| 15 | | Our solution uses the channel state information (CSI) that is embedded in !WiFi signals to determine what exercise a user is performing. !WiFi is the best candidate because it is ubiquitous in a most settings, meaning users would not have to buy dedicated devices for exercise monitoring. Additionally, individual features can be derived from CSI, allowing the detection of different exercises. To extract these features, we implemented a |
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| 17 | | ADD TYPE OF DEEP LEARNING MODEL HERE |
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| 19 | | that searches for repetitive patterns in the CSI data using autocorrelation, which compares a signal to a copy of itself at a different point in time. |
| | 15 | Our solution uses the channel state information (CSI) that is embedded in !WiFi signals to determine what exercise a user is performing. !WiFi is the best candidate because it is ubiquitous in a most settings, meaning users would not have to buy dedicated devices for exercise monitoring. Additionally, individual features can be derived from CSI, allowing the detection of different exercises. To extract these features, we implemented a Deep Neural Network that searches for repetitive patterns in the CSI data using autocorrelation, which compares a signal to a copy of itself at a different point in time. |
