Heart rate variability analysis based sleep music recommendation system

doi:10.11772/j.issn.1001-9081.2019111969

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (5): 1539-1544.DOI: 10.11772/j.issn.1001-9081.2019111969

• Frontier & interdisciplinary applications • Previous Articles Next Articles

Heart rate variability analysis based sleep music recommendation system

PENG Cheng, CHANG Xiangmao, QIU Yuan

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 211106, China

Received:2019-11-04 Revised:2019-11-21 Online:2020-05-15 Published:2020-05-10
Contact: PENG Cheng, born in 1995, M. S. candidate. His research interests include sleep monitoring, deep learning.
About author:PENG Cheng, born in 1995, M. S. candidate. His research interests include sleep monitoring, deep learning.CHANG Xiangmao, born in 1982, Ph. D., associate professor. His research interests include internet of things, intelligent health monitoring based wearable devices, sensory data processing and analysis of machine learning algorithms.QIU Yuan, born in 1995, M. S. candidate. Her research interests include anomaly detection, deep learning.

基于心率变异性分析的睡眠音乐推荐系统

彭程, 常相茂, 仇媛

南京航空航天大学计算机科学与技术学院，南京 211106

通讯作者: 彭程(1995—)
作者简介:彭程(1995—)，男，安徽合肥人，硕士研究生，CCF会员，主要研究方向：睡眠监测、深度学习；常相茂(1982—)，男，山东淄博人，副教授，博士，CCF会员，主要研究方向：物联网、基于可穿戴式设备的智能健康检测、机器学习算法的感知数据处理及分析；仇媛(1995—)，女，河北石家庄人，硕士研究生，CCF会员，主要研究方向：异常检测、深度学习。

Abstract

Abstract:

The existing sleep monitoring researches mainly focus on non-interfering monitoring methods for sleep quality, and lack research on active adjustment methods of sleep quality. The researches of mental state and sleep staging based on Heart Rate Variability (HRV) analysis focus on the acquisition of these two kinds of information, which needs people wearing professional medical equipment, and the researches lack the application and adjustment of the information. Music can be used as a non-pharmaceutical method to solve sleep problems, but existing music recommendation methods do not consider the differences in individual sleep and mental states. A music recommendation system according to mental stress and sleep state by mobile devices was proposed to solve above problems. Firstly, the photoplethysmography signals were collected by the watch to extract features and calculate the heart rate. Then, the collected signals were transmitted to the mobile phone via bluetooth, and these signals were used by the mobile phone to evaluate the person’s mental stress and sleep state to play the adjusted music. Finally, the music was recommended according to the sleep time per night of the individual. The experimental results show that after using the sleep music recommendation system, the total sleep time of users increases by 11.0%.

Key words: sleep monitoring, mental stress, sleep stage, music recommendation

摘要：

现有睡眠监测研究主要是针对睡眠质量提出非干扰式监测方法的研究，而缺乏对睡眠质量主动调节方法的研究。基于心率变异性(HRV)分析的精神状态以及睡眠分期研究主要集中在这两种信息的获取上，而这两种信息的获取需要佩戴专业医疗设备，并且这些研究缺乏对信息的应用以及调整。音乐可以作为一种解决睡眠问题的非药物类方法，但现有音乐推荐方法并未考虑个体睡眠及精神状态的差异。针对以上问题提出了一种基于移动设备的精神压力和睡眠状态的音乐推荐系统。首先，用手表采集光体积扫描计信号来提取特征并计算心率；其次，将采集的信号通过蓝牙传递给手机，手机通过这些信号评估人的精神压力以及睡眠状态来播放调整音乐；最后，根据个体每晚的入眠时间进行音乐推荐。实验结果表明，在使用睡眠音乐推荐系统后，用户睡眠总时长相较于使用前增长11.0%。

关键词: 睡眠监测, 精神压力, 睡眠状态, 音乐推荐

CLC Number:

TP391.4

PENG Cheng, CHANG Xiangmao, QIU Yuan. Heart rate variability analysis based sleep music recommendation system[J]. Journal of Computer Applications, 2020, 40(5): 1539-1544.

彭程, 常相茂, 仇媛. 基于心率变异性分析的睡眠音乐推荐系统[J]. 计算机应用, 2020, 40(5): 1539-1544.

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Heart rate variability analysis based sleep music recommendation system

基于心率变异性分析的睡眠音乐推荐系统

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