Wearable accelerometer based extended sleep position recognition

Date

2017-12-18

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

Sleep positions have an impact on sleep quality and therefore need to be further analyzed. Current research on position tracking includes only the four basic positions. In the context of wearable devices, energy efficiency is still an open issue. This research presents a way to detect eight positions with higher granularity under energy efficient constraints. Generalized Matrix Learning Vector Quantization is used, as it is a fast and appropriate method for environments with limited computation resources, and has not been seen for this kind of application before. The overall model trained on individuals performs with an averaged accuracy of 99.8%, in contrast to an averaged accuracy of 83.62% for grouped datasets. Real world application gives an accuracy of around 98%. The results show that energy efficiency will be feasible, as performance stays similar for lower sampling rate. This is a step towards a mobile solution which gives more insight in person's sleep behaviour.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link

Keywords

Sleep apnea, Sensors, Legged locomotion, Accelerometers, Biomedical monitoring, Monitoring, Imaging

Citation

Fallmann S., van Veen R., Chen L., Walker D., Chen F. and Pan C. (2017) Wearable Accelerometer Based Extended Sleep Position Recognition, 19th IEEE International Conference on E-health Networking, Application & Services (Healthcom), Dalian, October 2017.

Rights

Research Institute

Cyber Technology Institute (CTI)