TY - GEN
T1 - Applying Machine Learning to Design and Evaluate White Noise Recommendation System for Insomniacs
AU - Jhang, Nai Wun
AU - Hung, Yu Hsiu
AU - Lin, Yang Cheng
AU - Wu, You Hsun
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/23
Y1 - 2020/10/23
N2 - Many sleep problems have occurred due to changes in the modern lifestyle. Insomnia is more serious than other sleep disorders. Insomnia increases the risk of depression, obesity, and cardiovascular diseases when it is not treated properly. Nowadays, most sleep therapies involve drugs that cause side effects on. For non-drug therapies, certain sounds assist sleep. However, as the sound is subjective, it is difficult to determine the sound suitable for each individual. This research designs an application that recommends white noise to insomniacs. For the application, we use machine learning technology for white noise recommendation and the design method for the user interface. For the experiment, we conduct a randomized controlled experiment and a five-day sleep experiment. This experiment verifies the effectiveness of the recommended white noise for sleep improvement. In addition to sleep assessment, we also use the system usability scale and semi-structured interviews to validate this system's usability and willingness. The result shows that white noise improves deep sleep and reduces the time to fall asleep. Moreover, the usability score of this application is much higher than the passing score of the scale.
AB - Many sleep problems have occurred due to changes in the modern lifestyle. Insomnia is more serious than other sleep disorders. Insomnia increases the risk of depression, obesity, and cardiovascular diseases when it is not treated properly. Nowadays, most sleep therapies involve drugs that cause side effects on. For non-drug therapies, certain sounds assist sleep. However, as the sound is subjective, it is difficult to determine the sound suitable for each individual. This research designs an application that recommends white noise to insomniacs. For the application, we use machine learning technology for white noise recommendation and the design method for the user interface. For the experiment, we conduct a randomized controlled experiment and a five-day sleep experiment. This experiment verifies the effectiveness of the recommended white noise for sleep improvement. In addition to sleep assessment, we also use the system usability scale and semi-structured interviews to validate this system's usability and willingness. The result shows that white noise improves deep sleep and reduces the time to fall asleep. Moreover, the usability score of this application is much higher than the passing score of the scale.
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U2 - 10.1109/ECICE50847.2020.9301974
DO - 10.1109/ECICE50847.2020.9301974
M3 - Conference contribution
AN - SCOPUS:85099585665
T3 - 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
SP - 183
EP - 186
BT - 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
A2 - Meen, Teen-Hang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020
Y2 - 23 October 2020 through 25 October 2020
ER -
