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Sleeping posture recognition using fuzzy c-means algorithm
BACKGROUND: Pressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. However, lower limbs cannot be recognized accurately unless thousands of sensors...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6219018/ https://www.ncbi.nlm.nih.gov/pubmed/30396347 http://dx.doi.org/10.1186/s12938-018-0584-3 |
Sumario: | BACKGROUND: Pressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. However, lower limbs cannot be recognized accurately unless thousands of sensors are deployed on the bedsheet. METHOD: We designed a sleeping posture recognition scheme in which FSR sensors were deployed on the upper part of the bedsheet to record the pressure distribution of the upper body. In addition, an infrared array sensor was deployed to collect data for the lower body. Posture recognition was performed using a fuzzy c-means clustering algorithm. Six types of sleeping body posture were recognized from the combination of the upper and lower body postures. RESULTS: The experimental results showed that the proposed method achieved an accuracy of above 88%. Moreover, the proposed scheme is cost-efficient and easy to deploy. CONCLUSIONS: The proposed sleeping posture recognition system can be used for pressure ulcer prevention and sleep quality assessment. Compared to wearable sensors and cameras, FSR sensors and infrared array sensors are unobstructed and meet privacy requirements. Moreover, the proposed method provides a cost-effective solution for the recognition of sleeping posture. |
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