Design and development of a sensorized cylindrical object for grasping assessment

From Human Hand to Grasp Surface Detection, Tracking & Analysis

Hands are paramount for dexterous interactions that humans exhibit in daily life. Understanding the intricacies of human hand-object interactions is therefore necessary. Unfortunately, the limitations of state-of-the-art technologies make capturing the full hand-object complexity unfeasible, giving rise to the need for new technological means to achieve this aim. In this work, we propose an end-to-end framework in which individualized hand models are derived and used to capture quantitative personalized hand-object interaction information, precisely, hand shape, kinematics, and contact surfaces. The results of this study serve as a proof of concept that such a framework can significantly deepen personalized hand-object interaction analyses, providing, in perspective, insights for medical diagnoses and rehabilitation, among others.Clinical relevance- Our work showcases the need to incorporate bespoke human hand models in individualized hand function assessment technologies, as hand-object interaction information is subject-dependent.

PDMeter: A Wrist Wearable Device for an at-Home Assessment of the Parkinson's Disease Rigidity

This work focuses on the design and the validation of a wearable mechatronic device for an at-home assessment of wrist stiffness in patients affected by Parkinson's Disease (PD). The device includes one actuated joint and four passive revolute joints with a high overall intrinsic backdriveability. In order to allow the user to freely move the wrist during activities of daily living, we implemented a transparent controller on the basis of the interaction force sensed by the embedded load cell. Conversely, in order to provide perturbations for estimating the wrist flexion-extension rigidity, we implemented a torque controller. Firstly, we report a pilot study that aimed at characterizing the device in terms of range of motion (ROM) allowed, transparency perceived and torque-tracking capability. Then, we present a case study in which we tested our device with seven PD patients in both drug-OFF and drug-ON conditions and we compared the measured stiffness with the one measured in fourteen healthy controls and with the outcome of the most used clinical scale (MDS-UPDRS). The device allowed to successfully estimate the stiffness as different depending on the movement direction. Indeed, extension stiffness was higher than the flexion one, accordingly to the literature. Moreover, the device allowed to discriminate both Healthy subjects from PD subjects, and PD subjects in OFF condition from PD subjects in ON condition. In conclusion, we demonstrate the feasibility of the device in measuring wrist rigidity, thus enabling the possibility to implement an at-home assessment of the PD rigidity.

Evaluation of Pressure Capacitive Sensors for Application in Grasping and Manipulation Analysis

The analysis of the human grasping and manipulation capabilities is paramount for investigating human sensory-motor control and developing prosthetic and robotic hands resembling the human ones. A viable solution to perform this analysis is to develop instrumented objects measuring the interaction forces with the hand. In this context, the performance of the sensors embedded in the objects is crucial. This paper focuses on the experimental characterization of a class of capacitive pressure sensors suitable for biomechanical analysis. The analysis was performed in three loading conditions (Distributed load, 9 Tips load, and Wave-shaped load, thanks to three different inter-elements) via a traction/compression testing machine. Sensor assessment was also carried out under human- like grasping condition by placing a silicon material with the same properties of prosthetic cosmetic gloves in between the sensor and the inter-element in order to simulate the human skin. Data show that the input-output relationship of the analyzed, sensor is strongly influenced by both the loading condition (i.e., type of inter-element) and the grasping condition (with or without the silicon material). This needs to be taken into account to avoid significant measurement error. To go over this hurdle, the sensors have to be calibrated under each specific condition in order to apply suitable corrections to the sensor output and significantly improve the measurement accuracy.

An instrumented object for studying human grasping

This paper proposes the use of an instrumented object for the study of the human grasping strategies. The proposed object is able to measure the grasping forces by means of three Force Sensitive Resistor (FSR) sensors and triaxial acceleration through an accelerometer. The object orientation angles (roll and pitch) can be estimated from the accelerometer output in quasi-static condition, whereas slippage events can be detected through the Power Spectrum Density (PSD) computation of the acceleration on at least one of the three axes. An experimental session on 7 healthy subjects has been performed; each subject used the instrumented object to perform 8 tripod grasp trials. All the sensory information, i.e. applied forces, object orientation and slippage, have been analyzed in order to evaluate the grasping strategies of the different subjects.

A stochastic algorithm for automatic hand pose and motion estimation

In this paper, a novel, robust, and simple method for automatically estimating the hand pose is proposed and validated. The method uses a multi-camera optoelectronic system and a model-based stochastic algorithm. The approach is marker-based and relies on an Unscented Kalman Filter. A hand kinematic model is introduced for constraining relative marker's positions and improving the algorithm robustness with respect to outliers and possible occlusions. The algorithm outputs are 3D coordinate measures of markers and hand joint angle values. To validate the proposed algorithm, a comparison with ground truths for angular and 3D coordinate measures is carried out. The comparative analysis shows the advantages of using the model-based stochastic algorithm with respect to standard processing software of optoelectronic cameras in terms of implementation simplicity, time consumption, and user effort. The accuracy is remarkable, with a difference of maximum 0.035r a d and 4m m with respect to angular and 3D Cartesian coordinates ground truths, respectively.