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]).

Manipulation Tasks in Hazardous Environments Using a Teleoperated Robot: A Case Study at CERN

Remote robotic systems are employed in the CERN accelerator complex to perform different tasks, such as the safe handling of cables and their connectors. Without dedicated control, these kinds of actions are difficult and require the operators' intervention, which is subjected to dangerous external agents. In this paper, two novel modules of the CERNTAURO framework are presented to provide a safe and usable solution for managing optical fibres and their connectors. The first module is used to detect touch and slippage, while the second one is used to regulate the grasping force and contrast slippage. The force reference was obtained with a combination of object recognition and a look-up table method. The proposed strategy was validated with tests in the CERN laboratory, and the preliminary experimental results demonstrated statistically significant increases in time-based efficiency and in the overall relative efficiency of the tasks.

Development of the Biomechanical Technologies for the Modeling of Major Segments of the Human Body: Linking the Past with the Present

Simple Summary

The procedures of body measurement are as old as the inception of the scientific method. The human being has always had the necessity to shape the environment to its own needs, to care for the body and to improve quality of life. Over the centuries, several methods have been developed to measure body size. With the development of measurement sciences, technological tools as well as computational tools have become increasingly precise. This review paper aims to historically review the development of methods for the measurement of body segments from a biomechanical point of view, highlighting the link with the technologies available today.

Abstract

The knowledge of human body proportions and segmental properties of limbs, head and trunk is of fundamental importance in biomechanical research. Given that many methods are employed, it is important to know which ones are currently available, which data on human body masses, lengths, center of mass (COM) location, weights and moment of inertia (MOI) are available and which methods are most suitable for specific research purposes. Graphical, optical, x-ray and derived techniques, MRI, laser, thermography, has been employed for in-vivo measurement, while direct measurements involve cadaveric studies with dissection and various methods of acquiring shape and size of body segments. The present review examines the literature concerning human body segments’ properties for biomechanical purposes starting with a historical examination. It emerges that data obtained in studies on cadaveric specimens are still accurate in comparison to more recent technologies, whilst technological tools currently available are manifolds, each one with proper advantages and disadvantages. Classical studies were focused mainly on white men, while in recent years, the available data on body segments have been extended to children, women, and other races. Additionally, data on special populations (obese, pregnant women) are starting to appear in the scientific literature.