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 anaccelerometer, gyroscope, and magnetometer were used to simultaneously collect data from 20 subjects performing 10activities. Rather than using all of the available sensors, it is more efficient and economical to select part of them to maximisethe classification accuracy and avoid unnecessary computation to process information if it is not salient. Each individual sensorand several sensor combinations are trained with a quadratic-kernel support vector machine classifier. In addition, they arevalidated with an improved statistical approach, which uses data from unknown participants to test model rather than randomcross-validation to verify if the model generalises well for unknown subjects. Furthermore, the most suitable sensorcombinations 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)
| Original language | English |
|---|---|
| Pages (from-to) | 6784-6789 |
| Number of pages | 6 |
| Journal | The Journal of Engineering |
| Volume | 2019 |
| Issue number | 20 |
| DOIs | |
| Publication status | Published - 26 Sept 2019 |
| Event | IET International Radar Conference 2018 - Hilton Nanjing Riverside, Nanjing, China Duration: 17 Oct 2018 → 19 Oct 2018 |
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