scholarly journals Speaking system for speechless people using Flex sensors

2021 ◽  
Vol 1916 (1) ◽  
pp. 012201
Author(s):  
M Singaram ◽  
P Aravin ◽  
NA Dharshine ◽  
P Ganesh ◽  
E Gokul
Keyword(s):  
Flex Sensors

scholarly journals Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1461
Author(s):  
Shun-Hsin Yu ◽  
Jen-Shuo Chang ◽  
Chia-Hung Dylan Tsai
Keyword(s):  
Support Vector Machine ◽  
Daily Life ◽  
Object Classification ◽  
Target Object ◽  
The Other ◽  
Support Vector ◽  
Wide Range ◽  
Dark Space ◽  
Flex Sensors ◽  
Shape And Size

This paper proposes an object classification method using a flexion glove and machine learning. The classification is performed based on the information obtained from a single grasp on a target object. The flexion glove is developed with five flex sensors mounted on five finger sleeves, and is used for measuring the flexion of individual fingers while grasping an object. Flexion signals are divided into three phases, and they are the phases of picking, holding and releasing, respectively. Grasping features are extracted from the phase of holding for training the support vector machine. Two sets of objects are prepared for the classification test. One is printed-object set and the other is daily-life object set. The printed-object set is for investigating the patterns of grasping with specified shape and size, while the daily-life object set includes nine objects randomly chosen from daily life for demonstrating that the proposed method can be used to identify a wide range of objects. According to the results, the accuracy of the classifications are achieved 95.56% and 88.89% for the sets of printed objects and daily-life objects, respectively. A flexion glove which can perform object classification is successfully developed in this work and is aimed at potential grasp-to-see applications, such as visual impairment aid and recognition in dark space.

scholarly journals Training with Agency-Inspired Feedback from an Instrumented Glove to Improve Functional Grasp Performance

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1173
Author(s):  
Mingxiao Liu ◽  
Samuel Wilder ◽  
Sean Sanford ◽  
Soha Saleh ◽  
Noam Y. Harel ◽  
...  
Keyword(s):  
Sensory Feedback ◽  
Small Object ◽  
Intentional Binding ◽  
Audio Feedback ◽  
Instrumented Glove ◽  
Sensory Consequence ◽  
Preliminary Study ◽  
Future Work ◽  
Flex Sensors ◽  
Functional Task

Sensory feedback from wearables can be effective to learn better movement through enhanced information and engagement. Facilitating greater user cognition during movement practice is critical to accelerate gains in motor function during rehabilitation following brain or spinal cord trauma. This preliminary study presents an approach using an instrumented glove to leverage sense of agency, or perception of control, to provide training feedback for functional grasp. Seventeen able-bodied subjects underwent training and testing with a custom-built sensor glove prototype from our laboratory. The glove utilizes onboard force and flex sensors to provide inputs to an artificial neural network that predicts achievement of “secure” grasp. Onboard visual and audio feedback was provided during training with progressively shorter time delay to induce greater agency by intentional binding, or perceived compression in time between an action (grasp) and sensory consequence (feedback). After training, subjects demonstrated a significant reduction (p < 0.05) in movement pathlength and completion time for a functional task involving grasp-move-place of a small object. Future work will include a model-based algorithm to compute secure grasp, virtual reality immersion, and testing with clinical populations.

scholarly journals Hand Gesture Vocalizer for Dumb and Deaf People

SCITECH Nepal ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 22-29
Author(s):  
Sanish Manandhar ◽  
Sushana Bajracharya ◽  
Sanjeev Karki ◽  
Ashish Kumar Jha
Keyword(s):  
Random Forest ◽  
Hand Movement ◽  
Training Model ◽  
Hand Gesture ◽  
Deaf People ◽  
Random Forest Algorithm ◽  
The Disabled ◽  
Flex Sensors ◽  
Is Design ◽  
Pictorial Form

The main purpose of this paper is to confer the system that converts a given sign used by disabled person into its appropriate textual, audio, and pictorial form using components such as Arduino Mega, Flex sensors, Accelerometer, which could be under standby a common person. A wearable glove controller is design with fl ex sensors attached on each finger, which allows the system to sense the finger movements, and aGy-61 accelerometer, which are uses to sense the hand movement of the disabled person. The wearable input glove controller sends the collected input signal to the system for processing. The system uses Random forest algorithm to predict the correct output to an accuracy of 85% on current training model.

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

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 905 ◽  
Author(s):  
Joga Dharma Setiawan ◽  
Mochammad Ariyanto ◽  
M. Munadi ◽  
Muhammad Mutoha ◽  
Adam Glowacz ◽  
...  
Keyword(s):  
Neural Network ◽  
Haptic Feedback ◽  
Control Method ◽  
Middle Finger ◽  
Object Manipulation ◽  
Robotic System ◽  
Robotic Motion ◽  
Trained Neural Network ◽  
Flex Sensors ◽  
Robotic Fingers

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.

scholarly journals Continuous Finger Gesture Recognition Based on Flex Sensors

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3986 ◽  
Author(s):  
Wei-Chieh Chuang ◽  
Wen-Jyi Hwang ◽  
Tsung-Ming Tai ◽  
De-Rong Huang ◽  
Yun-Jie Jhang
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Maximum A Posteriori ◽  
Map Estimation ◽  
A Posteriori ◽  
Sensory Data ◽  
Robust Recognition ◽  
Flex Sensors ◽  
Gated Recurrent Unit ◽  
Gesture Spotting

The goal of this work is to present a novel continuous finger gesture recognition system based on flex sensors. The system is able to carry out accurate recognition of a sequence of gestures. Wireless smart gloves equipped with flex sensors were implemented for the collection of the training and testing sets. Given the sensory data acquired from the smart gloves, the gated recurrent unit (GRU) algorithm was then adopted for gesture spotting. During the training process for the GRU, the movements associated with different fingers and the transitions between two successive gestures were taken into consideration. On the basis of the gesture spotting results, the maximum a posteriori (MAP) estimation was carried out for the final gesture classification. Because of the effectiveness of the proposed spotting scheme, accurate gesture recognition was achieved even for complicated transitions between successive gestures. From the experimental results, it can be observed that the proposed system is an effective alternative for robust recognition of a sequence of finger gestures.

scholarly journals Design and Optimization of Piezoresistive PEO/PEDOT:PSS Electrospun Nanofibers for Wearable Flex Sensors

Nanomaterials ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 2166
Author(s):  
Eve Verpoorten ◽  
Giulia Massaglia ◽  
Gianluca Ciardelli ◽  
Candido Fabrizio Pirri ◽  
Marzia Quaglio
Keyword(s):  
Low Cost ◽  
Electrospun Nanofibers ◽  
Strain Sensors ◽  
Active Materials ◽  
Design And Optimization ◽  
Piezoelectric Strain ◽  
Poly Ethylene Oxide ◽  
Poly Ethylene ◽  
Poly Styrene Sulfonate ◽  
Flex Sensors

Flexible strain sensors are fundamental devices for application in human body monitoring in areas ranging from health care to soft robotics. Stretchable piezoelectric strain sensors received an ever-increasing interest to design novel, robust and low-cost sensing units for these sensors, with intrinsically conductive polymers (ICPs) as leading materials. We investigated a sensitive element based on crosslinked electrospun nanofibers (NFs) directly collected and thermal treated on a flexible and biocompatible substrate of polydimethylsiloxane (PDMS). The nanostructured active layer based on a blend of poly(ethylene oxide) (PEO) and poly(3,4-ethylenedioxythiophene) doped with poly(styrene sulfonate) (PEDOT:PSS) as the ICP was optimized, especially in terms of the thermal treatment that promotes electrical conductivity through crosslinking of PEO and PSS, preserving the nanostructuration and optimizing the coupling between the sensitive layer and the substrate. We demonstrate that excellent properties can be obtained thanks to the nanostructured active materials. We analyzed the piezoresistive response of the sensor in both compression and traction modes, obtaining an increase in the electrical resistance up to 90%. The Gauge Factors (GFs) reflected the extraordinary piezoresistive behavior observed: 45.84 in traction and 208.55 in compression mode, which is much higher than the results presented in the literature for non-nanostructurated PEDOT.

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