Environmental Health: Pest and Solid Waste Management

Presented for World Environmental Health Day, September 26, 2016 in Greenville, North Carolina

A quantitative probabilistic investigation into the accumulation of rounding errors in numerical ODE solution.

We examine numerical rounding errors of some deterministic solvers for systems of ordinary differential equations (ODEs) from a probabilistic viewpoint. We show that the accumulation of rounding errors results in a solution which is inherently random and we obtain the theoretical distribution of the trajectory as a function of time, the step size and the numerical precision of the computer. We consider, in particular, systems which amplify the effect of the rounding errors so that over long time periods the solutions exhibit divergent behaviour. By performing multiple repetitions with different values of the time step size, we observe numerically the random distributions predicted theoretically. We mainly focus on the explicit Euler and fourth order Runge–Kutta methods but also briefly consider more complex algorithms such as the implicit solvers VODE and RADAU5 in order to demonstrate that the observed effects are not specific to a particular method

EFFECTS OF FOOTWEAR AND RUNNING SPEED ON FOOT KINEMATICS IN THE FRONTAL PLANE

Excessive foot motion and increased running speed are frequently discussed as injury inducing factors in runners. We assessed foot kinematics using an inertial measurement unit while subjects ran at different running speeds wearing varying shoes. 20 male runners ran at 2.9, 3.5, 4.2 m/s wearing an all-purpose shoe (NS) and two differently configured running shoes: high arch support, wedges, soft damping (Con1) and low arch support, no wedges, hard damping (Con2). Maximum pronation velocity was higher when running in NS than in Con1 (p = 0.03) and when running at higher speeds (p < 0.01). Subjects showed increased ROM when wearing NS compared to Con1 (p < 0.01) or Con2 (p = 0.04) and at higher speeds (p < 0.01). As shoe variations and running speed led to changed kinematics, these parameters should be considered when investigating biomechanical parameters

Human activity recognition for emergency first responders via body-worn inertial sensors

Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first response team leaders had better information about the situation on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status during operations, based on data from wireless inertial measurement units. In this paper we investigate if Gradient Boosted Trees (GBT) could be used for recognising 17 activities, selected in consultation with first responders, from inertial data. By arranging these into more general groups we generate three additional classification problems which are used for comparing GBT with k-Nearest Neighbours (kNN) and Support Vector Machines (SVM). The results show that GBT outperforms both kNN and SVM for three of these four problems with a mean absolute error of less than 7%, which is distributed more evenly across the target activities than that from either kNN or SVM

Sensor and feature selection for an emergency first responders activity recognition system

Human activity recognition (HAR) has a wide range of applications, such as monitoring ambulatory patients’ recovery, workers for harmful movement patterns, or elderly populations for falls. These systems often operate in an environment where battery lifespan, power consumption, and hence computational complexity, are of prime concern. This work explores three methods for reducing the dimensionality of a HAR problem in the context of an emergency first responders monitoring system. We empirically estimate the accuracy of k-Nearest Neighbours, Support Vector Machines, and Gradient Boosted Trees when using different combinations of (A)ccelerometer, (G)yroscope and (P)ressure sensors. We then apply Principal Component Analysis for dimensionality reduction, and the Kruskal-Wallis test for feature selection. Our results show that the best combination is that which includes all three sensors (MAE: 3.6%), followed by the A/G (MAE: 3.7%), and the A/P combination (MAE 4.3%): the same as that when using the accelerometer alone. Moreover, our results show that the Kruskal-Wallis test can be used to discard up to 50% of the features, and yet improve the performance of classification algorithms