Paper Info
Title
MotionTree: A Tree-Based In-Bed Body Motion Classification System Using Load-Cells
Abstract
The basic necessity of sleep in our life is critically important to ensure our wellbeing. Sufficient sleep of good quality is highly desired in order to have enough energy to live. One of the main factors to measure sleep quality is the amount of body motion during sleep. In our earlier work, we have developed a load cell based system that can detect in-bed body movements and classify them into two broad classes: large or small movements. In this paper, we set out to achieve much finer body motion classification. Towards this goal, we define 9 classes of movements, and design a machine learning algorithm using Support Vector Machine (SVM) and Random Forest techniques to classify a movement into one of these 9 classes. In this way, we can find out which body parts are involved in every movement. For every movement, we have extracted 24 features and used them in our model. This movement classification system was evaluated on data collected from 40 subjects who performed 35 predefined movements in each experiment. The accuracy of our model is not exactly the same for all classes of movements. On average it classified correctly 90% of movements. This model can be used conveniently for long-term home monitoring.
Year
DOI
Venue
2017
10.1109/CHASE.2017.71
2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
Keywords
Field
DocType
Bed-Mounted Sensor,Sleep Monitoring,Signal Processing
Load cell,Signal processing,Decision tree,Computer vision,Support vector machine,Feature extraction,Sleep monitoring,Artificial intelligence,Sleep quality,Engineering,Random forest
Conference
ISBN
Citations 
PageRank 
978-1-5090-4723-9
1
0.39
References 
Authors
13
5
Name
Order
Citations
PageRank
Musaab Alaziz111.06
Zhenhua Jia2305.33
Richard Howard331125.10
Xiaodong Lin44193223.14
Yanyong Zhang53116184.08