## 6 Scope of the Study  
We have considered only healthy adult subjects (with an average age of about 27 from Table 2, column 2). Turning strategies depend on the grade of maturity of children [21] and may also vary for the elderly [43; 44]. In any case, some results showed that anticipation may not be so different for the elderly (Head Yaw-Trunk Yaw delay was found similar for young and elderly people [28]). Additionally, our analysis was limited to subjects walking at normal velocities (average walking speed of around 1.2 m/s from Table 2, column 2). No studies were found addressing this variable under significant different walking velocities to permit to extrapolate this result to a general case. For our study, we have combined results from experiments addressing diverse trajectories: corners at different angles, 180\({}^{\circ}\) turns, limacon, free trajectories, etc. (see Table 2, column 3). Thus, our analysis can be interpreted from the point of view of walking performance under general turning conditions. Likewise, all the experiments were run in a laboratory setup. It is open to investigation if they hold for daily living conditions, although [11] supported this after a preliminary study in an industrial environment. Another relevant question is that the experiments were designed such that subjectshad to undertake turning maneuvers in previously unknown environments and conditions. Re-test experiments to analyze the effect of habits or custom turns over the anticipation were not found in the literature. However, we hypothesize that gaze/head anticipation would still remain in custom turns, similar to the situation where gaze/head anticipation still happens in turns without vision ([20; 23]). The kinematics of head and trunk have been estimated in the addressed studies from optical motion capture systems using different sets of body markers and anatomical models (see Table 1). For our study, we have assumed that all these variables have been properly estimated in the original work. However, the Body Centre Of Mass estimation from body kinematics is in itself a complex problem. In the analyzed studies, different methods were used for that purpose to estimate the medio-lateral displacement of body center of mass (see Table 1): from a 14-segment anthropometric model including legs and arms in [24], from four trunk markers in [25], from head, neck and trunk segment markers in [16] and from a \"two-segments plus foot\" anthropometric model [28]. Likewise, no clear consensus has been found about how to define the actual trajectory of the subject or the actual time of trajectory change: from the time when the subject is at the maximum curvature point of the actual trajectory [21; 22], using the tangent of head trajectory [12], from \"the displacement vector of the midpoint between the three trunk markers\" [19], from the pelvis direction [20] or from \"the projection of the middle point of the head on the floor\" [29]. It is not clear how these different procedures to estimate body center of mass or trajectory heading may have affected the results. Finally, the methods used for the estimation of the kinematics of the gaze (oculography and eye tracking) were quite diverse and difficult to compare. Different signal processing procedures have been applied in the addressed works to analyze the time synchronization of kinematical variables (Turn Onset, Cross Correlation). What is more, in some works, turn onsets were timed from a different reference event. This makes a raw comparison of the quantitative reported values difficult. However, in our study, we differentially compared the turn onset times found in each study. This makes it irrelevant whether onsets were defined from different events, or even when different techniques were used to define them. Regarding the geographical anticipation analysis, the considered studies addressed the initiation of turning with respect to a geographical landmark (usually a corner pivot) defined consistently throughout the different works.