Sensor Based Human Movement Controlled Hydraulic and Electrical Robotic Arm

2016 ◽  
Vol 860 ◽  
pp. 1-6 ◽  
Author(s):  
Md Shad Rahman ◽  
Rasel A. Sultan ◽  
N.M. Hasan
Keyword(s):  
Data Acquisition ◽  
Hand Movement ◽  
Human Movement ◽  
Sensor Data ◽  
Robotic Arm ◽  
Human Hand ◽  
Robotic Hand ◽  
Precise Control ◽  
Data Application ◽  
Environmental Work

This system is designed for advance Robotic control. It based on sensor data acquisition and software data processing. With those systems controlling a robotic hand by hydraulic and electric means. It is separated by two different sections. First, data acquisition section with differential sensor data (Gyro sensor, Flex sensor, Pressure sensor). Second, software processed data application system consisting of robotic hand. Specialty of this system is it gives precise control of robotic arm following human hand movement. It also gives touch and pressure feelings in robotic hand. A lot of work can be done easily with the help of it. Like this system gives remote bomb disposal, hazardous environmental work remotely, remote operation, remote medical help and so on.

Cerebral Pulse Controlled Robotic Hand for Effective Paralytic Movements

2021 ◽  
Vol 9 (8) ◽  
pp. 2201-2204
Author(s):  
Abhay Patil
Keyword(s):  
Basic Assumption ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Robotic Arm ◽  
Human Hand ◽  
Robotic Hand ◽  
Handicapped People ◽  
Alpha Beta

Abstract: There are roughly 21 million handicapped people in India, which is comparable to 2.2% of the complete populace. These people are affected by various neuromuscular problems. To empower them to articulate their thoughts, one can supply them with elective and augmentative correspondence. For this, a Brain-Computer Interface framework (BCI) has been assembled to manage this specific need. The basic assumption of the venture reports the plan, working just as a testing impersonation of a man's arm which is intended to be powerfully just as kinematically exact. The conveyed gadget attempts to take after the movement of the human hand by investigating the signs delivered by cerebrum waves. The cerebrum waves are really detected by sensors in the Neurosky headset and produce alpha, beta, and gamma signals. Then, at that point, this sign is examined by the microcontroller and is then acquired onto the engineered hand by means of servo engines. A patient that experiences an amputee underneath the elbow can acquire from this specific biomechanical arm. Keywords: Brainwaves, Brain Computer Interface, Arduino, EEG sensor, Neurosky Mindwave Headset, Robotic arm

scholarly journals A Deep Learning Approach for Robotic Arm Control using Brain-Computer Interface

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Hand Movement ◽  
Substantial Improvement ◽  
Electrode System ◽  
Computer Interface ◽  
Robotic Arm ◽  
Robotic Hand

Brain-Computer Interface (BCI) is atechnology that enables a human to communicate with anexternal stratagem to achieve the desired result. This paperpresents a Motor Imagery (MI) – Electroencephalography(EEG) signal based robotic hand movements of lifting anddropping of an external robotic arm. The MI-EEG signalswere extracted using a 3-channel electrode system with theAD8232 amplifier. The electrodes were placed on threelocations, namely, C3, C4, and right mastoid. Signalprocessing methods namely, Butterworth filter and Sym-9Wavelet Packet Decomposition (WPD) were applied on theextracted EEG signals to de-noise the raw EEG signal.Statistical features like entropy, variance, standarddeviation, covariance, and spectral centroid were extractedfrom the de-noised signals. The statistical features werethen applied to train a Multi-Layer Perceptron (MLP) -Deep Neural Network (DNN) to classify the hand movementinto two classes; ‘No Hand Movement’ and ’HandMovement’. The resultant k-fold cross-validated accuracyachieved was 85.41% and other classification metrics, suchas precision, recall sensitivity, specificity, and F1 Score werealso calculated. The trained model was interfaced withArduino to move the robotic arm according to the classpredicted by the DNN model in a real-time environment.The proposed end to end low-cost deep learning frameworkprovides a substantial improvement in real-time BCI.

scholarly journals Research on Hand Action Pattern Recognition of Bionic Limb Based on Surface Electromyography

2021 ◽  
Vol 271 ◽  
pp. 01030
Author(s):  
Zihan Yin
Keyword(s):  
Pattern Recognition ◽  
Human Body ◽  
Surface Electromyography ◽  
Hand Movement ◽  
Main Tool ◽  
Human Movement ◽  
Human Hand ◽  
Communication Tool ◽  
Action Pattern ◽  
Hand Action

Hands are important parts of a human body. It is not only the main tool for people to engage in productive labor, but also an important communication tool. When the hand moves, the human body produces a kind of signal named surface electromyography (sEMG), which is a kind of electrophysiological signal that accompanies muscle activity. It contains a lot of information about human movement consciousness. The bionic limb is driven by multi-degree-freedom control, which is got by converting the recognition result and this can meet the urgent need of people with disabilities for autonomous operation. A profound study of hand action pattern technology based on sEMG signals can achieve the ability of the bionic limb to distinguish the hand action fast and accurately. From the perspective of the pattern recognition of the bionic limb, this paper discussed the human hand action pattern recognition technology of sEMG. By analyzing and summarizing the current development of human hand movement recognition, the author proposed a bionic limb schema based on artificial neural network and the improved DT-SVM hand action recognition system. According to the research results, it is necessary to expand the type and total amount of hand movements and gesture recognition, in order to adapt to the objective requirements of the diversity of hand action patterns in the application of the bionic limb.

Development of a Tilt Sensor Data Glove to Wirelessly Control a Multi-Fingered Robotic Hand

2012 ◽  
Author(s):  
Hamzah N. Laimon
Keyword(s):  
Sensor Data ◽  
Human Hand ◽  
Robotic Hand ◽  
Data Glove ◽  
Input Devices ◽  
Wireless Control ◽  
Language Interpretation ◽  
Tilt Sensor ◽  
Cost Efficient ◽  
Axial Tilt

Electronic gloves are one of the most common methods used as human hand input devices. They proved to be useful in various applications such virtual reality, sign language interpretation and robotic systems. However, many of these electronic gloves tend to be either economically or computationally expensive. In contrast, this article discusses the development of a data glove that is practical and cost efficient with wireless control capabilities. It is based on placing tri-axial tilt accelerometers on the glove to map the movement of human fingers. All data acquired from the glove is transmitted wirelessly via Bluetooth connection to a computer where it can be used for various applications. The glove was used to control a five-motor tendon driven robotic hand. Tests were carried out to correlate tilt angles acquired from the glove with the appropriate motor values that will move the robotic fingers to the same position as that of the glove fingers. As a result, the robotic hand was able to mimic each human hand finger and thereby perform sign and grasp movements.

scholarly journals Data Acquisition Glove for Hand Movement Impairment Rehabilitation

2016 ◽  
Vol 12 (04) ◽  
pp. 52 ◽  
Author(s):  
Rafael Tavares ◽  
Paulo Abreu ◽  
Manuel Rodrigues Quintas
Keyword(s):  
Data Acquisition ◽  
Hand Movement ◽  
Wearable Device ◽  
Sensor Data ◽  
Motion Processing ◽  
Processing Unit ◽  
Hand Rehabilitation ◽  
Peripheral Device ◽  
Serial Peripheral Interface ◽  
Electronic Hardware

The present paper describes a data acquisition wearable device for hand rehabilitation. The main goal of this glove is to be used by patients with hand movement impairment. It has position sensors to measure the bending of synovial joints and sensors to measure the fingertip contact pressure. There is a coin motor and a LED placed on each finger to produce a vibratory and visual stimulus. The glove also tracks the hand rotation and translation using a MPU (Motion Processing Unit) that contains an accelerometer and a gyroscope. A graphical application for an HMI module was developed in order to create rehabilitation game like exercises where sensor data can be logged for further analysis by a therapist. The wearable device electronic hardware comprises a Glove module and an HMI module that communicate through SPI protocol (Serial Peripheral Interface). The wearable device supports USB connection to send data to a computer or to be used as a peripheral device in virtual or augmented reality applications.

scholarly journals Variable Thumb Moment Arm Modeling and Thumb-Tip Force Production of a Human-Like Robotic Hand

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Taylor D. Niehues ◽  
Ashish D. Deshpande
Keyword(s):  
Physical Simulation ◽  
Three Dimensional ◽  
Force Production ◽  
Human Hand ◽  
Robotic Hand ◽  
Motion Data ◽  
Moment Arms ◽  
Musculoskeletal Structure ◽  
Thumb Motion ◽  
Force Vectors

The anatomically correct testbed (ACT) hand mechanically simulates the musculoskeletal structure of the fingers and thumb of the human hand. In this work, we analyze the muscle moment arms (MAs) and thumb-tip force vectors in the ACT thumb in order to compare the ACT thumb's mechanical structure to the human thumb. Motion data are used to determine joint angle-dependent MA models, and thumb-tip three-dimensional (3D) force vectors are experimentally analyzed when forces are applied to individual muscles. Results are presented for both a nominal ACT thumb model designed to match human MAs and an adjusted model that more closely replicates human-like thumb-tip forces. The results confirm that the ACT thumb is capable of faithfully representing human musculoskeletal structure and muscle functionality. Using the ACT hand as a physical simulation platform allows us to gain a better understanding of the underlying biomechanical and neuromuscular properties of the human hand to ultimately inform the design and control of robotic and prosthetic hands.

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