Applications of non-invasive sensor devices to personalised health care

Abstract

The application of non-invasive devices in personal health care is becoming more and more widespread, especially sleep quality, which is a critical part of personal health because it is often associated with many diseases. Using some sensor devices such as pressure sensors, photoplethysmography and heart rate devices, the authors can collect a lot of physiological signals. In this work, they provide a method of fuzzy inference to evaluate the sleep phase, which uses the values of heart rate, heart rate variation, and body movement as input parameters that collected by the sensor devices. This method has been applied to the actual product. The results show that the measurement results of this method are consistent with polysomnography, which is recognised as the best method for measuring sleep quality currently. At the same time, the device can make some additional contributions to monitoring personal health. Combining personal activity information collected by GPS with heart rate information collected by heart rate sensors and using process mining to analyse those data, they can provide good recommendations for personal health care.

Keywords

• biomedical measurement
• sleep
• data mining
• health care
• cardiology
• patient monitoring
• diseases
• fuzzy reasoning
• sensors
• biomedical equipment
• medical computing
• sleep phase
• heart rate variation
• sleep quality
• personal activity information
• heart rate information
• heart rate sensors
• process mining
• noninvasive sensor devices
• personalised health care
• pressure sensors
• heart rate devices
• diseases
• fuzzy inference
• body movement