Artificial Neural Network (ANN) Enabled Internet of Things (IoT) Architecture for Music Therapy

Electronics

AusafAhmed Khan¹  KarimaKaram Khan¹  Shama Siddiqui²  AnwarAhmed Khan³  MuhammadZeeshan Shakir⁴  Rory Nesbitt⁴  Naeem Ramzan⁴ 

[1] Department of Anaesthesiology, Aga Khan University Hospital, Karachi City 74800, Pakistan;Department of Computer Science, DHA Suffa University, Karachi City 75500, Pakistan;Department of Computer Science, Institute of Business Administration, Karachi City 75270, Pakistan;School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Glasgow G72 0LH, UK;

关键词: music therapy;    alternative medicine;    Artificial Neural Networks;    Body Area Networks;    Internet of Things;   

DOI  :  10.3390/electronics9122019

来源: DOAJ

【 摘 要 】

Alternative medicine techniques such as music therapy have been a recent interest of medical practitioners and researchers. Significant clinical evidence suggests that music has a positive influence over pain, stress and anxiety for the patients of cancer, pre and post surgery, insomnia, child birth, end of life care, etc. Similarly, the technologies of Internet of Things (IoT), Body Area Networks (BAN) and Artificial Neural Networks (ANN) have been playing a vital role to improve the health and safety of the population through offering continuous remote monitoring facilities and immediate medical response. In this article, we propose a novel ANN enabled IoT architecture to integrate music therapy with BAN and ANN for providing immediate assistance to patients by automating the process of music therapy. The proposed architecture comprises of monitoring the body parameters of patients using BAN, categorizing the disease using ANN and playing music of the most appropriate type over the patient’s handheld device, when required. In addition, the ANN will also exploit Music Analytics such as the type and duration of music played and its impact over patient’s body parameters to iteratively improve the process of automated music therapy. We detail development of a prototype Android app which builds a playlist and plays music according to the emotional state of the user, in real time. Data for pulse rate, blood pressure and breath rate has been generated using Node-Red, and ANN has been created using Google Colaboratory (Colab). MQTT broker has been used to send generated data to Android device. The ANN uses binary and categorical cross-entropy loss functions, Adam optimiser and ReLU activation function to predict the mood of patient and suggest the most appropriate type of music.

【 授权许可】

Unknown