Grasp Posture Control of Wearable Extra Robotic Fingers with Flex Sensors Based on Neural Network

Abstract

This study proposes a data-driven control method of extra robotic fingers to assist a user in bimanual object manipulation that requires two hands. The robotic system comprises two main parts, i.e., robotic thumb (RT) and robotic fingers (RF). The RT is attached next to the user’s thumb, while the RF is located next to the user’s little finger. The grasp postures of the RT and RF are driven by bending angle inputs of flex sensors, attached to the thumb and other fingers of the user. A modified glove sensor is developed by attaching three flex sensors to the thumb, index, and middle fingers of a wearer. Various hand gestures are then mapped using a neural network. The input data of the robotic system are the bending angles of thumb and index, read by flex sensors, and the outputs are commanded servo angles for the RF and RT. The third flex sensor is attached to the middle finger to hold the extra robotic finger’s posture. Two force-sensitive resistors (FSRs) are attached to the RF and RT for the haptic feedback when the robot is worn to take and grasp a fragile object, such as an egg. The trained neural network is embedded into the wearable extra robotic fingers to control the robotic motion and assist the human fingers in bimanual object manipulation tasks. The developed extra fingers are tested for their capacity to assist the human fingers and perform 10 different bimanual tasks, such as holding a large object, lifting and operate an eight-inch tablet, and lifting a bottle, and opening a bottle cap at the same time.

Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms

“…, D is the number of parameters to be optimized. en, the search is started, the employed bees search the food source in the search area according to equation (21), and a new food source V id is generated.…”

“…where fit i is the fitness value of the solution i evaluated by its employed bees, which is proportional to the nectar amount of the food source in the position i. p i is the selection probability of the current solution. Subsequently, the onlooker bees will search in the selected food source field and determine the location of the new food source according to equation (21). Replacement of the old food source with a new food source is determined in accordance with the amount of food and through the greedy algorithm.…”

“…A traditional feature contraction method is conducted through principal component analysis (PCA) [19]. e combination of PCA and neural network can be used for finger motion recognition and posture control of the hand movements [20,21]. In addition, several new algorithms have been proposed recently to improve the search quality, scalability, efficiency, and stability, as well as the recognition rate [22][23][24].…”

EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges

“…Electromechanical sensors are capable of detecting the physical information effectively. For example, in [ 12 ], flex sensors and force sensitive sensors (FSRs) are utilized to detect the bending angle of the finger and the grasp force, respectively. The sensing information is then used for the control of robotic fingers in performing ten different tasks.…”

New Design Procedure of Transtibial ProsthesisBed Stump Using Topological Optimization Method

“…A prosthesis is a (usually removable) artificial replacement of missing body parts; the term can also describe an additional part that helps with manipulations [1]. Below the knee, or transtibial, amputation ]1causes asymmetry of the patient.…”