Simple Summary In general practice, the veterinarian and caregiver usually detect lameness in elephants from observation of any discomforting characteristics when walking. Currently, motion analysis can offer an objective method… Click to show full abstract
Simple Summary In general practice, the veterinarian and caregiver usually detect lameness in elephants from observation of any discomforting characteristics when walking. Currently, motion analysis can offer an objective method to evaluate normal and abnormal gait accurately, thus identifying changes in some characteristics when walking. This report aimed to introduce a recent technology utilizing wireless sensors for quantitative analysis of joint angles during the gait cycle in Asian elephants. To enable three-dimensional limb segment motion, a triaxial inertial measurement unit (IMU) is equipped with three sensor types: an accelerometer, a gyroscope, and a magnetometer. Therefore, we hope that this portable sensor-based system can help clinicians in diagnosis, especially in the early stages of lameness. Moreover, with wireless signal transmission, the system is clinically applicable for use in all areas where electricity is available. Abstract Gait analysis is a method of gathering quantitative information to assist in determining the cause of abnormal gait for the purpose of making treatment decisions in veterinary medicine. Recent technology has offered the wearable wireless sensor of an inertial measurement unit (IMU) for determining gait parameters. This study proposed the use of a triaxial IMU, comprising an accelerometer, a gyroscope, and a magnetometer, for detecting three-dimensional limb segment motion (XYZ axis) during the gait cycle in Asian elephants. A new algorithm was developed to estimate the kinematic parameter that represents each limb segment of the forelimbs and hindlimbs while walking at a comfortable speed. For future use, this study aimed to create a new prototype of the IMU with a configuration that is tailored to the elephant and apply machine learning in an effort to achieve greater precision.
               
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