimol at 5.0mM also prevented seizures, but decreased significantly the baseline multineuronal firing rate of 7.0±1.8 to 3.7±0.9 spikes/s in the last 10 min of pretreatment. These data indicate that transmeningeal muscimol in a submillimolar concentration range can prevent focal neocortical seizures without stopping multineuronal activity in the treated area, and thus this treatment is unlikely to interrupt local physiological functions.

Multi-layer coils for efficient Transcutaneous Power Transfer.

TETS (Transcutaneous Energy Transfer System) has been successfully used for powering medical implants for different purposes such as for neural recordings and drug delivery. Yet, due to their low power transfer efficiency, these devices can cause unacceptable increase in skin temperature limiting their scalability to high power levels. Although, the efficiency of these systems can be improved by increasing coil diameter, in many cases this is not practical due to strict physical constraints on the coil diameter. In this paper, we investigate using multi-layer coils as secondary coils in the TETS to increase the power transfer efficiency, and thus allowing the delivery of the desired power safely for a longer period. Our experiments show a 5× increase in the duration of safe power delivery (not increasing the skin temperature more than 2 C) using multi-layer coils as the secondary coil compared to using single-layer coils even when there is a 50% misalignment in between primary and secondary coils. This increase in duration of safe power transfer is shown to be over 16× more when the coils are aligned. The improvement in the duration of safe power transfer is achieved without increasing the coil diameter and with a coil thickness of 2 mm.

A high-performance transcutaneous battery charger for medical implants.

As new functionality is added to the implantable devices, their power requirements also increase. Such power requirements make it hard for keeping such implants operational for long periods by non-rechargeable batteries. This result in a need for frequent surgeries to replace these batteries. Rechargeable batteries can satisfy the long-term power requirements of these new functions. To minimize the discomfort to the patients, the recharging of the batteries should be as infrequent as possible. Traditional battery charging methods have low battery charging efficiency. This means they may limit the amount of charge that can be delivered to the device, speeding up the depletion of the battery and forcing frequent recharging. In this paper, we evaluate the suitability of a state-of-the-art general purpose charging method called current-pumped battery charger (CPBC) for implant applications. Using off-the-shelf components and with minimum optimization, we prototyped a proof-of-concept transcutaenous battery charger based on CPBC and show that the CPBC can charge a 100 mAh battery transcutaneously within 137 minutes with at most 2.1°C increase in tissue temperature even with a misalignment of 1.3 cm in between the coils, while keeping the battery charging efficiency at 85%.

Effect of anoxic decay process on simultaneous nitrification denitrification in a membrane bioreactor operated without an anoxic tank.

This study was focused on evaluating the role and the effect of anoxic decay on the extent of simultaneous nitrification-denitrification (SNdN) process sustained in a single membrane bioreactor. The membrane bioreactor was fed with relatively strong domestic sewage and operated at steady state at a sludge age of 36 days at a corresponding suspended solids level maintained in the range of 17,500-21,000 mg/L. The SNdN could be sustained due to diffusion limitation of oxygen into the flocs. The evaluation identified an MLSS threshold level of around 17,000-18,000 mg/L below which nitrogen removal was essentially controlled by denitrification and above, the rate limiting mechanism shifted to nitrification maintaining total nitrogen removal efficiency of 85-95% for a typical domestic sewage. The contribution of anoxic decay process to the overall denitrification potential was evaluated as 60%, substantially higher than the remaining 40% associated with the anoxic growth during the SNdN process.

How does shear affect aggregation in granular sludge sequencing batch reactors? Relations between shear, hydrophobicity, and extracellular polymeric substances.

The objective was to provide an answer to "how to grow/survive in aggregative physiology" through evaluating the relation between physical stress and observed biomass characteristics. For that, a lab-scale sequencing batch reactor was operated at an anaerobic-aerobic mode and under altered hydraulic selection pressures of settling time (10-1 min) and hydrodynamic shear rates due to mechanical mixing (15.5-12.0 cm/s) and/or aeration (1.76-0.24 cm/s). Main physical stress experienced by the biomass was mechanical mixing, which resulted in extreme shearing conditions at the first operational stage (days 1-86), during which first granules formed but settling properties deteriorated and biomass was almost totally washed out. After relaxing the overall shear stress at the second stage, biomass formation accelerated, settling properties enhanced and granulation proceeded (days 86-136), until disturbance of the process at the last month of operation (days 136-163). Aggregative physiology-related parameters, being cell surface hydrophobicity and extracellular polymeric substances (EPS), followed increasing trends parallel to the progress of granulation, and then decreased upon disturbance of the process. There was an increase in the EPS production also during the first stage under extreme shear, while a substantial amount of biomass was present in the system. A direct correlation was also found between %hydrophobicity and EPS-composition expressed as ExoPN/ExoPS.

Modelling of long-term simultaneous nitrification and denitrification (SNDN) performance of a pilot scale membrane bioreactor.

Nutrient removal capability of the MBR process has attracted more attention than organics removal in the past few years. Apart from the conventional schemes for nitrogen removal in MBR process, simultaneous nitrification-denitrification (SNDN) requires the most attention for further research. In order to fully understand the fundemantals and mechanism of SNDN in MBRs, a pilot plant was set up. A mathematical model was adopted for investigation and calibration against the observed values. This paper reports a study focusing on evaluating major mechanisms that govern nitrogen removal from domestic wastewater in membrane bioreactors. Two items need to be emphasized in this evaluation: (i) an MBR is basically regarded as an activated sludge process-a suspended growth bioreactor with total biomass recycle and substantially higher biomass concentration; (ii) in this context an AS model, namely ASM1R modified for endogenous respiration, is used for dynamic modelling and calibration of experimental results. The impact of diffusion through biomass which obviously exerts a significant effect on system performance and denitrification is evaluated with success using the adopted model by means of switch functions that regulate nitrification-denitrification with respect to dissolved oxygen concentration in the bulk liquid.

Performance evaluation of SBR treatment for nitrogen removal from tannery wastewater.

Performance of SBR treatment for nitrogen removal from tannery is evaluated for a wide range of wastewater temperature between 7 and 30 degrees C. A pilot-scale SBR unit fed with plain-settled wastewater is operated on site for this purpose. Effective nitrogen removal is sustained by adjustment of the sludge age from 28 to 5 days. Concentration profiles of nitrogen compounds within a selected complete SBR cycle during the steady state operation at different wastewater temperatures and sludge ages are evaluated by model simulation. System performance is also interpreted in terms of modeling and stoichiometric calculation. Additional nitrate loss was observed during aerobic period when the aeration intensity was reduced by the factor of 50%.

Effect of primary sludge fermentation products on mass balance for biological treatment.

Laboratory batch experiments were conducted at 20 degrees C to investigate the potential of primary sludge fermentation for the generation of readily biodegradable substrate and to evaluate the effect of fermentation products on mass balance for organic carbon, nitrogen and phosphorus, emphasizing COD fractionation. Fermentation converted between 18 to 30% of the initial volatile suspended solids in the sludge into soluble biodegradable COD. The volatile fatty fraction of the soluble COD was approximately 85% after the fermentation process. The average volatile fatty acid composition in fermentation involved 50% acetic acid, 33% propionic acid, 9% butyric acid and 8% valeric acid, indicating that the most important volatile fatty acid obtained during the biological fermentation process was acetate with more than half of total VFA concentration, which is one of the most important carbon sources for denitrification and biological nutrient removal processes. The recoverable fraction of the fermented sludge supernatant could potentially increase the readily biodegradable COD content of the primary effluent by 5%, together with a potential increase of the soluble nitrogen and phosphorus content by 2%.