Name
Abstract | ||
|---|---|---|
Sleeping posture is an important factor in the quality of sleep. In this paper, we present a study on the feasibility of the automatic detection of personal sleeping posture from ballistocardiograms (BCGs) recorded by unobtrusive loadcell-embedded mattress. The proposed system is intended as a screening and monitoring tool in home-healthcare applications and provides reference for patients with chronic disease to adjust sleeping pattern. This work extracts the features of a heartbeat waveform pattern obtained from BCG and use the Random Forest classifier to classify various sleeping postures. The experimental results of this work indicate that the proposed algorithm can achieve high accuracy when sleep is stable and in the stage of sleep instability it can also make most of correct decisions about sleeping posture. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-981-10-6571-2_216 | Lecture Notes in Electrical Engineering |
Keywords | DocType | Volume |
Sleeping posture detection,Ballistocardiography (BCG),Classification,Home health monitoring | Conference | 463 |
ISSN | Citations | PageRank |
1876-1100 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Wang | 1 | 2 | 0.75 |
| Peizhi Liu | 2 | 0 | 0.68 |
| Weidong Gao | 3 | 0 | 1.35 |