The Design Evolution of a Sensing and Force-Feedback Exoskeleton Robotic Glove for Hand Rehabilitation Application

2016 ◽  
Vol 8 (5) ◽  
Author(s):  
Pinhas Ben-Tzvi ◽  
Jerome Danoff ◽  
Zhou Ma
Keyword(s):  
Contact Force ◽  
Force Feedback ◽  
Gaussian Mixture ◽  
Contact Forces ◽  
Human Hand ◽  
Hand Rehabilitation ◽  
Mixture Regression ◽  
Design Evolution ◽  
Grasping Force ◽  
Force Contact

This paper presents the design evolution of the sensing and force-feedback exoskeleton robotic (SAFER) glove with application to hand rehabilitation. The hand grasping rehabilitation system is designed to gather kinematic and force information from the human hand and then playback the motion to assist a user in common hand grasping movements, such as grasping a bottle of water. Grasping experiments were conducted where fingertip contact forces were measured by the SAFER glove. These forces were then modeled based on a machine learning approach to obtain the learned contact force distributions. Using these distributions, fingertip force trajectories were generated with a Gaussian mixture regression (GMR) method. To demonstrate the glove's effectiveness to manipulate the hand, experiments were performed using the glove to demonstrate grasping capabilities on several objects. Instead of defining a grasping force, contact force trajectories were used to control the SAFER glove in order to actuate a user's hand while carrying out a learned grasping task.

Sensing and Force-Feedback Exoskeleton Robotic (SAFER) Glove Mechanism for Hand Rehabilitation

2015 ◽  
Author(s):  
Zhou Ma ◽  
Pinhas Ben-Tzvi ◽  
Jerome Danoff
Keyword(s):  
Force Feedback ◽  
Force Sensor ◽  
Gaussian Mixture ◽  
Contact Forces ◽  
Learning System ◽  
Human Hand ◽  
Hand Rehabilitation ◽  
Mixture Regression ◽  
Grasping Force ◽  
Force Contact

This paper presents the design and application of the SAFER glove in the field of hand rehabilitation. The authors present preliminary results on a new hand grasping rehabilitation learning system that is designed to gather kinematic and force information of the human hand and to playback the motion to assist a user in common hand grasping movements, such as grasping a bottle of water. The fingertip contact forces during grasping have been measured by the SAFER Glove from 12 subjects. The measured fingertip contact forces were modeled with Gaussian Mixture Model (GMM) based on machine learning approach. The learned force distributions were then used to generate fingertip force trajectories with a Gaussian Mixture Regression (GMR) method. To demonstrate the glove’s potential to manipulate the hand, experiments with the glove fitted on a wooden hand to grasp various objects were performed. Instead of defining a grasping force, contact force trajectories were used to control the SAFER Glove to actuate/assist this hand while carrying out a learned grasping task. To prove that the hand can be driven safely by the haptic mechanism, force sensor readings placed between each finger and the mechanism have been plotted. The experimental results show the potential of the proposed system in future hand rehabilitation therapy.

Measurements of human force control during a constrained arm motion using a force-actuated joystick

1995 ◽  
Vol 73 (3) ◽  
pp. 1201-1222 ◽  
Author(s):  
J. McIntyre ◽  
E. V. Gurfinkel ◽  
M. I. Lipshits ◽  
J. Droulez ◽  
V. S. Gurfinkel
Keyword(s):  
Real Time ◽  
Contact Force ◽  
Internal Model ◽  
Force Feedback ◽  
Contact Forces ◽  
Proprioceptive Information ◽  
Constrained Motion ◽  
Surface Geometry ◽  
The Subject ◽  
Compliance Model

1. When interacting with the environment, human arm movements may be prevented in certain directions (i.e., when sliding the hand along a surface) resulting in what is called a "constrained motion." In the directions that the movement is restricted, the subject is instead free to control the forces against the constraint. 2. Control strategies for constrained motion may be characterized by two extreme models. Under the active compliance model, an essentially feedback-based approach, measurements of contact force may be used in real time to modify the motor command and precisely control the forces generated against the constraint. Under the passive compliance model the motion would be executed in a feedforward manner, using an internal model of the constraint geometry. The feedforward model relies on the compliant behavior of the passive mechanical system to maintain contact while avoiding excessive contact forces. 3. Subjects performed a task in which they were required to slide the hand along a rigid surface. This task was performed in a virtual force environment in which contact forces were simulated by a two-dimensional force-actuated joystick. Unknown to the subject, the orientation of the surface constraint was varied from trial to trial, and contact force changes induced by these perturbations were measured. 4. Subjects showed variations in contact force correlated with the direction of the orientation perturbation. "Upward" tilts resulted in higher contact forces, whereas "downward" tilts resulted in lower contact forces. This result is consistent with a feedforward-based control of a passively compliant system. 5. Subject responses did not, however, correspond exactly to the predictions of a static analysis of a passive, feedforward-controlled system. A dynamic analysis reveals a much closer resemblance between a passive, feedforward model and the observed data. Numerical simulations demonstrate that a passive, dynamic system model of the movement captures many more of the salient features observed in the measured human data. 6. We conclude that human subjects execute surface-following motions in a largely feedforward manner, using an a priori model of the surface geometry. The evidence does not suggest that active, real time use of force feedback is used to guide the movement or to control limb impedance. We do not exclude, however, the possibility that the internal model of the constraint is updated at somewhat longer latencies on the basis of proprioceptive information.

Force Optimization Approaches for Common Anthropomorphic Grasps

2016 ◽  
Author(s):  
Aimee Cloutier ◽  
James Yang
Keyword(s):  
Point Contact ◽  
Contact Forces ◽  
Applied Force ◽  
Linear Matrix ◽  
Human Hand ◽  
Numerical Examples ◽  
Contact Points ◽  
Force Optimization ◽  
Grasping Forces ◽  
Grasping Force

A smart choice of contact forces between robotic grasping devices and objects is important for achieving a balanced grasp. Too little applied force may cause an object to slip or be dropped, and too much applied force may cause damage to delicate objects. Prior methods of grasping force optimization in literature have mostly assumed grasp only at the fingertips but have rarely considered how the whole hand grasps more common to anthropomorphic hands affect the optimization of grasping forces. Further, although numerical examples of grasping force optimization methods are routinely provided, it is often difficult to compare the performance of separate methods when they are evaluated using different parameters, such as the type of grasping device, the object grasped, and the contact model, among other factors. This paper presents three optimization approaches (linear, nonlinear, and nonlinear with linear matrix inequality (LMI) friction constraints) which are compared for an anthropomorphic hand. Numerical examples are provided for three types of grasp commonly performed by the human hand (cylindrical grasp, tip grasp, and tripod grasp) using both soft finger contact and point contact with friction models. Contact points between the hand and the object are predetermined. Results are compared based on their accuracy, computational efficiency, and other various benefits and drawbacks unique to each method. Future work will extend the problem of grasping force optimization to include consideration for variable forces and object manipulation.

scholarly journals Grasping Force Optimization Approaches for Anthropomorphic Hands

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Aimee Cloutier ◽  
James Yang
Keyword(s):  
Computational Efficiency ◽  
Linear Method ◽  
Contact Forces ◽  
Applied Force ◽  
Contact Friction ◽  
Linear Matrix ◽  
Human Hand ◽  
Contact Points ◽  
Force Optimization ◽  
Grasping Force

An appropriate choice of contact forces for anthropomorphic robotic grasping devices is important for achieving a balanced grasp. Too little applied force may cause an object to slip or be dropped, and too much applied force may cause damage to delicate objects. Prior methods of grasping force optimization (GFO) in the literature can be difficult to compare due to variability in the parameters, such as the type of grasping device, the object grasped, and the contact model, among other factors. Additionally, methods are typically tested on a very small number of scenarios and may not be as robust in other settings. This paper presents a detailed analysis of three optimization approaches based on the literature, comparing them on the basis of accuracy and computational efficiency. Numerical examples are provided for three types of grasp commonly performed by the human hand (cylindrical grasp, tip grasp, and tripod grasp) using both soft finger (SF) contact and hard finger (HF) contact friction models. For each method and grasping example, an external force is applied to the object in eighteen different directions to provide a more complete picture of the methods' performance. Contact points between the hand and the object are predetermined (given). A comparison of the results showed that the nonlinear and linear matrix inequality (LMI) approaches perform best in terms of accuracy, while the computational efficiency of the linear method is stronger unless the number of contact points and segments becomes too large. In this case, the nonlinear method performs more quickly. Future work will extend the problem of GFO to real-time implementation, and a related work (briefly addressed here) examines the sensitivity of optimization methods to variability in the contact locations.

scholarly journals Learning Grasp Configuration Through Object-Specific Hand Primitives for Posture Planning of Anthropomorphic Hands

2021 ◽  
Vol 15 ◽  
Author(s):  
Bingchen Liu ◽  
Li Jiang ◽  
Shaowei Fan ◽  
Jinghui Dai
Keyword(s):  
Degrees Of Freedom ◽  
Dimensional Space ◽  
Principal Component ◽  
Past Research ◽  
Shape Descriptor ◽  
Gaussian Mixture ◽  
Human Hand ◽  
Mixture Regression ◽  
Low Dimensional ◽  
Anthropomorphic Hands

The proposal of postural synergy theory has provided a new approach to solve the problem of controlling anthropomorphic hands with multiple degrees of freedom. However, generating the grasp configuration for new tasks in this context remains challenging. This study proposes a method to learn grasp configuration according to the shape of the object by using postural synergy theory. By referring to past research, an experimental paradigm is first designed that enables the grasping of 50 typical objects in grasping and operational tasks. The angles of the finger joints of 10 subjects were then recorded when performing these tasks. Following this, four hand primitives were extracted by using principal component analysis, and a low-dimensional synergy subspace was established. The problem of planning the trajectories of the joints was thus transformed into that of determining the synergy input for trajectory planning in low-dimensional space. The average synergy inputs for the trajectories of each task were obtained through the Gaussian mixture regression, and several Gaussian processes were trained to infer the inputs trajectories of a given shape descriptor for similar tasks. Finally, the feasibility of the proposed method was verified by simulations involving the generation of grasp configurations for a prosthetic hand control. The error in the reconstructed posture was compared with those obtained by using postural synergies in past work. The results show that the proposed method can realize movements similar to those of the human hand during grasping actions, and its range of use can be extended from simple grasping tasks to complex operational tasks.

scholarly journals A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5505
Author(s):  
Guanwen Ding ◽  
Yubin Liu ◽  
Xizhe Zang ◽  
Xuehe Zhang ◽  
Gangfeng Liu ◽  
...  
Keyword(s):  
Learning Strategy ◽  
Gaussian Mixture ◽  
Skill Learning ◽  
Robotic Assembly ◽  
Hand Movements ◽  
Human Hand ◽  
Mixture Regression ◽  
Movement Primitives ◽  
Task Learning ◽  
Assembly Tasks

In manufacturing, traditional task pre-programming methods limit the efficiency of human–robot skill transfer. This paper proposes a novel task-learning strategy, enabling robots to learn skills from human demonstrations flexibly and generalize skills under new task situations. Specifically, we establish a markerless vision capture system to acquire continuous human hand movements and develop a threshold-based heuristic segmentation algorithm to segment the complete movements into different movement primitives (MPs) which encode human hand movements with task-oriented models. For movement primitive learning, we adopt a Gaussian mixture model and Gaussian mixture regression (GMM-GMR) to extract the optimal trajectory encapsulating sufficient human features and utilize dynamical movement primitives (DMPs) to learn for trajectory generalization. In addition, we propose an improved visuo-spatial skill learning (VSL) algorithm to learn goal configurations concerning spatial relationships between task-relevant objects. Only one multioperation demonstration is required for learning, and robots can generalize goal configurations under new task situations following the task execution order from demonstration. A series of peg-in-hole experiments demonstrate that the proposed task-learning strategy can obtain exact pick-and-place points and generate smooth human-like trajectories, verifying the effectiveness of the proposed strategy.

scholarly journals Design and Calibration of a Force/Tactile Sensor for Dexterous Manipulation

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 966 ◽  
Author(s):  
Marco Costanzo ◽  
Giuseppe De Maria ◽  
Ciro Natale ◽  
Salvatore Pirozzi
Keyword(s):  
Experimental Validation ◽  
Sensory System ◽  
Tactile Sensor ◽  
Contact Forces ◽  
Dexterous Manipulation ◽  
Soft Contact ◽  
Grasping Force ◽  
Sense Of Touch ◽  
Human Sense ◽  
Robotic Applications

This paper presents the design and calibration of a new force/tactile sensor for robotic applications. The sensor is suitably designed to provide the robotic grasping device with a sensory system mimicking the human sense of touch, namely, a device sensitive to contact forces, object slip and object geometry. This type of perception information is of paramount importance not only in dexterous manipulation but even in simple grasping tasks, especially when objects are fragile, such that only a minimum amount of grasping force can be applied to hold the object without damaging it. Moreover, sensing only forces and not moments can be very limiting to securely grasp an object when it is grasped far from its center of gravity. Therefore, the perception of torsional moments is a key requirement of the designed sensor. Furthermore, the sensor is also the mechanical interface between the gripper and the manipulated object, therefore its design should consider also the requirements for a correct holding of the object. The most relevant of such requirements is the necessity to hold a torsional moment, therefore a soft distributed contact is necessary. The presence of a soft contact poses a number of challenges in the calibration of the sensor, and that is another contribution of this work. Experimental validation is provided in real grasping tasks with two sensors mounted on an industrial gripper.

scholarly journals Dynamics of Multibody Systems With Spherical Clearance Joints

2005 ◽  
Author(s):  
P. Flores ◽  
J. Ambro´sio ◽  
J. C. P. Claro ◽  
H. M. Lankarani
Keyword(s):  
Contact Force ◽  
Multibody Systems ◽  
Contact Forces ◽  
Force Model ◽  
Clearance Joints ◽  
Continuous Contact ◽  
Clearance Joint ◽  
Contact Force Model ◽  
Dynamics Of Multibody Systems ◽  
The Impact

This work deals with a methodology to assess the influence of the spherical clearance joints in spatial multibody systems. The methodology is based on the Cartesian coordinates, being the dynamics of the joint elements modeled as impacting bodies and controlled by contact forces. The impacts and contacts are described by a continuous contact force model that accounts for geometric and mechanical characteristics of the contacting surfaces. The contact force is evaluated as function of the elastic pseudo-penetration between the impacting bodies, coupled with a nonlinear viscous-elastic factor representing the energy dissipation during the impact process. A spatial four bar mechanism is used as an illustrative example and some numerical results are presented, being the efficiency of the developed methodology discussed in the process of their presentation. The results obtained show that the inclusion of clearance joints in the modelization of spatial multibody systems significantly influences the prediction of components’ position and drastically increases the peaks in acceleration and reaction moments at the joints. Moreover, the system’s response clearly tends to be nonperiodic when a clearance joint is included in the simulation.

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