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 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.

A passive RFID-based location system for personnel and asset monitoring.

BACKGROUND: Typical radio frequency identification (RFID) access control system can be ineffective if an unauthorized person tailgates an authorized person through an access area. OBJECTIVE: To propose a system by using indoor locating and tracking techniques address this problem, which is to prevent unauthorized Alzheimer's and dementia patients from getting lost including by tailgating. METHODS: To achieve accurate target location, passive RFID deployment strategy is studied and a fingerprinting based passive RFID localization algorithm is proposed. The proposed system was evaluated in a building environment to simulate the performance of access control. RFID reader was installed on ceiling near the access area and tags were stitched on both shoulders of the experiment subject's garments. RESULTS: The probability of the error distance within 0.3 m achieved 97% in the warning area; the location precision achieved 97% within 0.4 m in the monitoring area. CONCLUSIONS: The result showed that if an unauthorized person enters the restricted area, the system can initiate an alert signal accurately. Therefore, the proposed system is very suitable to be used in nursing home or hospital to prevent unauthorized personnel and assets entering/exiting a confined location.

Body posture recognition and turning recording system for the care of bed bound patients.

This paper proposes body posture recognition and turning recording system for assisting the care of bed bound patients in nursing homes. The system continuously detects the patient's body posture and records the length of time for each body posture. If the patient remains in the same body posture long enough to develop pressure ulcers, the system notifies caregivers to change the patient's body posture. The objective of recording is to provide the log of body turning for querying of patients' family members. In order to accurately detect patient's body posture, we developed a novel pressure sensing pad which contains force sensing resistor sensors. Based on the proposed pressure sensing pad, we developed a bed posture recognition module which includes a bed posture recognition algorithm. The algorithm is based on fuzzy theory. The body posture recognition algorithm can detect the patient's bed posture whether it is right lateral decubitus, left lateral decubitus, or supine. The detected information of patient's body posture can be then transmitted to the server of healthcare center by the communication module to perform the functions of recording and notification. Experimental results showed that the average posture recognition accuracy for our proposed module is 92%.