Abstract: Often it can be seen that men with a lost arm face severe difficulties doing daily chores. Artificial
Intelligence could be effectively used to provide some respite to those people. Neural networks and their
applications have been an active research topic since recent past in the rehabilitation robotics/machine
learning community, as it can be used to predict posture/gesture which is guided by signals from the human
brain. In this paper, a method is proposed to estimate force from Surface Electromyography (s-EMG) signals
generated by specific hand movements and then design and control a Robotic arm using Artificial Neural
Network (ANN) to replicate human arm. Here the force prediction is a Regression process. A hand model has
been successfully moved using servo motor that has been programmed based on the results obtained from
sample data. The results shown in this paper illustrate how the Robotic arm performs.
Index Terms: Surface EMG, Artificial Neural Network, Robotic arm, Regression.
Estimation of Arm Joint Angles from Surface Electromyography signals using Ar...IOSR Journals
Abstract: Vicon system is implemented in almost every motion analysis systems. It has many applications like
robotics, gaming, virtual reality and animated movies. The motion and orientation plays an important role in
the above mentioned applications. In this paper we propose a method to estimate arm joint angles from surface
Electromyography (s-EMG) signals using Artificial Neural Network (ANN). The neural network is trained with
EMG data from wrist flexion and extension action as input and joint angle values from the vicon system as
target. The results shown in this paper illustrate the neural network performance in estimating the joint angle
values during offline testing.
Index Terms: Vicon system, Joint angle, Surface EMG, Artificial Neural Network, Virtual reality, Robotics.
A robotic arm is a Programmable mechanical arm which copies the functions of the human arm. They
are widely used in industries. Human robot-controlled interfaces mainly focus on providing rehabilitation to
amputees in order to overcome their amputation or disability leading them to live a normal life. The major
objective of this project is to develop a movable robotic arm controlled by EMG signals from the muscles of the
upper limb. In this system, our main aim is on providing a low 2-dimensional input derived from emg to move the
arm. This project involves creating a prosthesis system that allows signals recorded directly from the human body.
The arm is mainly divided into 2 parts, control part and moving part. Movable part contains the servo motor
which is connected to the Arduino Uno board, and it helps in developing a motion in accordance with the EMG
signals acquired from the body. The control part is the part that is controlled by the operation according to the
movement of the amputee. Mainly the initiation of the movement for the threshold fixed in the coding. The major
aim of the project is to provide an affordable and easily operable device that helps even the poor sections of the
amputated society to lead a happier and normal life by mimicking the functions of the human arm in terms of both
the physical, structural as well as functional aspects.
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
EMG Driven IPMC Based Artificial Muscle FingerAbida Zama
The medical, rehabilitation and bio-mimetic technology demands human actuated devices which can support in the daily life activities such as functional assistance or functional substitution of human organs. These devices can be used in the form of prosthetic, skeletal and artificial muscles devices. However, we still have some difficulties in the practical use of these devices. The major challenges to overcome are the acquisition of the user’s intention from his or her bionic signals and to provide with an appropriate control signal for the device. Also, we need to consider the mechanical design issues such as lightweight and small size with flexible behavior etc. For the bionic signals, the electromyography (EMG) signal can be used to control these devices, which reflect the muscles motion, and can be acquired from the body surface. We are familiar with the fact that Ionic polymer metal composite (IPMC) has tremendous potential as an artificial muscle. In place of the supply voltage from external source for actuating an IPMC, EMG signal can be used where EMG electrodes show a reliable approach to extract voltage signal from body. Using this voltage signal via EMG sensor, IPMC can illustrate the bio-mimetic behavior through the movement of human muscles. Therefore, an IPMC is used as an artificial muscle finger for the bio-mimetic/micro robot.
Emg driven ipmc based artificial muscle finger Abida Zama
The medical, rehabilitation and bio-mimetic technology demands human actuated devices which can support in the daily life activities such as functional assistance or functional substitution of human organs. These devices can be used in the form of prosthetic, skeletal and artificial muscles devices. However, we still have some difficulties in the practical use of these devices. The major challenges to overcome are the acquisition of the user’s intention from his or her bionic signals and to provide with an appropriate control signal for the device. Also, we need to consider the mechanical design issues such as lightweight and small size with flexible behavior etc. For the bionic signals, the electromyography (EMG) signal can be used to control these devices, which reflect the muscles motion, and can be acquired from the body surface. We are familiar with the fact that Ionic polymer metal composite (IPMC) has tremendous potential as an artificial muscle. This can be stimulated by supplying a small voltage of 3V and shows evidence of a large bending behavior. In place of the supply voltage from external source for actuating an IPMC, EMG signal can be used where EMG electrodes show a reliable approach to extract voltage signal from body. Using this voltage signal via EMG sensor, IPMC can illustrate the bio-mimetic behavior through the movement of human muscles. Therefore, an IPMC is used as an artificial muscle finger for the bio-mimetic/micro robot.
Park’s Vector Approach to detect an inter turn stator fault in a doubly fed i...cscpconf
An electrical machine failure that is not identified in an initial stage may become catastrophic and it may suffer severe damage. Thus, undetected machine faults may cascade in it failure, which in turn may cause production shutdowns. Such shutdowns are costly in terms of lost production time, maintenance costs, and wasted raw materials. Doubly fed induction generators are used mainly for wind energy conversion in MW power plants. This paper presents a detection of an inter turn stator fault in a doubly fed induction machine whose stator and rotor are supplied by two pulse width modulation (PWM) inverters. The method used in this article to detect this fault, is based on Park’s Vector Approach , using a neural network s.
Estimation of Arm Joint Angles from Surface Electromyography signals using Ar...IOSR Journals
Abstract: Vicon system is implemented in almost every motion analysis systems. It has many applications like
robotics, gaming, virtual reality and animated movies. The motion and orientation plays an important role in
the above mentioned applications. In this paper we propose a method to estimate arm joint angles from surface
Electromyography (s-EMG) signals using Artificial Neural Network (ANN). The neural network is trained with
EMG data from wrist flexion and extension action as input and joint angle values from the vicon system as
target. The results shown in this paper illustrate the neural network performance in estimating the joint angle
values during offline testing.
Index Terms: Vicon system, Joint angle, Surface EMG, Artificial Neural Network, Virtual reality, Robotics.
A robotic arm is a Programmable mechanical arm which copies the functions of the human arm. They
are widely used in industries. Human robot-controlled interfaces mainly focus on providing rehabilitation to
amputees in order to overcome their amputation or disability leading them to live a normal life. The major
objective of this project is to develop a movable robotic arm controlled by EMG signals from the muscles of the
upper limb. In this system, our main aim is on providing a low 2-dimensional input derived from emg to move the
arm. This project involves creating a prosthesis system that allows signals recorded directly from the human body.
The arm is mainly divided into 2 parts, control part and moving part. Movable part contains the servo motor
which is connected to the Arduino Uno board, and it helps in developing a motion in accordance with the EMG
signals acquired from the body. The control part is the part that is controlled by the operation according to the
movement of the amputee. Mainly the initiation of the movement for the threshold fixed in the coding. The major
aim of the project is to provide an affordable and easily operable device that helps even the poor sections of the
amputated society to lead a happier and normal life by mimicking the functions of the human arm in terms of both
the physical, structural as well as functional aspects.
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
EMG Driven IPMC Based Artificial Muscle FingerAbida Zama
The medical, rehabilitation and bio-mimetic technology demands human actuated devices which can support in the daily life activities such as functional assistance or functional substitution of human organs. These devices can be used in the form of prosthetic, skeletal and artificial muscles devices. However, we still have some difficulties in the practical use of these devices. The major challenges to overcome are the acquisition of the user’s intention from his or her bionic signals and to provide with an appropriate control signal for the device. Also, we need to consider the mechanical design issues such as lightweight and small size with flexible behavior etc. For the bionic signals, the electromyography (EMG) signal can be used to control these devices, which reflect the muscles motion, and can be acquired from the body surface. We are familiar with the fact that Ionic polymer metal composite (IPMC) has tremendous potential as an artificial muscle. In place of the supply voltage from external source for actuating an IPMC, EMG signal can be used where EMG electrodes show a reliable approach to extract voltage signal from body. Using this voltage signal via EMG sensor, IPMC can illustrate the bio-mimetic behavior through the movement of human muscles. Therefore, an IPMC is used as an artificial muscle finger for the bio-mimetic/micro robot.
Emg driven ipmc based artificial muscle finger Abida Zama
The medical, rehabilitation and bio-mimetic technology demands human actuated devices which can support in the daily life activities such as functional assistance or functional substitution of human organs. These devices can be used in the form of prosthetic, skeletal and artificial muscles devices. However, we still have some difficulties in the practical use of these devices. The major challenges to overcome are the acquisition of the user’s intention from his or her bionic signals and to provide with an appropriate control signal for the device. Also, we need to consider the mechanical design issues such as lightweight and small size with flexible behavior etc. For the bionic signals, the electromyography (EMG) signal can be used to control these devices, which reflect the muscles motion, and can be acquired from the body surface. We are familiar with the fact that Ionic polymer metal composite (IPMC) has tremendous potential as an artificial muscle. This can be stimulated by supplying a small voltage of 3V and shows evidence of a large bending behavior. In place of the supply voltage from external source for actuating an IPMC, EMG signal can be used where EMG electrodes show a reliable approach to extract voltage signal from body. Using this voltage signal via EMG sensor, IPMC can illustrate the bio-mimetic behavior through the movement of human muscles. Therefore, an IPMC is used as an artificial muscle finger for the bio-mimetic/micro robot.
Park’s Vector Approach to detect an inter turn stator fault in a doubly fed i...cscpconf
An electrical machine failure that is not identified in an initial stage may become catastrophic and it may suffer severe damage. Thus, undetected machine faults may cascade in it failure, which in turn may cause production shutdowns. Such shutdowns are costly in terms of lost production time, maintenance costs, and wasted raw materials. Doubly fed induction generators are used mainly for wind energy conversion in MW power plants. This paper presents a detection of an inter turn stator fault in a doubly fed induction machine whose stator and rotor are supplied by two pulse width modulation (PWM) inverters. The method used in this article to detect this fault, is based on Park’s Vector Approach , using a neural network s.
A novel efficient human computer interface using an electrooculogrameSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
he main idea of the current work is to use a wireless Electroencephalography (EEG) headset as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many features were extracted using different amplitude estimators for the wavelet coefficients. The extracted features were inputted into machine learning algorithms to generate the decision rules required for our application. The suggested real time implementation of the system was tested and very good performance was achieved. This system could be helpful for disabled people as they can control computer applications via the imagination of fists and feet movements in addition to closing eyes for a short period of time
An Experimental Study on a Pedestrian Tracking Deviceoblu.io
The implemented navigational algorithm of an inertial
navigation system (INS), along with the hardware configuration, decides its tracking performance. Besides, operating conditions also influence its tracking performance. The aim of this study is to demonstrate robust performance of a multiple Inertial Measurement Units (IMUs) based foot-mounted INS, The Osmium MIMU22BTP, under varying operating conditions. The device, which performs zero-velocity-update (ZUPT) aided navigation, is subjected to different conditions which could potentially influence gait of its wearer, its hardware configuration etc. The gait-influencing factors chosen for study are shoe type, walking surface, path profile and walking speed. Besides, the tracking performance of the device is also studied for different number of on-board IMUs and the ambient temperature. The tracking performance of MIMU22BTP is reported for all these factors and benchmarked using identified performance metrics. We observe very robust tracking performance of MIMU22BTP. The average relative errors are less than 3 to 4% under all the conditions, with respect to drift, distance and height, indicating a potential for a variety of location based services based on foot mounted inertial sensing and dead reckoning.
Multi Inertial Measurement Units (MIMU) Platforms: Designs & Applicationsoblu.io
There are typically three categories of multi-sensor systems. First, classical sensors system with different types of collocated sensors, e.g. a positioning system making use of a collocated inertial sensor, a pressure sensor and a GPS. Second, sensor joint systems wherein multiple same type of sensors coordinate to predict state of a system, e.g. estimating motion of a robotic or a human arm using multiple sensors attached to different positions, for capturing a versatile motion. The third kind of multi sensors system consists of collocated sensors with the same properties. The redundancy due to multiple sensors, results not only in enhanced noise performance of the system, but also allows the multi sensor system to achieve what single sensor system can not, e.g. a two dimensional array of accelerometers on a rigid circuit board can produce rotational information. On the one hand enhancing capabilities, shrinking size and reducing cost of MEMS sensors favor redundancy, but on the other hand data communication, processing and calibration compensation pose system level challenges.
The talk focused on technical merits of such multi-sensor systems. Talk covered the architecture of massive multi-IMU arrays with up to 288 measurement channels at 1 kHz, the engineering challenges associated with them including the requirements on on-node data processing, their merits and some applications.
Humans have evolved to better survive and have evolved their invention. In today’s age, a
large number of robots are placed in many areas replacing manpower in severe or dangerous
workplaces. Moreover, the most important thing is to take care of this technology for developing
robots progresses. This paper proposes an autonomous moving system which automatically finds its
target from a scene, lock it and approach towards its target and hits through a shooting mechanism.
The main objective is to provide reliable, cost effective and accurate technique to destroy an unusual
threat in the environment using image processing.
The Osmium MIMU4444, with 32 IMUs, is a massive inertial sensory array module with two mirrored 4x4 square IMU arrays. MIMU4444 is an ideal platform for carrying out research in motion sensing by using Sensor Fusion and Array Signal Processing methods. MIMU4444 is an easy to use and highly configurable hardware platform, serves the needs for niche applications, such as gait analysis, 3D motion capture, Structure from Motion (SfM) etc.
Osmium MIMU22BT: A Micro Wireless Multi-IMU (MIMU) Inertial Navigation Moduleoblu.io
The Osmium MIMU22BT is a miniaturized MIMU based wireless inertial navigation module suitable for foot mounted indoor positioning and other applications based on wearable sensors. An on-board Bluetooth module provides a wireless data link. Presence of on-board floating point processing capability, along with four IMUs, makes navigational computation possible inside the module itself, which in turn results in very accurate tracking of wearer.
Bionic Arm is the best revolution idea for amputees across the world. This is as close as we can get to our natural limb. This paper is about the study of the prosthetic arm used for amputees and gives an overview of upper limb evolution based on control technologies. It focused on the mechanical parameters like actuation system and prototyping techniques that are required to meet the design specifications. The drive systems which hold the key for proper functioning are described and their pros and cons are stated. A review of materials for prosthetic applications and role of 3D printing as a manufacturing method is discussed This would further enable to choose a system based on variables like dexterity, patients need, a weight of the system and feasibility. Detailed research of robotic limb generation could help us to develop a prosthetic limb that mimics the salient features of the limb. M. Sreedhar | S. Sai Mani Shekar | K. Aditya Vardhan | S. Vaibhav Krishna ""A Review on Bionic Arm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23221.pdf
Paper URL: https://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/23221/a-review-on-bionic-arm/m-sreedhar
Massive Sensors Array for Precision Sensingoblu.io
More than a billion smartphones being sold annually and growing with CAGR of 16%, the smartphone industry has become a driving force in the development of ultralow-cost inertial sensors. Unfortunately, these ultra low-cost sensors do not yet meet the needs of more demanding applications like inertial navigation and biomedical motion tracking systems. However, by adapting a wisdom of the crowd’s thinking and design arrays consisting of hundreds of sensing elements, one can capitalize on the decreasing cost, size, and power-consumption of the sensors to construct virtual high-performance low-cost inertial sensors. Team at KTH, Sweden and WUSTL, USA share findings and challenges.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
DESIGN AND DEVELOPMENT OF WRIST-TILT BASED PC CURSOR CONTROL USING ACCELEROMETERIJCSEA Journal
Human computer Interfacing apparatus is key part in modern electronics period. Motion recognition can be well introduced in present day computers to play games. In this work simple inertial navigation sensor like accelerometer can be use to get Dynamic or Static acceleration profile of movement to move cursor of mouse or even rotate 3-D object. In this paper a human computer interfaces system is presented, which will be able to act as an enhanced version of one of the most common interfacing system, which is computer mouse. In this research work, an alternative to interact with computer, for those who do not want to use conventional HCI (human computer interface) or not able to use conventional human computer interface and this achieved by using a sensor accelerometer mount on human wrist or anywhere in human body.Accelerometer device use to detect the position in x, y direction caused by movement of device mount on the wrist, as referred from acceleration of gravity (1g=9.8m/s2). Accelerometer is connected with PIC16F877A for analog to digital conversion and PIC16F877A are connected with LCD for displaying the co-ordinates(x, y) in which the accelerometer move and it further connected with ZigBee transmitter which is used to transmit the wireless signal to ZigBee receiver. ZigBee receiver receives the signal from transmitting end and transferred to PC through MAX232 serial communication. In PC an application for cursor control in response to accelerometer movement is developed
Evolution of a shoe-mounted multi-IMU pedestrian dead reckoning PDR sensoroblu.io
Shoe-mounted inertial navigation systems, aka pedestrian dead reckoning or PDR sensors, are being preferred for pedestrian navigation because of the accuracy offered by them. Such shoe sensors are, for example, the obvious choice for real time location systems of first responders. The opensource platform OpenShoe has reported application of multiple IMUs in shoe-mounted PDR sensors to enhance noise performance. In this paper, we present an experimental study of the noise performance and the operating clocks based power consumption of multi-IMU platforms. The noise performances of a multi-IMU system with different combinations of IMUs are studied. It is observed that four-IMU system is best optimized for cost, area and power. Experiments with varying operating clocks frequency are performed on an in-house four-IMU shoe-mounted inertial navigation module (the Oblu module). Based on the outcome, power-optimized operating clock frequencies are obtained. Thus the overall study suggests that by selecting a well-designed operating point, a multi-IMU system can be made cost, size and power efficient without practically affecting its superior positioning performance.
Abstract - Positioning is a fundamental component of human life to make meaningful interpretations of the environment. Without knowledge of position, human beings are like machines and have very limited capabilities to interact with the environment. Even machines in today’s world can be made smarter if positioning information is made available to them. Indoor positioning of pedestrians is the broad area considered in this thesis. A foot mounted pedestrian tracking device has been studied for this purpose. Systems which utilize foot mounted inertial navigation system has been in the literature for more than two decades. However very few real time implementations have been possible. The purpose of this thesis is to benchmark and improve the performance of one such implementation.
Structural analysis of multiplate clutchIOSR Journals
Clutch is mechanism which transfers the rotary motion of one shaft to the other shaft when desired. In automobiles friction clutches are widely used in power transmission applications. To transmit maximum torque in friction clutches selection of the friction material is one of the important task. In this paper, the multi plate clutch is designed by using uniform wear theory. The 3D model of multi plate clutch has been prepared using modeling software Pro/E. The structural analysis is carried out for friction plate by using analysis software Ansys Workbench 14.0. The results for stress, strain, total deformation and for strain energy are obtained. These results are compared for two different friction materials viz. cork and SF001
A novel efficient human computer interface using an electrooculogrameSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
he main idea of the current work is to use a wireless Electroencephalography (EEG) headset as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many features were extracted using different amplitude estimators for the wavelet coefficients. The extracted features were inputted into machine learning algorithms to generate the decision rules required for our application. The suggested real time implementation of the system was tested and very good performance was achieved. This system could be helpful for disabled people as they can control computer applications via the imagination of fists and feet movements in addition to closing eyes for a short period of time
An Experimental Study on a Pedestrian Tracking Deviceoblu.io
The implemented navigational algorithm of an inertial
navigation system (INS), along with the hardware configuration, decides its tracking performance. Besides, operating conditions also influence its tracking performance. The aim of this study is to demonstrate robust performance of a multiple Inertial Measurement Units (IMUs) based foot-mounted INS, The Osmium MIMU22BTP, under varying operating conditions. The device, which performs zero-velocity-update (ZUPT) aided navigation, is subjected to different conditions which could potentially influence gait of its wearer, its hardware configuration etc. The gait-influencing factors chosen for study are shoe type, walking surface, path profile and walking speed. Besides, the tracking performance of the device is also studied for different number of on-board IMUs and the ambient temperature. The tracking performance of MIMU22BTP is reported for all these factors and benchmarked using identified performance metrics. We observe very robust tracking performance of MIMU22BTP. The average relative errors are less than 3 to 4% under all the conditions, with respect to drift, distance and height, indicating a potential for a variety of location based services based on foot mounted inertial sensing and dead reckoning.
Multi Inertial Measurement Units (MIMU) Platforms: Designs & Applicationsoblu.io
There are typically three categories of multi-sensor systems. First, classical sensors system with different types of collocated sensors, e.g. a positioning system making use of a collocated inertial sensor, a pressure sensor and a GPS. Second, sensor joint systems wherein multiple same type of sensors coordinate to predict state of a system, e.g. estimating motion of a robotic or a human arm using multiple sensors attached to different positions, for capturing a versatile motion. The third kind of multi sensors system consists of collocated sensors with the same properties. The redundancy due to multiple sensors, results not only in enhanced noise performance of the system, but also allows the multi sensor system to achieve what single sensor system can not, e.g. a two dimensional array of accelerometers on a rigid circuit board can produce rotational information. On the one hand enhancing capabilities, shrinking size and reducing cost of MEMS sensors favor redundancy, but on the other hand data communication, processing and calibration compensation pose system level challenges.
The talk focused on technical merits of such multi-sensor systems. Talk covered the architecture of massive multi-IMU arrays with up to 288 measurement channels at 1 kHz, the engineering challenges associated with them including the requirements on on-node data processing, their merits and some applications.
Humans have evolved to better survive and have evolved their invention. In today’s age, a
large number of robots are placed in many areas replacing manpower in severe or dangerous
workplaces. Moreover, the most important thing is to take care of this technology for developing
robots progresses. This paper proposes an autonomous moving system which automatically finds its
target from a scene, lock it and approach towards its target and hits through a shooting mechanism.
The main objective is to provide reliable, cost effective and accurate technique to destroy an unusual
threat in the environment using image processing.
The Osmium MIMU4444, with 32 IMUs, is a massive inertial sensory array module with two mirrored 4x4 square IMU arrays. MIMU4444 is an ideal platform for carrying out research in motion sensing by using Sensor Fusion and Array Signal Processing methods. MIMU4444 is an easy to use and highly configurable hardware platform, serves the needs for niche applications, such as gait analysis, 3D motion capture, Structure from Motion (SfM) etc.
Osmium MIMU22BT: A Micro Wireless Multi-IMU (MIMU) Inertial Navigation Moduleoblu.io
The Osmium MIMU22BT is a miniaturized MIMU based wireless inertial navigation module suitable for foot mounted indoor positioning and other applications based on wearable sensors. An on-board Bluetooth module provides a wireless data link. Presence of on-board floating point processing capability, along with four IMUs, makes navigational computation possible inside the module itself, which in turn results in very accurate tracking of wearer.
Bionic Arm is the best revolution idea for amputees across the world. This is as close as we can get to our natural limb. This paper is about the study of the prosthetic arm used for amputees and gives an overview of upper limb evolution based on control technologies. It focused on the mechanical parameters like actuation system and prototyping techniques that are required to meet the design specifications. The drive systems which hold the key for proper functioning are described and their pros and cons are stated. A review of materials for prosthetic applications and role of 3D printing as a manufacturing method is discussed This would further enable to choose a system based on variables like dexterity, patients need, a weight of the system and feasibility. Detailed research of robotic limb generation could help us to develop a prosthetic limb that mimics the salient features of the limb. M. Sreedhar | S. Sai Mani Shekar | K. Aditya Vardhan | S. Vaibhav Krishna ""A Review on Bionic Arm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23221.pdf
Paper URL: https://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/23221/a-review-on-bionic-arm/m-sreedhar
Massive Sensors Array for Precision Sensingoblu.io
More than a billion smartphones being sold annually and growing with CAGR of 16%, the smartphone industry has become a driving force in the development of ultralow-cost inertial sensors. Unfortunately, these ultra low-cost sensors do not yet meet the needs of more demanding applications like inertial navigation and biomedical motion tracking systems. However, by adapting a wisdom of the crowd’s thinking and design arrays consisting of hundreds of sensing elements, one can capitalize on the decreasing cost, size, and power-consumption of the sensors to construct virtual high-performance low-cost inertial sensors. Team at KTH, Sweden and WUSTL, USA share findings and challenges.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
DESIGN AND DEVELOPMENT OF WRIST-TILT BASED PC CURSOR CONTROL USING ACCELEROMETERIJCSEA Journal
Human computer Interfacing apparatus is key part in modern electronics period. Motion recognition can be well introduced in present day computers to play games. In this work simple inertial navigation sensor like accelerometer can be use to get Dynamic or Static acceleration profile of movement to move cursor of mouse or even rotate 3-D object. In this paper a human computer interfaces system is presented, which will be able to act as an enhanced version of one of the most common interfacing system, which is computer mouse. In this research work, an alternative to interact with computer, for those who do not want to use conventional HCI (human computer interface) or not able to use conventional human computer interface and this achieved by using a sensor accelerometer mount on human wrist or anywhere in human body.Accelerometer device use to detect the position in x, y direction caused by movement of device mount on the wrist, as referred from acceleration of gravity (1g=9.8m/s2). Accelerometer is connected with PIC16F877A for analog to digital conversion and PIC16F877A are connected with LCD for displaying the co-ordinates(x, y) in which the accelerometer move and it further connected with ZigBee transmitter which is used to transmit the wireless signal to ZigBee receiver. ZigBee receiver receives the signal from transmitting end and transferred to PC through MAX232 serial communication. In PC an application for cursor control in response to accelerometer movement is developed
Evolution of a shoe-mounted multi-IMU pedestrian dead reckoning PDR sensoroblu.io
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network secrets can launch low-effort jamming attacks that are difficult to detect and counter. In this work, we
address the problem of selective jamming attacks in wireless networks. In these attacks, the hacker is active only
for a short period of time, selectively targeting messages of high importance. We demonstrate the advantages of
selective jamming in terms of network performance degradation and hacker effort by presenting two case
studies; a selective attack on TCP and one on routing. We show that selective jamming attacks can be
forwarded by performing real-time packet classification at the physical layer. To reduce these attacks, we
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Myoelectric prostheses are a viable solution for people with amputations. The chal- lenge in implementing a usable myoelectric prosthesis lies in accurately recognizing different hand gestures. The current myoelectric devices usually implement very few hand gestures. In order to approximate a real hand functionality, a myoelectric prosthesis should implement a large number of hand and finger gestures. However, increasing number of gestures can lead to a decrease in recognition accuracy. In this work a Myo armband device is used to recognize fourteen gestures (five build in gestures of Myo armband in addition to nine new gestures). The data in this research is collected from three body-able subjects for a period of 7 seconds per gesture. The proposed method uses a pattern recognition technique based on Multi-Layer Perceptron Neural Network (MLPNN). The results show an average accuracy of 90.5% in recognizing the proposed fourteen gestures.
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Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode grid from different types of subjects, sex, age, and type of muscles have been studied when muscles are under regular tension activity. A goodness of fit measure (R2) is used to evaluate the proposed technique. The interpolated signals are compared with the actual signals; the Goodness of fit measure's value is almost 99%, with a processing time of 100msec. The resulting technique is shown to be of high accuracy and matching of spatial interpolated signals to actual signals compared with IDW and Kriging techniques.
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Electromyography (EMG) signals are used for various applications, especially in smart prostheses. Recognizing various gestures (hand movements) in EMG systems introduces challenges. These challenges include the noise effect on EMG signals and the difficulty in identifying the exact movement from the collected EMG data amongst others. In this paper, three neural network models are trained using an open EMG dataset to classify and recognize seven different gestures based on the collected EMG data. The three implemented models are: a four-layer deep neural network (DNN), an eight-layer DNN, and a five-layer convolutional neural network (CNN). In addition, five optimizers are tested for each model, namely Adam, Adamax, Nadam, Adagrad, and AdaDelta. It has been found that four layers achieve respectable recognition accuracy of 95% in the proposed model.
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Prosthetic hand using Artificial Neural NetworkSreenath S
Real Time Moving Prosthetic.
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Modelling and Control of a Robotic Arm Using Artificial Neural Network
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 15, Issue 2 (Nov. - Dec. 2013), PP 42-49
www.iosrjournals.org
www.iosrjournals.org 42 | Page
Modelling and Control of a Robotic Arm Using Artificial Neural
Network
Satyajit Bhowmick1
, Rajesh Bag1
, SK Masud Hossain1
, Subhajit Ghosh1
,
Shanta Mazumder1
, Sauvik Das Gupta2
1
Department of Electronics and Communication Engineering, WBUT, Kolkata, India
2
School of Electrical and Computer Engineering, Oklahoma State University, OK, USA
Abstract: Often it can be seen that men with a lost arm face severe difficulties doing daily chores. Artificial
Intelligence could be effectively used to provide some respite to those people. Neural networks and their
applications have been an active research topic since recent past in the rehabilitation robotics/machine
learning community, as it can be used to predict posture/gesture which is guided by signals from the human
brain. In this paper, a method is proposed to estimate force from Surface Electromyography (s-EMG) signals
generated by specific hand movements and then design and control a Robotic arm using Artificial Neural
Network (ANN) to replicate human arm. Here the force prediction is a Regression process. A hand model has
been successfully moved using servo motor that has been programmed based on the results obtained from
sample data. The results shown in this paper illustrate how the Robotic arm performs.
Index Terms: Surface EMG, Artificial Neural Network, Robotic arm, Regression.
I. Introduction
This project has been designed on data taken as Surface EMG signals from the human arm. Surface
Electromyography is a non-invasive technique for measuring muscle electrical activity that occurs during mus-
cle contraction and relaxation cycles. EMG signals contain the information about the muscle force which can be
used in human-machine interaction. Force plays an important role in these applications. Rezazadeh et al. [1]
proposed a co-adaptive Human-Machine Interface (HMI) that is developed to control virtual forearm prosthesis
over a long period of operation. This paper has influenced us to make a robotic arm based on the hand EMG
signal. The physical structure of the robotic arm has been modeled such that it can be easily assembled. It has
been simplified such that it has 1 degree of freedom (with capability of rotating 180◦
) while retaining some of
the important motions of the human arm. A five-finger model has been constructed. The working is based on the
simple fact of comparison of performances of EMG signals taken from human arm and the target to be achieved.
This model exhibits the full range of motion required to move the arm with some degree of force. A virtual re-
ality model package has been developed in MATLAB to control the model in such a manner that it is able to
demonstrate the potential of the work. Mobasser et al. [2] used multilayer perceptron ANN for hand force esti-
mation from surface EMG signals for applications in sports activities. Yang et al. [3] demonstrated the use of
ANN, Locally Weighted Projection Regression (LWPR) and SVM to estimate hand grasp force from surface
EMG signals for force control of multi-functional prosthetic hands. Haritha et al. [4] used a method to estimate
the hand force from Surface Electromyography signals using ANN.In this work, we will estimate the predicted
value of force from the surface EMG signals using a feed-forward ANN. The neural network is trained with
both EMG and force data. Then the arm model is moved using servo motor which is programmed based on the
results obtained from sample data.The next section describes the hardware setup of our system. Section III pre-
sents the methodology we proposed for force estimation and operation of the robotic arm. Section IV discusses
the experimental evaluation and results. Section V presents the conclusion of the paper.
II. Hardware Platform
This Module details about the hardware components used in this work. The hardware platform of our
research consists of surface electrode, amplifier, data acquisition board and arm model. Below we will discuss in
more details about the individual components.
A. ELECTRODE
The electrodes are used to collect muscle signals. We are using surface gelled disposable electrodes.
They come in various
2. Modelling And Control Of A Robotic Arm Using Artificial Neural Network
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(a) (b)
Figure 1. (a) Kendall Arbo skin surface electrode and (b) Snap-on electrode cable
Figure 2. Schematic of the EMG extraction along with differential amplifier configuration
shapes and sizes. They are very light. We use the Kendall Arbo [5] skin surface electrodes in this project as the
sensing electrodes.We also have used Snap-On type electrode cable to transmit EMG signals to the amplifier.
B. AMPLIFIER
EMG is a low amplitude signal and raw EMG data from the muscle cannot be read or used for further
processing without amplifying. Hence, an amplifier is connected to the electrodes to get measurable and
process-able EMG data. In our project
Figure 3. Connection diagram of AD620
we have used Instrumentation amplifier AD620.It is a low cost, high accuracy instrumentation amplifier that
requires only one external resistor to set gains of 1 to 1000.The low noise, low input bias current and low power
of the AD620 make it well suited for medical Applications such as ECG and non-invasive blood pressure moni-
tors.
C. DATA ACQUISITION BOARD
The amplified data is acquired by NI USB-6008 [6] data acquisition device and used in this work for
collecting EMG signals. It is an Analog to Digital Converter (ADC) DAQ manufactured by National Instru-
ments. The picture of the device is shown in the section below.
Figure 4. NI USB-6008
3. Modelling And Control Of A Robotic Arm Using Artificial Neural Network
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This device is connected to the PC by a USB cable.
We need EMG and Force data to go further. Forces are collected and pre-saved from a Novint Falcon haptic
device, along with the s-EMG. The Novint Falcon is a haptic device that has a grip or handle that the user can
hold to control it. The grip moves in three dimensions (right-left, forwards-backwards and up-down). However,
only the force in the Z direction is considered to represent the force produced through wrist flexion of the hand.
The force is normalized to match EMG for further analysis. When the grip is moved, the Falcon's software
tracks the movements and sends current to the motor which in turn creates a force that a user can feel. These
EMG and Force data samples are utilized.
D. ARM MODEL
The robotic arm consists of a wooden base connected to another wooden part which is made to resem-
ble the human part from the shoulder to the elbow. The servo motor is placed at the elbow which is clamped to a
hand made up of light metal sheet, covered with crape bandage and a five finger model of cardboard.
Figure 5. Robotic arm model
A servomotor is a rotary actuator that allows for precise control of angular position. It consists of a motor cou-
pled to a sensor for position feedback, through a reduction gear box. We have used servomotor VS1 [7]. The
entire model is made to mimic a human arm. The logic control of the arm is entirely done using an Arduino
board placed at the arm’s base. Arduino [8] is a single-board microcontroller designed to make the process of
(a)
(b) (c)
Figure 6. (a)Arduino decimilia and (b) Servo motor VS1 and (c) The standard USB A plug (left) and B plug
(right)
using electronics in multidisciplinary projects more accessible. The arm is made as light as possible because the
servomotor can take maximum of 200 grams of weight, so the lighter the arm, the better it is. USB connector is
used to connect the arduino to the computer & Serial Communication protocol is used to establish communica-
tion between MATLAB & arduino microcontroller. The basic USB connection is of two male jacks viz. type A
and type B.
Servo Motor
Arduino
USB Connector
Arm
4. Modelling And Control Of A Robotic Arm Using Artificial Neural Network
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Figure 7. Overall method for Movement of the Robotic Arm
III. Methodology
After Data Acquisition, the whole procedure till operating and moving the robotic arm can be broadly
classified into three steps, namely pre-processing, training the ANN and compilation. Before describing the me-
thods, it should be mentioned that we are using data samples, previously collected from falcon. The samples are
not discrete, they are in matrix form. We took five data samples of EMG vector and corresponding 5 samples of
equivalent force vectors. The co-relation is done between sample EMG of matrix (1x3311) and sample Force of
matrix (1x3311).
A. PRE-PROCESSING
i. Feature Extraction
This step extracts important information from EMG signals. Root Mean Square (RMS) is used as the
feature extraction technique in this work & eliminates the dual-axis nature of the acquired EMG data. The ma-
thematical equation for RMS is given as follows:
1
Wher1e,
N = Total number of values
x = Individual data points
A MATLAB code is written in order to get the dynamic RMS values of the EMG signals through an overlap-
ping continuous
window update method for the whole data length of the EMG. The value of the window (N) is fixed to 90 sam-
ples in this case
Figure 9. RMS plot of EMG data
Figure 8. Raw EMG signal
5. Modelling And Control Of A Robotic Arm Using Artificial Neural Network
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ii. Data filtering
Raw EMG data needs to be filtered in order to reduce the noise or improve the Signal-to-Noise Ratio
(SNR). Smoothing filter is applied on the signal to remove the high frequency fluctuations. Here we implement
a Pseudo-Gaussian smoothing filter in MATLAB to achieve the above purpose. The noise is considerably re-
duced and the SNR is improved which in turn facilitates better feature extraction. Seeing the figure we can un-
derstand the importance of this step.
Figure 10. RMS plot of EMG data after filtering
iii. Transformation
Even though, filtered EMG and force values were recorded at the same time and for the same amount
of time, Force transformation or normalization was done by a MATLAB program using the mapstd function in
order to overcome the problem of mismatch in the scale of samples of force values (in Newtons) and EMG val-
ues (in Millivolts). It process matrices by mapping each row’s mean to zero and deviations to one. It is an ap-
proach for scaling network inputs and targets so that they will have zero mean and unity standard deviation. The
input and the target vector to the neural network are the feature vector of EMG values and normalized force vec-
tor values. The regression results are improved.
Figure 11. Normalized EMG data on left and normalized force on right
iv. Dynamic Time Warping
Dynamic Time Warping (DTW) is a well-known technique to find an optimal alignment between two
given (time-dependent) sequences. It gives nonlinear time normalization between the two signals. DTW can be
applied to two waveforms which have approximately the same overall amplitude profiles, but are not close to
each other in the time axes. In spite of the actions being the same, it is impossible to repeat the same action with
same intensity & for the same time-span. Hence, in some actions, though the peak values of forces are same, the
time axes do not match. DTW was used to find an optimal alignment with time series and to improve regression
results. Reference templates for DTW are created by averaging all the signals. DTW is used to eliminate the
distortion in these signals.
Figure 12. Force data after DTW
6. Modelling And Control Of A Robotic Arm Using Artificial Neural Network
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B. TRAINING THE ANN
In this work, a two layer feed forward artificial neural network with sigmoid hidden neurons and linear
output neurons are used for force regression from a single channel of EMG signal. The network is trained with
data samples for flexions. The input to the feed forward neural network is the EMG feature vector and the force
is the target vector (We have taken data samples of force). In order to improve the performance of the network
and to prevent high bias and variance, the data is separated into training set, cross-validation set and testing set.
The network is trained with four different sets of inputs (different sets of data obtained from a single channel of
EMG) and targets (corresponding force values).The Mean Squared Error (mse) is used as the performance
measure. The average training error is found to be 3.05355e-05. Average overall regression value from training,
validation and test set is found to be 0.9999 (maximum value of R is 1).
C. COMPILATION in MATLAB GUI
In this automated callback function, we introduce our required code.
Figure 13. Flowchart of basic steps to make a Matlab GUI
We have constructed a MATLAB GUI in order to simplify the workflow through graphical control & avoid
using huge commands repetitively.
Figure 14. The final GUI looks like this
i. Interfacing and MSE calculation
After the serial port is made available for duplex communication between MATLAB & Arduino, we
check the mse. If the mse is lower than a certain threshold value to match the original signal, then the movement
of motor will be clockwise or counter clockwise as programmed. Otherwise it will not rotate. By observation,
we found this threshold mse value to be 0.05. For our test case, we considered the Flexion movement as our test
case. Movement can be in both ways with greater prospects.
ii. Compilation Workflow
The mse obtained from ANN is checked.
If mse is less than 0.05, MATLAB communicates with Arduino with a baud rate of 9600 bits/sec &
sends a go-ahead flag signal.
If Arduino receives the go-ahead flag, it commands the servo to rotate at an angle 60◦
and again return
to its original position.
Thus flexion is replicated.
If mse is greater than 0.05, it implies, the obtained signal is varied too much from the original signal to
be considered as flexion. Thus no action is taken.
IV. Experimental Evaluation
First of all, the muscle site, the flexor carpi ulnaris muscle is identified to be involved in the flexion ac-
tion. The selected hand area is cleaned with NuPrep abrasive skin cream [9] to remove dry and dead cells in
order to increase signal conduction and skin electrode impedance. Ten20 conductive enhancement paste [10] is
applied to the grey portion of the EMG electrode to enhance conduction. The electrode is connected to the am-
plifier through electrode cable. This amplifier through NI USB-6008 is connected to the computer. Using this
7. Modelling And Control Of A Robotic Arm Using Artificial Neural Network
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arrangement we have collected the EMG data samples for different hand movement using DAQ software and
MATLAB. The second part of the experiment is to operate the robotic arm using the flexion EMG samples. In
this part, we connect the arm using USB connector to the computer. We have randomly chosen three samples,
one for each of flexion (bending of arm from elbow), grasp (grasp of palm) and wave (wrist waving) move-
ments. We have got the mse for those samples, using the previously mentioned methods, as follows:
Table 1: Mean square error between actual and predicted force
Figure15. (a) Regression curve for flexion and (b) validation performance curve for flexion (Training curve,
validation curve, test curve, best fitting curve)
As force is correlated with flexion, grasp and wave show no correlation. So, mse for grasp and wave is higher.
We have set the value of the mse, 0.05, as the condition for movement of the arm. If mse is less than 0.05, then
there will be a movement of the arm (from stable state to upwards or downwards direction and then back to the
stable state). When we run the Matlab GUI then we found that the robotic arm moved UP and DOWN only for
the input EMG of flexion as this is the best fitting curve, matches closely with the force and the mse is within
the range. For the rest of the signals the arm does not move as mse does not match with that of the condition.
V. Discussions And Conclusions
This paper presents a method to estimate the muscle force from the surface EMG signals using ANN
and replicate it through a robotic arm. It is a very basic model. Here we have collected data from only one mus-
cle as involving different muscle would make it more complicated. The network trained for one subject may not
be applied for other subjects since the muscle force exerted may not be the same for all the subjects. Hence, the
network has to be re-trained when the subject changes. Moving on to the part of replication through robotic arm,
current generation of robotic arms generally contains six degrees of freedom. But for the sake of simplicity, we
have considered only one. Moreover, only the arm joint of the hand has been replicated, while joints of fingers
Action MSE
Flexion 3.50355e-5
Grasp 0.095
Wave 0.074
(b)
8. Modelling And Control Of A Robotic Arm Using Artificial Neural Network
www.iosrjournals.org 49 | Page
have been ignored. A force feedback system would also increase sensitivity. Though there are some shortcom-
ings, the model could effectively be used in future with some modifications and advancement for purposes like
prosthetic arm, force multiplication using EEG signals etc.
Acknowledgement
This project is supported by WBUT and ESL, Salt Lake, Kolkata.
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