Can Music Therapy Reduce Human Psychological Stress: A Review

Hatwar, Nikita R.; Gawande, Ujwalla

doi:10.1007/978-981-15-0077-0_41

Cited by 7 publications

(10 citation statements)

References 11 publications

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“…In 2017, aut et al [12] used the classification method of support vector machine (SVM) to classify emotions stimulated by music by using EEG signals as a medium and achieved a good classification effect. In 2020, Hatwar and Gawande [13] used the KNN (nearest neighbor) classifier to analyze EEG signals to recognize human emotions, and the effect was significant. From this point of view, as one of the most popular concepts in the field of artificial intelligence, deep learning, coupled with its technology that keeps pace with the times, is having a significant impact on human life in the past, present, and future.…”

Section: Related Workmentioning

confidence: 99%

Intelligent System of Somatosensory Music Therapy Information Feedback in Deep Learning Environment

Zhao

2021

Complexity

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In the treatment of children with autistic spectrum disorder (ASD) through music perception, the perception effect and the development of the disease are mainly reflected in the fluctuations of the electroencephalogram (EEG), which is clinically effective on the brain. There is an inaccuracy problem in electrogram judgment, and deep learning has great advantages in signal feature extraction and classification. Based on the theoretical basis of Deep Belief Network (DBN) in deep learning, this paper proposes a method that combines the optimized Restricted Boltzmann machine (RBM) feature extraction model with the softmax classification algorithm. Brain wave tracking analysis is performed on children with autism who have received different music perception treatments to improve classification accuracy and achieve the purpose of accurately judging the condition. Through continuous adjustment and optimization of the weight matrix in the model, a stable recognition model is obtained. The simulation results show that this optimization algorithm can effectively improve the recognition performance of DBN, with an accuracy of 94% in a certain environment, and has a better classification effect than other traditional classification methods.

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Section: Related Workmentioning

confidence: 99%

Intelligent System of Somatosensory Music Therapy Information Feedback in Deep Learning Environment

Zhao

2021

Complexity

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“…dissatisfaction, absenteeism, and early retirement, among others, which tend to undermine the performance at work, as well as the workers' personal, social, and economic life (Hatwar & Gawande, 2020;Lipp & Lipp, 2019).…”

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confidence: 99%

“…The world’s dissatisfaction at work gained notoriety with the advent of the Industrial Revolution and grew with the process of globalization (Hatwar & Gawande, 2020; Trovão & Araújo, 2018). This growth has been causing some problems with work dynamics, such as work overload, pay dissatisfaction, absenteeism, and early retirement, among others, which tend to undermine the performance at work, as well as the workers’ personal, social, and economic life (Hatwar & Gawande, 2020; Lipp & Lipp, 2019).…”

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confidence: 99%

“…From that, some scientific initiatives based on several theoretical–philosophical frameworks have looked for alternatives to prevent or alleviate stress. One of them is the use of musical therapy (Beck et al, 2015; Hatwar & Gawande, 2020), which, when applied in the work environment, is called organizational musical therapy and comes as a complement to organizational psychology and personnel management (Bruscia, 2016) and is widely interdisciplinary (Matoso et al, 2018). Therefore, upon using music in the organizational domain, its therapeutic power can contribute to psychic health and welfare.…”

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confidence: 99%

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The effect of organizational musical therapy on reducing the stress level of health care professionals in a public hospital

Matoso

Oliveira

Sousa

2021

Psychology of Music

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This study aimed at analyzing the influence of receptive organizational therapy on the levels of stress of nursing professionals in a public hospital. Concerning the methodology, the study has a quantitative approach and an explanatory nature and adopts quasi-experimental procedures. The research universe encompassed 74 nursing professionals. As a research tool, the study used the Lipp’s Stress Symptoms Inventory for Adults. Primary results showed that, out of the 74 professionals, 20.3% ( n = 15) were classified as having no levels of stress. However, 79.7% ( n = 59) of the participants had some levels of stress. Out of these professionals, 71.6% ( n = 53) were in the second stage, known as resistance. Thirty days after the application of receptive musical therapy, the 74 professionals who took part in this experience had their levels of stress reevaluated and compared to the levels of stress from before the musical intervention with the use of the Wilcoxon test. The test results highlighted that the levels of stress during the resistance, near-exhaustion, and exhaustion stages were minimized. As a conclusion, therefore, receptive organizational musical therapy has meaningful effects on reducing stress and improving the work life quality of professionals.

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“…On the other hand, when the patient is under stress, the two allowed types shall include classical and ambient nature sounds, including in Set 1. These selections have been made in line with the previous research, where it has been found that classical music [54,55], particularly Mozart [56] can relieve stress and also, it has been shown that ambient nature sound can also have a calming effect on a person [57][58][59], due to which we made it the second choice. Finally, Set 2 in the file structure shown in Figure 5 has been included to facilitate the patient while performing exercise.…”

Section: Managing Musicmentioning

confidence: 73%

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

et al. 2020

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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.

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Can Music Therapy Reduce Human Psychological Stress: A Review

Cited by 7 publications

References 11 publications

Intelligent System of Somatosensory Music Therapy Information Feedback in Deep Learning Environment

Intelligent System of Somatosensory Music Therapy Information Feedback in Deep Learning Environment

The effect of organizational musical therapy on reducing the stress level of health care professionals in a public hospital

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

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