Name
Title | ||
|---|---|---|
Learning Room Structure and Activity Patterns Using RF Sensing for In-Home Monitoring of Older Adults. |
Abstract | ||
|---|---|---|
In this paper, we describe two methods for learning the room structure via radio wave reflections for longitudinal health monitoring of older adults in a naturalistic home setting. The goal is to use these data as part of a monitoring system that can be easily installed in a home with minimal configuration, for the purpose of detecting very early signs of illness and functional decline. Two studies are conducted using RF (radio frequency) sensing. The first method learns the structure from the RF clutter patterns, and uses the beat frequency of the maximum peak in each chirp to calculate the wall position. The second method learns the room structure from active movement patterns, and uses the open space between the clusters of active movement patterns to estimate the possible wall locations. Comparing the two results from these methods provides a more robust wall location. In addition, a background filter is designed based on the wall position, and the activity level of people in different rooms is estimated using a fuzzy rule system applied to the RF motion data. We evaluate our approach in a naturalistic setting. Preliminary results indicate that RF sensors can be used to capture both room structure and overall activity patterns. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BIBM49941.2020.9313335 | BIBM |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nuerzati Resuli | 1 | 0 | 0.34 |
| Marjorie Skubic | 2 | 1045 | 105.36 |
| Scott Kovaleski | 3 | 0 | 0.34 |