The first one (Fig. 1A) consisted of elevating the trunk to the vertical position (movement phase) and remaining in that position (posture phase, hip angle ∼90°) for 5 s.
The second task (Fig. 1B) consisted of elevating both legs to the vertical position (movement phase) and remaining in that position (posture phase, hip and knee angles ∼90°) for 5 s.
Typically, such an approach consists of a training phase and a positioning phase.
During the positioning phase, the position likelihood is calculated based on the current Wi-Fi RSS measurements.
In the positioning phase, live Wi-Fi measurements will be collected and used to query the fingerprint database.
During the positioning phase, the position of a target point can be identified by comparing its measured fingerprint with the pre-stored reference fingerprint DB.
The proposed user feedback model adjusts the positioning results via placing a compensation mask over the likelihood vector (distribution) generated in the positioning phase.
Using only a few Wi-Fi scans during the positioning phase may generate a large error due to lack of informative RSS data.
The degree of fluctuation indicates the environmental complexity, that is, we can use the RSSI difference between a pair of nodes to infer the environmental complexity and the trustworthiness of the localization determined in the positioning phase.
Based on the constructed fingerprint database using the algorithms described in Section 3, the positioning phase determines the current location based on the obtained RSSI observations in real time.
Analysis of videostroboscopic findings indicated that the glottic closure, vocal fold edge, vocal fold position, phase symmetry and regularity were significantly improved in the UVFP group (P<0.001, postoperative vs. preoperative).
