No abstract

Recently, animal emotion recognition has become an important field for developing intelligent systems for tracking and monitoring rhinos and elephants. In this work, a smart sensing system that helps in detecting animal emotions based on information from physiological parameters obtained from sensors attached on animal body, has been designed. The signals are continuously obtained from a heart rate sensor,galvanic skin resistance sensorand body temperature sensor. After amplifying and filtering of the signals from the sensors are done, they are processed in the microcontroller and transmitted wirelessly using GSM modem and ZigBee technologies.The signals which are received from the system are displayed and stored in the database where they are analyzed visually for patterns. The four basic emotions parameters observed in this project are happy (excited), sad, angry and neutral (relaxed). In this research dog have been used for the pilot study.

ABSTRACT

Events such as poaching, accidents, unexpected adverse health events (e.g., heart problems, seizures, heart stroke, dizziness, breathing problems, bleeding and broken bones) have adverse impact on animals’ health. Many of such events, if known to someone in a position to assist the animal, can be avoided, minimized or ameliorated. Unfortunately, many events occur in a manner in which assistance is unavailable or provided too late. In regard to this, measures needed include improving systems and methods for avoiding or reducing the impact of such adverse events. The study developed an intelligent real-time tracking system for monitoring rhinos and elephant. It was guided by four specific objectives: reviewing and analyzing the existing systems for tracking animals and proposing new more intelligent systems, designing of smart sensing system for animal emotions recognition, developing computational models’ analysis of wildlife tracking system for optimality, and developing an intelligent wildlife collar information management system using mobile application. The first objective was accomplished by conducting a cross-sectional study, and one-time data collection at Ngorongoro Conservation Area, Tanzania. The second objective was completed by designing a modern smart sensing animal collar belt that can recognize animal emotions. The third objective was performed by attenuation models-based analysis of wildlife tracking system for optimality. And the fourth objective was accomplished by developing a mobile application that collects periodical sensor output responses from tracked elephant/rhino with their GPS locations. All developed solutions were promising and can be utilized on improvement of the current existing ant-poaching system. The solutions can be considered as a proof of concept, which needs to be developed further for use in final product.