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This article proposes a navigation scheme for a wheeled robot in unknown environments. The navigation scheme consists of obstacle boundary following (OBF), target seeking (TS), and vertex point seeking (VPS) behaviors and a behavior supervisor. The OBF behavior is achieved by a fuzzy controller (FC). This article formulates the FC design problem as a new constrained multiobjective optimization problem and finds a set of nondominated FC solutions through the combination of expert knowledge and data-driven multiobjective ant colony optimization. The TS behavior is achieved by new fuzzy proportional-integral-derivative (PID) and proportional-derivative (PD) controllers that control the orientation and speed of the robot, respectively. The VPS behavior is proposed to shorten the navigation route by controlling the robot to move toward a new subgoal determined from the vertex point of an obstacle. A new behavior supervisor that manages the switching among the OBF, TS, and VPS behaviors in unknown environments is proposed. In the navigation of a real robot, a new robot localization method through the fusion of encoders and an infrared localization sensor using a particle filter is proposed. Finally, this article presents simulations and experiments to verify the feasibility and advantages of the navigation scheme.

Manual titration of positive airway pressure (PAP) is a gold standard to provide an optimal pressure for the treatment of obstructive sleep apnea-hypopnea syndrome (OSAS). Since manual titration studies were costly and time-consuming, many statistical models for predicting effective PAPs were reported. However, the prediction accuracies of the models associated with nocturnal parameters still remain low. This study proposes a fuzzy neural prediction network (FNPN) with input candidate variables, selected among easily available measurements (e.g., body mass index (BMI), waist circumstance (WC), and body composition) and OSAS related questionnaires, to rapidly predict an optimal PAP. The FNPN comprises fuzzy rules and is characterized with the ability of automatic rule growing and pruning from training data. A total of 147 participants from April 2018 to April 2019 were enrolled in Taichung Veterans General Hospital, Taiwan. After two selection processes for feature extraction, WC and BMI were the significant variables for entering the FNPN to predict optimal PAP. Experimental results showed that the average successful prediction rate of the proposed method was 71.8%. This study also found that Epworth sleepiness scales (ESS) and body composition, such as visceral fat area and percent body fat, were excluded in the final prediction model. Compared with existing models, the proposed prediction approach provided a rapid prediction of optimal PAP with higher accuracy.

This paper proposes a new method for the tracking of three-dimensional (3D) hand palms from the whole human standing body using fuzzy rule-based fusion of explainable fuzzy features from a monocular video. The characteristics of this method include visually and linguistically explainable fuzzy features and rules and computational efficiency. This paper first tracks the two-dimensional (2D) palms using the following four fuzzy features: optical flows, the degree of a pixel in the foreground, skin color information, and the search area around a hand palm candidate from a segmented body. Afterward, a fuzzy system is proposed to fuse the four fuzzy features to estimate the 2D palm positions. Localization of the elbows is based on the estimated palm locations, human body skeletons, and body contour. The 2D palms and elbows are tracked using a modified particle filter. To estimate the depth of each palm, the locations of the palm and elbow are fed as inputs to a neural fuzzy system. The 3D palm tracking result is applied to a robot upper-body imitation system. Experiments with comparisons of different hand palm tracking methods are performed to verify the real-time computational ability and accuracy of the proposed method.