Douglas F. Britton

The design of an interactive stroke rehabilitation gaming system

Abstract. There is a compelling need to create an alternative and affordable home based therapy system founded on sound rehabilitative principles, that is readily available, engaging and motivational, and can be remotely monitored by therapists. In the past two years, stroke related medical costs have increased 20%, while the number of clinical treatment sessions have declined. The purpose of this study was to develop an affordable interactive stroke rehabilitation gaming experience based on therapeutic fundamentals that can easily be used in the clinical setting or the home environment.

Detecting symptoms of diseases in poultry through audio signal processing

We developed an audio signal processing algorithm that detects rales (gurgling noises that are a distinct symptom of common respiratory diseases in poultry). We derived features from the audio by calculating mel frequency cepstral coefficients (MFCCs), clustering the MFCC vectors, and examining the distribution of cluster indices over a window of time. The features are classified with a C4.5 decision tree. Our training data consisted of eight minutes of manually labeled audio selected from 25 days of continuous recording from a controlled study. The experiment group was challenged with the infectious bronchitis virus and became sick, while the control group remained healthy. We tested the algorithm on the entire dataset and obtained results that match the course of the disease. Algorithms such as this could be used to continuously monitor chickens in commercial poultry farms, providing an early warning system that could significantly reduce the costs incurred from disease.

Evaluation of the ShapeTape for studying biomechanics in the workplace

Motion capture systems may be difficult to use in harsh environments such as a poultry plant, and therefore should be self-contained, portable, unobtrusive, and not interfere with or be degraded by plant machinery or processes. The purpose of this study was to evaluate the validity, reliability and accuracy of the ShapeTape system as a potential solution. This was accomplished by comparing kinematic data from the ShapeTape against the Vicon system. Subjects performed cyclical movements along a plane angled 45° up from the horizontal using their right arms. Results revealed that the ShapeTape kinematic data was significantly larger than the Vicon data, yet statistically reliable.

Identifying rale sounds in chickens using audio signals for early disease detection in poultry

Extreme learning machine (ELM) and support vector machine (SVM) classifiers are developed to detect rales (a gurgling sound that is a symptom of respiratory diseases in poultry). These classifiers operate on Mel-scaled spectral features calculated from recordings of healthy and sick chickens during a vaccine trial. Twenty minutes of labeled data were used to train and test the classifiers, then they were run on the full 25 days of continuous recordings from the healthy and sick chickens. The resulting detection rate follows the course of the disease and clearly distinguishes between the healthy and sick chickens. These results improve on our previous findings from the same data, and demonstrate the potential for automated acoustic monitoring of the health of commercial flocks.

Machine-vision-based quality control decision making for naturally varying product

The application of machine vision system to industrial manufacturing and inspection processes has motivate the development of intelligent and yet flexible decision making processes. When working with highly uniform product, most of the quality or inspection decisions can be based on straightforward but rigid rules once the relevant features have been extracted from the image. However when the product is highly nonuniform, other techniques must be applied to allow for product variability while still being capable of identifying and classifying defects. This paper will investigate methods for accomplishing this based on soft computing. A discussion of the general approach and then a specific methods for accomplishing this based on soft computing. A discussion of the general approach and then a specific examples of an integrated system for product quality determination is presented. This system combines color image processing and feature extraction with neural network classifiers and fuzzy logic based decision outputs to allow for maximum flexibility in accommodating product variability while still maintaining quality standards. The techniques for optimizing the classification parameters and the determination of the fuzzy logic membership functions and user rules are presented.

Development of an Audio and Video Observation and Recording Platform for Data Collection in a Broiler Growout Environment

While sensors exist for monitoring the environmental conditions within a poultry growout house, there are currently no systems that directly use bird vocalizations or behavior to indicate the health and welfare of a flock. As part of a research effort to explore this possibility, an experimental system was designed to collect audio, video, temperature, and humidity data for the entire six-week growout cycle of a flock of broiler chickens. The system consists of a Linux-based personal computer platform, two Shure condenser microphones, four video cameras, two temperature sensors, and a relative humidity sensor. A custom software application was developed to manage, record, and compress the data from the sensors, and it includes a graphic user interface where ammonia levels and other activities within the growout house could be logged manually. This paper describes in detail the design, configuration and operation of the integrated system along with considerations for data management and analysis. Overall the design has proven to be robust, and the operation relatively straight forward. Since the research is ongoing, the system continues to be improved for use in future data collection activities.

Integration of USB and firewire cameras in machine vision applications

Digital cameras have been around for many years, but a new breed of consumer market cameras is hitting the main stream. By using these devices, system designers and integrators will be well posited to take advantage of technological advances developed to support multimedia and imaging applications on the PC platform. Having these new cameras on the consumer market means lower cost, but it does not necessarily guarantee ease of integration. There are many issues that need to be accounted for like image quality, maintainable frame rates, image size and resolution, supported operating system, and ease of software integration. This paper will describe briefly a couple of the consumer digital standards, and then discuss some of the advantages and pitfalls of integrating both USB and Firewire cameras into computer/machine vision applications.

Image compensation for camera and lighting variability

With the current trend of integrating machine vision systems in industrial manufacturing and inspection applications comes the issue of camera and illumination stabilization. Unless each application is built around a particular camera and highly controlled lighting environment, the interchangeability of cameras of fluctuations in lighting become a problem as each camera usually has a different response. An empirical approach is proposed where color tile data is acquired using the camera of interest, and a mapping is developed to some predetermined reference image using neural networks. A similar analytical approach based on a rough analysis of the imaging systems is also considered for deriving a mapping between cameras. Once a mapping has been determined, all data from one camera is mapped to correspond to the images of the other prior to performing any processing on the data. Instead of writing separate image processing algorithms for the particular image data being received, the image data is adjusted based on each particular camera and lighting situation. All that is required when swapping cameras is the new mapping for the camera being inserted. The image processing algorithms can remain the same as the input data has been adjusted appropriately. The results of utilizing this technique are presented for an inspection application.