Predicting object properties based on movement kinematics

Unveiling the invisible: receivers use object weight cues for grip force planning in handover actions

Handover actions are part of our daily lives. Whether it is the milk carton at the breakfast table or tickets at the box office, we usually perform these joint actions without much conscious attention. The individual actions involved in handovers, that have already been studied intensively at the level of individual actions, are grasping, lifting, and transporting objects. Depending on the object's properties, actors must plan their execution in order to ensure smooth and efficient object transfer. Therefore, anticipatory grip force scaling is crucial. Grip forces are planned in anticipation using weight estimates based on experience or visual cues. This study aimed to investigate whether receivers are able to correctly estimate object weight by observing the giver's kinematics. For this purpose, handover actions were performed with 20 dyads, manipulating the participant role (giver/receiver) and varying the size and weight of the object. Due to the random presentation of the object weight and the absence of visual cues, the participants were unaware of the object weight from trial to trial. Kinematics were recorded with a motion tracking system and grip forces were recorded with customized test objects. Peak grip force rates were used as a measure of anticipated object weight. Results showed that receiver kinematics are significantly affected by object weight. The peak grip force rates showed that receivers anticipate object weight, but givers not. This supports the hypothesis that receivers obtain information about the object weight by observing giver's kinematics and integrating this information into their own action execution.

A machine-learning method isolating changes in wrist kinematics that identify age-related changes in arm movement

Normal aging often results in an increase in physiological tremors and slowing of the movement of the hands, which can impair daily activities and quality of life. This study, using lightweight wearable non-invasive sensors, aimed to detect and identify age-related changes in wrist kinematics and response latency. Eighteen young (ages 18-20) and nine older (ages 49-57) adults performed two standard tasks with wearable inertial measurement units on their wrists. Frequency analysis revealed 5 kinematic variables distinguishing older from younger adults in a postural task, with best discrimination occurring in the 9-13 Hz range, agreeing with previously identified frequency range of age-related tremors, and achieving excellent classifier performance (0.86 AUROC score and 89% accuracy). In a second pronation-supination task, analysis of angular velocity in the roll axis identified a 71 ms delay in initiating arm movement in the older adults. This study demonstrates that an analysis of simple kinematic variables sampled at 100 Hz frequency with commercially available sensors is reliable, sensitive, and accurate at detecting age-related increases in physiological tremor and motor slowing. It remains to be seen if such sensitive methods may be accurate in distinguishing physiological tremors from tremors that occur in neurological diseases, such as Parkinson's Disease.

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).