This study explored the use of body-worn smartphone inertial sensors (accelerometer and gyroscope) to objectively detect ataxic gait patterns in dogs. Researchers collected data from 770 walking sessions, comparing 55 healthy dogs to 23 dogs diagnosed with ataxia. By placing an iPhone SE on the dog’s back using an adjustable harness and applying various machine learning techniques, they achieved a 95% accuracy in distinguishing between healthy and ataxic dogs, with the K-nearest neighbors (KNN) technique performing best. This indicates a significant potential for developing smartphone applications for canine ataxia diagnosis and monitoring of treatment effects in both clinical and domestic settings.
Supervised Neural Style Transfer as an Augmentation Technique for Facial Landmark Detection
A major challenge in training accurate facial landmark detection models
