Western Music History Recommendation System Based on Internet-of-Things Data Analysis Technology

Algorithm 1

The recommendation algorithm’s flow.

	Input: Signal Photo Plethysmography from the first source of data
	Input: Galvanic Skin Response is the second source of data
	Output: Respective Target Emotion Labels based on Arousal and Nucleon Values
	Output: Recommendation Music
(1)	Obtain data from Image plethysmography and Galvanic Skin Response Sensors
(2)	Samples and feature extracts from and
(3)	Arousal and Valence values in the Machine Learning Pipeline can be used to predict labels for target emotions
(4)	Feed the recommendation engine’s decision-making algorithm with data
(5)	Recommendation engine for streams and user profiles can be integrated into .
(6)	Get and deliver it to player