Code: https://github.com/Aneapiy/Swift-HAR
Human activity recognition (HAR) is the use of sensors and algorithms to classify human action. Applications of HAR in healthcare involve remote monitoring of patient physical activity, detecting falls in elderly patients, and helping classify movement in patients with motion disorders. HAR can also be used in wearable electronics that track user exercise and activity. In this video, I demonstrate an HAR application written in Swift 3.1 for the iPhone 6s running iOS 10.3.1. Data logs gathered from the CoreMotion API feeds into a Feed Forward Neural Network created using the Swift-AI framework. Both training and inference from the neural network is executed locally in an iPhone 6s. The neural network can recognize multiple repetitive and continuous activities by changing the number of output nodes to match the number of unique activities the user logs. The trained neural network successfully classifies the tested user activities of standing still, walking, or doing bicep curls.
