Measuring System for Synchronous Recording of Kinematic and Force Data during Handover Action of Human Dyads.

Handover actions are joint actions between two people in which an object is handed over from a giver to a receiver. This necessitates precise coordination and synchronization of both the reach and grasp kinematics and the scaling of grip forces of the actors during the interaction. For this purpose, a measurement object is presented that records the grip forces of both actors on the instrument and allows synchronous measurement of the kinematic data of both actors and the position and orientation of the instrument in space using an optical motion capture system. Additionally, the object allows one to alter its weight in a covert fashion so that it cannot be anticipated by the actors. It is shown that the four phases of a handover, (1) reach and grasp, (2) object transport, (3) object transfer, and (4) end of handover, can be clearly identified with the described measurement system. This allows the user to measure movement kinematics and grip forces during the individual phases with high precision and therefore systematically investigate handover actions. Using exemplary data, we demonstrate in this study how movement kinematics and grip forces during a handover depend on the characteristics of the object to be measured (i.e., its size or weight).

Predicting object properties based on movement kinematics.

In order to grasp and transport an object, grip and load forces must be scaled according to the object's properties (such as weight). To select the appropriate grip and load forces, the object weight is estimated based on experience or, in the case of robots, usually by use of image recognition. We propose a new approach that makes a robot's weight estimation less dependent on prior learning and, thereby, allows it to successfully grasp a wider variety of objects. This study evaluates whether it is feasible to predict an object's weight class in a replacement task based on the time series of upper body angles of the active arm or on object velocity profiles. Furthermore, we wanted to investigate how prediction accuracy is affected by (i) the length of the time series and (ii) different cross-validation (CV) procedures. To this end, we recorded and analyzed the movement kinematics of 12 participants during a replacement task. The participants' kinematics were recorded by an optical motion tracking system while transporting an object, 80 times in total from varying starting positions to a predefined end position on a table. The object's weight was modified (made lighter and heavier) without changing the object's visual appearance. Throughout the experiment, the object's weight (light/heavy) was randomly changed without the participant's knowledge. To predict the object's weight class, we used a discrete cosine transform to smooth and compress the time series and a support vector machine for supervised learning from the achieved discrete cosine transform parameters. Results showed good prediction accuracy (up to [Formula: see text], depending on the CV procedure and the length of the time series). Even at the beginning of a movement (after only 300 ms), we were able to predict the object weight reliably (within a classification rate of [Formula: see text]).

A systematic review of handover actions in human dyads.

Introduction: Handover actions are joint actions in which an object is passed from one actor to another. In order to carry out a smooth handover action, precise coordination of both actors' movements is of critical importance. This requires the synchronization of both the kinematics of the reaching movement and the grip forces of the two actors during the interaction. Psychologists, for example, may be interested in studying handover actions in order to identify the cognitive mechanisms underlying the interaction of two partners. In addition, robotic engineers may utilize insights from sensorimotor information processing in human handover as models for the design controllers in robots in hybrid (human-robot) interaction scenarios. To date, there is little knowledge transfer between researchers in different disciplines and no common framework or language for the study of handover actions. Methods: For this reason, we systematically reviewed the literature on human-human handover actions in which at least one of the two types of behavioral data, kinematics or grip force, was measured. Results: Nine relevant studies were identified. The different methodologies and results of the individual studies are here described and contextualized. Discussion: Based on these results, a common framework is suggested that, provides a distinct and straightforward language and systematics for use in future studies. We suggest to term the actors as giver and receiver, as well as to subdivide the whole action into four phases: (1) Reach and grasp, (2) object transport, (3) object transfer, and (4) end of handover to comprehensively and clearly describe the handover action. The framework aims to foster the necessary exchange between different scientific disciplines to promote research on handover actions. Overall, the results support the assumption that givers adapt their executions according to the receiver's intentions, that the start of the release of the object is processed feedforward and that the release process is feedback-controlled in the transfer phase. We identified the action planning of the receiver as a research gap.