Francesca Cordella

Literature Review on Needs of Upper Limb Prosthesis Users.

The loss of one hand can significantly affect the level of autonomy and the capability of performing daily living, working and social activities. The current prosthetic solutions contribute in a poor way to overcome these problems due to limitations in the interfaces adopted for controlling the prosthesis and to the lack of force or tactile feedback, thus limiting hand grasp capabilities. This paper presents a literature review on needs analysis of upper limb prosthesis users, and points out the main critical aspects of the current prosthetic solutions, in terms of users satisfaction and activities of daily living they would like to perform with the prosthetic device. The ultimate goal is to provide design inputs in the prosthetic field and, contemporary, increase user satisfaction rates and reduce device abandonment. A list of requirements for upper limb prostheses is proposed, grounded on the performed analysis on user needs. It wants to (i) provide guidelines for improving the level of acceptability and usefulness of the prosthesis, by accounting for hand functional and technical aspects; (ii) propose a control architecture of PNS-based prosthetic systems able to satisfy the analyzed user wishes; (iii) provide hints for improving the quality of the methods (e.g., questionnaires) adopted for understanding the user satisfaction with their prostheses.

Control of Prosthetic Hands via the Peripheral Nervous System.

This paper intends to provide a critical review of the literature on the technological issues on control and sensorization of hand prostheses interfacing with the Peripheral Nervous System (i.e., PNS), and their experimental validation on amputees. The study opens with an in-depth analysis of control solutions and sensorization features of research and commercially available prosthetic hands. Pros and cons of adopted technologies, signal processing techniques and motion control solutions are investigated. Special emphasis is then dedicated to the recent studies on the restoration of tactile perception in amputees through neural interfaces. The paper finally proposes a number of suggestions for designing the prosthetic system able to re-establish a bidirectional communication with the PNS and foster the prosthesis natural control.

Human Hand Motion Analysis and Synthesis of Optimal Power Grasps for a Robotic Hand

Biologically inspired robotic systems can find important applications in biomedical robotics, since studying and replicating human behaviour can provide new insights into motor recovery, functional substitution and human-robot interaction. The analysis of human hand motion is essential for collecting information about human hand movements useful for generalizing reaching and grasping actions on a robotic system. This paper focuses on the definition and extraction of quantitative indicators for describing optimal hand grasping postures and replicating them on an anthropomorphic robotic hand. A motion analysis has been carried out on six healthy human subjects performing a transverse volar grasp. The extracted indicators point to invariant grasping behaviours between the involved subjects, thus providing some constraints for identifying the optimal grasping configuration. Hence, an optimization algorithm based on the Nelder-Mead simplex method has been developed for determining the optimal grasp configuration of a robotic hand, grounded on the aforementioned constraints. It is characterized by a reduced computational cost. The grasp stability has been tested by introducing a quality index that satisfies the form-closure property. The grasping strategy has been validated by means of simulation tests and experimental trials on an arm-hand robotic system. The obtained results have shown the effectiveness of the extracted indicators to reduce the non-linear optimization problem complexity and lead to the synthesis of a grasping posture able to replicate the human behaviour while ensuring grasp stability. The experimental results have also highlighted the limitations of the adopted robotic platform (mainly due to the mechanical structure) to achieve the optimal grasp configuration.

A grasp synthesis algorithm based on postural synergies for an anthropomorphic arm-hand robotic system

In this paper development, implementation and experimental validation of a grasp synthesis algorithm for an anthropomorphic robotic arm-hand system in a low dimensional posture subspace is proposed. The algorithm has been developed on the basis of the analysis of human hand postural synergies. Drawing inspiration from neuroscientific studies, a database of grasps has been created through the observation and the analysis of the human finger posture during reaching and grasping tasks of several objects. The optimal hand configuration and wrist pose have been determined by applying an optimization procedure grounded on a stochastic method. The grasp synthesis algorithm has been validated in simulation and on a real arm-hand robotic platform consisting of the KUKA LWR 4+ robot arm and the DLR-HIT Hand II. The experimental results have validated the hypothesis made during algorithm implementation and have shown that the armhand robotic platform is able to perform the hand preshaping configurations predicted by the grasp synthesis algorithm.

Learning by Demonstration for Motion Planning of Upper-Limb Exoskeletons

The reference joint position of upper-limb exoskeletons is typically obtained by means of Cartesian motion planners and inverse kinematics algorithms with the inverse Jacobian; this approach allows exploiting the available Degrees of Freedom (i.e. DoFs) of the robot kinematic chain to achieve the desired end-effector pose; however, if used to operate non-redundant exoskeletons, it does not ensure that anthropomorphic criteria are satisfied in the whole human-robot workspace. This paper proposes a motion planning system, based on Learning by Demonstration, for upper-limb exoskeletons that allow successfully assisting patients during Activities of Daily Living (ADLs) in unstructured environment, while ensuring that anthropomorphic criteria are satisfied in the whole human-robot workspace. The motion planning system combines Learning by Demonstration with the computation of Dynamic Motion Primitives and machine learning techniques to construct task- and patient-specific joint trajectories based on the learnt trajectories. System validation was carried out in simulation and in a real setting with a 4-DoF upper-limb exoskeleton, a 5-DoF wrist-hand exoskeleton and four patients with Limb Girdle Muscular Dystrophy. Validation was addressed to (i) compare the performance of the proposed motion planning with traditional methods; (ii) assess the generalization capabilities of the proposed method with respect to the environment variability. Three ADLs were chosen to validate the system: drinking, pouring and lifting a light sphere. The achieved results showed a 100% success rate in the task fulfillment, with a high level of generalization with respect to the environment variability. Moreover, an anthropomorphic configuration of the exoskeleton is always ensured.

Learning by Demonstration for Planning Activities of Daily Living in Rehabilitation and Assistive Robotics

This letter presents a motion planning system for robotic devices to be adopted in assistive or rehabilitation scenarios. The proposed system is grounded on a learning by demonstration approach based on dynamic movement primitives (DMP) and presents a high level of generalization allowing the user to perform activities of daily living. The proposed approach has been experimentally validated on a robotic arm (i.e., the Kuka LWR4+) attached to a human subject wrist. Two experimental sessions have been carried out in order to: 1) evaluate the differences between our approach and the one proposed in “Dynamical movement primitives: Learning attractor models for motor behaviors” (A. J. Ijspeert et al., Neural Comput., 2013) in terms of reconstruction error between the demonstrated trajectory and the learned one, and in terms of memory size required to record the database of DMP parameters; and 2) measure the generalization level of the proposed system with respect to the variation of the object positions by evaluating the success rate of the task execution. The experimental results demonstrate that the proposed approach allows 1) reproducing the users personal motion style with high accuracy and 2) efficiently generalizing with respect to the change of object position. Furthermore, a significant reduction of memory allocation for the database can be achieved, with a consequent significant computational time saving.

Development and preliminary testing of an instrumented object for force analysis during grasping.

This paper presents the design and realization of an instrumented object for force analysis during grasping. The object, with spherical shape, has been constructed with three contact areas in order to allow performing a tripod grasp. Force Sensing Resistor (FSR) sensors have been employed for normal force measurements, while an accelerometer has been used for slip detection. An electronic board for data acquisition has been embedded into the object, so that only the cables for power supply exit from it. Validation tests have been carried out for: (i) comparing the force measurements with a ground truth; (ii) assessing the capability of the accelerometer to detect slippage for different roughness values; (iii) evaluating object performance in grasp trials performed by a human subject.

Feasibility and safety of shared EEG/EOG and vision-guided autonomous whole-arm exoskeleton control to perform activities of daily living

Arm and finger paralysis, e.g. due to brain stem stroke, often results in the inability to perform activities of daily living (ADLs) such as eating and drinking. Recently, it was shown that a hybrid electroencephalography/electrooculography (EEG/EOG) brain/neural hand exoskeleton can restore hand function to quadriplegics, but it was unknown whether such control paradigm can be also used for fluent, reliable and safe operation of a semi-autonomous whole-arm exoskeleton restoring ADLs. To test this, seven abled-bodied participants (seven right-handed males, mean age 30 ± 8 years) were instructed to use an EEG/EOG-controlled whole-arm exoskeleton attached to their right arm to perform a drinking task comprising multiple sub-tasks (reaching, grasping, drinking, moving back and releasing a cup). Fluent and reliable control was defined as average ‘time to initialize’ (TTI) execution of each sub-task below 3 s with successful initializations of at least 75% of sub-tasks within 5 s. During use of the system, no undesired side effects were reported. All participants were able to fluently and reliably control the vision-guided autonomous whole-arm exoskeleton (average TTI 2.12 ± 0.78 s across modalities with 75% successful initializations reached at 1.9 s for EOG and 4.1 s for EEG control) paving the way for restoring ADLs in severe arm and hand paralysis.

Design and development of a sensorized cylindrical object for grasping assessment

Aim of this work is to design and develop an instrumented cylindrical object equipped with force sensors, which is able to assess grasping performance of both human and robotic hands. The object is made of two concentric shells between which sixteen piezoresistive sensors have been located in order to measure the forces applied by the hand fingers during grasping. Furthermore, a magneto-inertial unit has been positioned inside the object for acquiring information about object orientation during manipulation. A wireless communication between the electronic boards, responsible for acquiring the data from the sensors, and a remote laptop has been guaranteed. The object has been conceived in such a way to be adopted for evaluating both power and precision grasps and for measuring the forces applied by each finger of the hand. In order to evaluate object performance, a finite element analysis for estimating the deformation of the external shell for different force values has been carried out. Moreover, to evaluate object sensitivity, a static analysis of the force transmitted by the external shell to the underlying sensors has been performed by varying the thickness of the shells. The obtained preliminary results have validated the feasibility of using the developed object for assessing grasping performed by human and robotic hands.Aim of this work is to design and develop an instrumented cylindrical object equipped with force sensors, which is able to assess grasping performance of both human and robotic hands. The object is made of two concentric shells between which sixteen piezoresistive sensors have been located in order to measure the forces applied by the hand fingers during grasping. Furthermore, a magneto-inertial unit has been positioned inside the object for acquiring information about object orientation during manipulation. A wireless communication between the electronic boards, responsible for acquiring the data from the sensors, and a remote laptop has been guaranteed. The object has been conceived in such a way to be adopted for evaluating both power and precision grasps and for measuring the forces applied by each finger of the hand. In order to evaluate object performance, a finite element analysis for estimating the deformation of the external shell for different force values has been carried out. Moreover, to evaluate object sensitivity, a static analysis of the force transmitted by the external shell to the underlying sensors has been performed by varying the thickness of the shells. The obtained preliminary results have validated the feasibility of using the developed object for assessing grasping performed by human and robotic hands.

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.