Patricia Mellodge

Stance width changes how sensory feedback is used for multisegmental balance control

A multilink sensorimotor integration model of frontal plane balance control was developed to determine how stance width influences the use of sensory feedback in healthy adults. Data used to estimate model parameters came from seven human participants who stood on a continuously rotating surface with three different stimulus amplitudes, with eyes open and closed, and at four different stance widths. Dependent variables included lower body (LB) and upper body (UB) sway quantified by frequency-response functions. Results showed that stance width had a major influence on how parameters varied across stimulus amplitude and between visual conditions. Active mechanisms dominated LB control. At narrower stances, with increasing stimulus amplitude, subjects used sensory reweighting to shift reliance from proprioceptive cues to vestibular and/or visual cues that oriented the LB more toward upright. When vision was available, subjects reduced reliance on proprioception and increased reliance on vision. At wider stances, LB control did not exhibit sensory reweighting. In the UB system, both active and passive mechanisms contributed and were dependent on stance width. UB control changed across stimulus amplitude most in wide stance (opposite of the pattern found in LB control). The strong influence of stance width on sensory integration and neural feedback control implies that rehabilitative therapies for balance disorders can target different aspects of balance control by using different stance widths. Rehabilitative strategies designed to assess or modify sensory reweighting will be most effective with the use of narrower stances, whereas wider stances present greater challenges to UB control.

Using the arduino platform to enhance student learning experiences

We present preliminary experiences using the Arduino microprocessor platform in the undergraduate computing curricula, at both the upper and lower levels. The goal is to enhance student learning by engaging them in a contextualized project-based learning experience and introducing them to fundamental computing and engineering concepts in the context of a highly visual and easy to use environment.

Remotely Monitoring a Patient's Mobility: A Digital Health Application

This paper was focused on iShoe, a device used for balancing. This device was designed for patient rehabilitation that would operate safely in home of elderly or recovering patient. This device was comprise of wearable device such as force sensor, assistive device such as walker, and environmental sensor. Topics on design such as Tekscans FlexiForce sensor, force sensor, accelerometer, PC interfacing, microcontroller, insole development, walker development,and data acquisition development were discussed.

Uncertainty Propagation in Abstracted Systems

In this chapter, it is shown that given a system and its abstraction, the evolution of uncertain initial conditions in the original system is, in some sense, matched by the evolution of the uncertainty in the abstracted system. In other words, it is shown that the concept of Φ-related vector fields extends to the case of stochastic initial conditions where the probability density function (pdf) for the initial conditions is known. In the deterministic case, the Φ mapping commutes with the system dynamics. In this chapter, it is shown that in the case of stochastic initial conditions, the induced mapping, Φpdf, commutes with the evolution of the pdf according to the Liouville equation. It is also shown that a control system abstraction can capture the time evolution of the uncertainty in the original system by an appropriate choice of control input. Application of the convservation law results in a partial differential equation known as the Liouville equation, for which a closed form solution is known. The solution provides the time evolution of the initial pdf which can be followed by the abstracted system.

Open-Loop Vehicle Control Using an Abstraction of its Model

Feedback control design of complex systems can be made easier by working on simpler models of the system that are their abstractions. This paper presents a method to control a car-like robot using abstraction: the car is represented by a uni- cycle. A transformation is provided to calculate car inputs from unicycle inputs so that the car follows the unicycle trajectory whenever proper initial conditions are met. The transformation does not give correct results for the case when the unicycle is rotating. In this case, an open-loop optimal control algorithm is presented to generate car inputs. Simulation results are given for different initial car inputs and the results are compared.

Scaled instrumented vehicle system: modelling, control and hardware

This paper describes the current development of a scale model vehicle laboratory at Virginia Tech. This lab and the scale model cars contained therein provide a testbed for the small scale development stage of intelligent transportation systems (ITS). In addition, the lab serves as a home to the prototype display being developed for an educational museum exhibit. The modelling and control of the scale vehicle is given and the implementation of the controller in hardware is discussed. The vehicle platform is described and the hardware and software architecture detailed. The car described is capable of operating manually and autonomously. In autonomous mode, several sensors are utilised including: infrared, magnetic, ultrasound, and image based technology. The operation of each sensor type is described and the information received by the processor from each is discussed. The possibility exists to implement many different types of controllers to perform path following or realise other control objectives.

Bionic Egg: Sealed mobile sensor packaging design with adaptive power consumption

The goals of this research are to design a 3D printed environmental data logger while considering sensor packaging space constraints and to implement a smart power consumption system for increased endurance. The product is sealed for harsh environmental conditions per IP65 standard as well as for impacts. In this paper, we share our electronics packaging design approach for a mobile sensor suite so called “Bionic Egg” which is capable of logging ambient conditions as well as external forces on a turkey egg during transportation. While available space is limited due to the shell size, there is an insufficient amount of room to mount a large enough battery to execute all day long operation requirement. We designed an adaptive power consumption methodology which adopts a reactive sensory usage logic by adjusting processor clock between 2–72Mhz. The power consumption of Low Profile Quad Flat Package (LQFP) 32 bit ARM Cortex microcontroller could be reduced as low as to 230 µA in deep sleep mode with a maximum of 62.1 mA drain for continuous logging state. The Mobile sensor suite includes inertial measurement unit (IMU), temperature and humidity sensors, Global Positioning System (GPS) unit with an internal antenna as well as a SD (Secure Digital) card board for data logging. The overall design approach, hardware structure, power consumption data as well as software structure are presented.

Uncertainty Propagation in Abstracted Systems via the Liouville Equation

This technical brief shows that given a system and its abstraction, the evolution of uncertain initial conditions in the original system is, in some sense, matched by the evolution of the uncertainty in the abstracted system. In other words, it is shown that the concept of Φ-related vector fields extends to the case of stochastic initial conditions where the probability density function (pdf) for the initial conditions is known. In the deterministic case, the Φ mapping commutes with the system dynamics. In this paper, we show that in the case of stochastic initial conditions, the induced mapping Φ pdf commutes with the evolution of the pdf according to the Liouville equation.

Uncertainty propagation in Φ related control systems via the Liouville equation

This paper studies the relationship between the evolutions of uncertain initial conditions in Φ-related control systems. It is shown that a control system abstraction can capture the time evolution of the uncertainty in the original system by an appropriate choice of control input. Φ-related control systems with stochastic initial conditions show the same behaviour as systems with deterministic initial conditions. A conservation law is applied to the probability density function (pdf) requiring that the area under it be unity. Application of the conservation law results in a partial differential equation known as the Liouville equation, for which a closed form solution is known. The solution provides the time evolution of the initial pdf which can be followed by the abstracted system.

Kinematic Modeling and Control

This chapter describes the kinematic modeling of a car-like mobile robot. Kinematic modeling is often used because of its simplicity and accuracy in predicting the car’s behavior under normal driving conditions. This type of modeling uses the nonholonomic constraints of the system as described in Section 2.3.