Wearable accelerometer based extended sleep position recognition
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.
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
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.
ISBN : 9781509067046
Research Group : CIIRG
Research Institute : Cyber Technology Institute (CTI)
Peer Reviewed : Yes