## 1 Introduction  
Human motion prediction is essential for the safe and effective interaction between humans and robots in collaborative spaces. For instance, it has been outlined as a fundamental need for robots that consciously navigate in the presence of humans, in order to develop a \"human-aware\" navigation strategy [1]. Additionally, fueled by recently proposed specifications (ISO/TS 15066:2016--Robots and robotic devices--Collaborative robots), it will likely be essential in the near future for the realization of the \"intimate collaboration\" or the \"close proximity interaction\" paradigms described by [2, 3], respectively. In particular, studies have shown recently that human motion prediction technologies have the potential to improve the efficiency of the team and the perception of safety and comfort by the user in different settings in which persons and robots share the same physical workspace [4; 5]. Likewise, human motion prediction may also be relevant in assistive devices (active prosthesis/orthosis/exoskeletons) to switch between different locomotion modes [6; 7], by changing the control algorithms or by modifying the mechanical properties of the assistive devices [8]. Automatic anticipated detection of walking turns from body kinematics is a plausible approach as it is a well-known fact in neurosciences that postural adjustments are initiated prior to the actual change in the heading direction, and that these adjustments are reflected in the kinematics of body segments (see for instance [9]). It is also a feasible approach as nowadays there are different reliable technologies that allow to sample these variables in real time, with optical sensors and Inertial Measurement Units being the most representative. Some pioneering applications have confirmed the validity of this approach [10; 11]. Finally, it is a promising approach for human robot collaboration as it may be used to schedule and execute robotic navigation plans that are compliant with the expected trajectories of the user [1], furthermore contributing to improve the safety and comfort of the interacting humans [11]. In particular, the state-of-the-art comprises a vast amount of knowledge about the role of upper body kinematics during locomotion steering that suggests that turn detection systems can be implemented from them, especially from gaze, trunk and head kinematics (see Figure 1). For instance, it has been shown that head anticipates motion direction to provide a stable reference frame that helps to coordinate the motion of the other body segments [12]. Likewise, it was argued that trunk roll and yaw control help the lower segments to further redirect the Body Center of Mass in the new direction of travel [13]. Moreover, head yaw and trunk tilt were shown to be connected to eye movements in order to stabilize the optic flow needed to control the upcoming steering maneuver [14]. Different steering maneuvers, such as lane-changes [15] or obstacle circumventing [16], were shown to be eventually preceded by some postural adaptations in the upper trunk. Steering under special circumstances, such as manual wheelchair navigation [17] or while pushing a cart [18], changed the preceding upper trunk motion as compared to normal biped walking. However, it is difficult to interpret this knowledge from the point of view of turn prediction systems because the original studies were fundamentally oriented to study human motor and cognitive controls. Additionally, the experimental methodology was not consistent throughout the different works and the relevant data was spread over different scientific sources in an unstructured way and not always given in an explicit form, with different works containing partial, complementary or even contradictory results.