FMCW radar and inertial sensing synergy for assisted living

Abstract

This study presents preliminary results about the multi-sensory recognition of indoor daily activities and fall detection, to monitor the well-being of older people at risk of physical and cognitive chronic health conditions. Five different sensors, continuous wave (CW) radar, frequency-modulated CW (FMCW) radar, and inertial measurement unit comprising an accelerometer, gyroscope, and magnetometer were used to simultaneously collect data from 20 subjects performing 10 activities. Rather than using all of the available sensors, it is more efficient and economical to select part of them to maximise the classification accuracy and avoid unnecessary computation to process information if it is not salient. Each individual sensor and several sensor combinations are trained with a quadratic-kernel support vector machine classifier. In addition, they are validated with an improved statistical approach, which uses data from unknown participants to test model rather than random cross-validation to verify if the model generalises well for unknown subjects. Furthermore, the most suitable sensor combinations are derived for each specific group of tested subjects selected (e.g. the oldest, youngest, tallest, and shortest sub-groups of participants out of the entire group).

Keywords

• cw radar
• cognition
• gyroscopes
• accelerometers
• support vector machines
• fm radar
• statistical analysis
• image classification
• tested subjects
• suitable sensor combinations
• unknown subjects
• random cross-validation
• improved statistical approach
• quadratic-kernel support vector machine classifier
• individual sensor
• unnecessary computation
• classification accuracy
• inertial measurement unit
• cw
• cognitive chronic health conditions
• physical health conditions
• older people
• fall detection
• indoor daily activities
• multisensory recognition
• assisted living
• inertial sensing synergy
• fmcw radar