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.

A Real-Time Wireless Sweat Rate Measurement System for Physical Activity Monitoring.

There has been significant research on the physiology of sweat in the past decade, with one of the main interests being the development of a real-time hydration monitor that utilizes sweat. The contents of sweat have been known for decades; sweat provides significant information on the physiological condition of the human body. However, it is important to know the sweat rate as well, as sweat rate alters the concentration of the sweat constituents, and ultimately affects the accuracy of hydration detection. Towards this goal, a calorimetric based flow-rate detection system was built and tested to determine sweat rate in real time. The proposed sweat rate monitoring system has been validated through both controlled lab experiments (syringe pump) and human trials. An Internet of Things (IoT) platform was embedded, with the sensor using a Simblee board and Raspberry Pi. The overall prototype is capable of sending sweat rate information in real time to either a smartphone or directly to the cloud. Based on a proven theoretical concept, our overall system implementation features a pioneer device that can truly measure the rate of sweat in real time, which was tested and validated on human subjects. Our realization of the real-time sweat rate watch is capable of detecting sweat rates as low as 0.15 µL/min/cm², with an average error in accuracy of 18% compared to manual sweat rate readings.

Implementation of a microfluidic conductivity sensor -- a potential sweat electrolyte sensing system for dehydration detection.

As dehydration continues to plague performance athletes and soldiers, the need for improved dehydration detection is clear. We propose the use of a conductometric sensor as the foundation of a sweat-sensing patch to address this need. The conductometric sensor evaluates the conductivity of solutions with varying sodium concentrations. A lithographic process was used to fabricate a Polydimethylsiloxane (PDMS) microfluidic channel through which solution was flowed. The ionization of the solution that occurs when a voltage is applied results in an effective resistance across the channel. The measured resistance therefore, reflects the ionization of the solution and the corresponding sodium concentration. The potential application of the conductometric sensor in a sweat-sensing patch requires compatibility with a microcontroller and Bluetooth module. Thus, a circuit interface was created. A voltage divider was utilized to convert the output resistance of the sensor to a voltage that could be input into a microcontroller. An AC voltage signal with a frequency of 10 kHz was used as the source voltage of the voltage divider to minimize the faradaic impedance and the double layer effect of the ionized solution. Tests have revealed that the conductometric is capable of precisely measuring the conductivity of a sodium solution. The conductometric sensor will be applied to a sweat sensing patch through future work involving studying the link between sodium concentration in sweat and an individual's dehydration level, developing a sweat-collection method, and developing a method of consideration for the other ions contained in sweat.

Direct upstream motility in Escherichia coli.

We provide an experimental demonstration of positive rheotaxis (rapid and continuous upstream motility) in wild-type Escherichia coli freely swimming over a surface. This hydrodynamic phenomenon is dominant below a critical shear rate and robust against Brownian motion and cell tumbling. We deduce that individual bacteria entering a flow system can rapidly migrate upstream (>20 µm/s) much faster than a gradually advancing biofilm. Given a bacterial population with a distribution of sizes and swim speeds, local shear rate near the surface determines the dominant hydrodynamic mode for motility, i.e., circular or random trajectories for low shear rates, positive rheotaxis for moderate flow, and sideways swimming at higher shear rates. Faster swimmers can move upstream more rapidly and at higher shear rates, as expected. Interestingly, we also find on average that both swim speed and upstream motility are independent of cell aspect ratio.

Towards the run and walk activity classification through step detection--an android application.

Falling is one of the most common accidents with potentially irreversible consequences, especially considering special groups, such as the elderly or disabled. One approach to solve this issue would be an early detection of the falling event. Towards reaching the goal of early fall detection, we have worked on distinguishing and monitoring some basic human activities such as walking and running. Since we plan to implement the system mostly for seniors and the disabled, simplicity of the usage becomes very important. We have successfully implemented an algorithm that would not require the acceleration sensor to be fixed in a specific position (the smart phone itself in our application), whereas most of the previous research dictates the sensor to be fixed in a certain direction. This algorithm reviews data from the accelerometer to determine if a user has taken a step or not and keeps track of the total amount of steps. After testing, the algorithm was more accurate than a commercial pedometer in terms of comparing outputs to the actual number of steps taken by the user.

Fuzzy multicriteria disposal method and site selection for municipal solid waste.

The use of fuzzy multiple criteria analysis (MCA) in solid waste management has the advantage of rendering subjective and implicit decision making more objective and analytical, with its ability to accommodate both quantitative and qualitative data. In this paper a modified fuzzy TOPSIS methodology is proposed for the selection of appropriate disposal method and site for municipal solid waste (MSW). Our method is superior to existing methods since it has capability of representing vague qualitative data and presenting all possible results with different degrees of membership. In the first stage of the proposed methodology, a set of criteria of cost, reliability, feasibility, pollution and emission levels, waste and energy recovery is optimized to determine the best MSW disposal method. Landfilling, composting, conventional incineration, and refuse-derived fuel (RDF) combustion are the alternatives considered. The weights of the selection criteria are determined by fuzzy pairwise comparison matrices of Analytic Hierarchy Process (AHP). It is found that RDF combustion is the best disposal method alternative for Istanbul. In the second stage, the same methodology is used to determine the optimum RDF combustion plant location using adjacent land use, climate, road access and cost as the criteria. The results of this study illustrate the importance of the weights on the various factors in deciding the optimized location, with the best site located in Catalca. A sensitivity analysis is also conducted to monitor how sensitive our model is to changes in the various criteria weights.

Characterization of hydrodynamic surface interactions of Escherichia coli cell bodies in shear flow.

We experimentally demonstrate that nonflagellated Escherichia coli strains follow modified Jeffery orbits in shear flow near a surface. We fully characterize their Jeffery orbits as a function of their aspect ratios and distance from that surface. Thanks to the linearity of Navier-Stokes equations under low-Reynolds-number conditions, the hydrodynamic body-wall interactions described here can be superimposed with flagellar motility and Brownian motion to construct models that explain the full picture of bacterial motility near a surface under shear flow.