Ambient Sleep Quality Analysis with a Machine Learning Model
- Resource Type
- Conference
- Authors
- Bagci, Mehmet Furkan; Nguyen, Truong; Ozturk, Yusuf
- Source
- 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 2023 IEEE International Conference on. :1-5 Jun, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Machine learning algorithms
Medical devices
Signal processing algorithms
Machine learning
Signal processing
Infrared sensors
Quality assessment
Sleep Monitoring
LSTM
K-mean Clustering
PIR
- Language
The average person moves about 40 to 50 times a night during sleep. In addition to complete body movements, twitches and limb jerks are common during normal sleep. Chest or abdomen also move slightly and periodically due to respiration. Passive infrared sensors can detect fine motion such as respiration induced chest motion as well as stronger motion induced by body movements. This study presents a machine learning model for sleep quality assessment using ambient passive infrared sensors. The ambient sensor and associated machine learning algorithm successfully identified sleep and motion activity with better than 88% accuracy. The model presented here performed better in monitoring motion than a wearable Garmin watch and reported respiration rates inline with a medical quality wearable sleep monitoring sensor by Masimo Inc.