# Biometric Vibration Signal Detection Devices for Swallowing Activity Monitoring
## 3 Results and Discussion
### Experimental Results  
Eight healthy normal subjects were recruited, generating a total of 65 swallows each for seven types of swallowing. The experiment was conducted according to the protocol sequence described in Table 2. In Figure 4a, the gray line is the differential signal from two accelerometers, and the black line is the bandpass-filtered signal with 90-120 Hz. The red dot is an event detected as swallowing using the adaptive threshold described in the previous section, and the purple cross is the time of the event marker checked by the subject and the experimental supervisor. True positives were determined and the accuracy of the algorithm was calculated by comparing the swallowing detected by the algorithm with the time recorded in the event marker. Notably, accounting for human reaction speed, any swallowing signal detected by the algorithm within a time frame of \(\pm\)1 s from the subject-recorded event marker was adjudged as a true positive. Figure 4: (**a**) Detection result of swallowing events during 7 types of activities and confounding motions; (**b**) standard deviation (SD) of the filtered signal and the adaptive thresholds for detecting the swallowing events. Figure 3: Differential sensing for measurement of swallowing during physical activities. (**a**) Swallowing while body swaying side to side; (**b**) swallowing while walking. Figure 4b shows the standard deviation calculated in a 0.05 s window using the filtered signal. The blue line is the standard deviation (SD), the red line is the adaptive threshold, and the red dot is the detected swallowing (SW) by the algorithm. Swallowing was mostly detected in each activity during the scenario that lasted until about 1100 s. In the confounding phase that takes place after 1100 s, the SD increases significantly, and the adaptive threshold also increases accordingly. Additionally, although there was no swallowing scenario in the protocol after 1200 s, there were cases where the subject swallowed a large amount of saliva accumulated in the mouth while speaking or chewing gum. The swallowing event was recorded by the subject in the event marker system, and also detected as swallowing in the algorithm. Two accelerometers were used to develop a device and algorithm that can demonstrate robust performance even when accompanied by movements of the entire body other than swallowing. To compare the results with previous studies, the results of detecting swallowing using one accelerometer were compared with the results calculated using two accelerometers. Table 3 shows the results of the analysis using signals acquired from one of the two accelerometers for each swallowing type, and Table 4 shows the results of the analysis using both accelerometers. 'Saliva' means swallowing saliva, 'Sitting' indicates swallowing while sitting, 'B and F' indicates swallowing while the body is swaying back and forth, 'S2S' indicates swallowing while the body is swaying side to side, 'Walking' indicates swallowing while walking, 'Cycling' indicates swallowing while cycling, and 'Running' indicates swallowing while running. In Table 3, for swallowing saliva, the precision is 48.9 and the sensitivity is 55, which is very low. The swallowed saliva is difficult to classify as swallowing when the amount of liquid is small. However, in the results in Table 4 using two accelerometers, saliva swallowing is more accurately classified as swallowing than when only one accelerometer is used. Except for swallowing in body movement from side to side, all values show an improvement of about 5% points. In the case of food intake without any special events, the dominant situation is that swallowing is performed while sitting still in a chair. Even in this case, detecting swallowing was improved when two accelerometers were used rather than a single accelerometer. movement, such as walking and swaying the body.