Recognition of human arm gestures using Myo armband for the game of hand cricket

Krishnan, Karthik Sivarama; Saha, Akash; Ramachandran, Srinath; Kumar, Shitij

doi:10.1109/iris.2017.8250154

Cited by 22 publications

(10 citation statements)

References 7 publications

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“…Este bracelete possui 8 eletrodos capazes de realizar uma eletromiografia de superfície em antebrac ¸os e, através de técnicas matemáticas, pode detectar alguns gestos padrão executados por uma pessoa. Possui também uma Unidade de Medic ¸ão de Inércia (IMU -Inertial Measurement Unit), que mede os 3 ângulos de Euler [Krishnan et al 2018].…”

Section: Bracelete De Eletromiografiaunclassified

Interface de Controle por Métodos de Autonomia Adaptável Deslizante para Robôs de Inspeção

Palar¹

2021

Anais Estendidos Do XIII Simpósio Brasileiro De Robótica E XVIII Simpósio Latino Americano De Robótica (SBR/LARS Estendido 2021

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Este trabalho realiza uma implementação de uma interface humano-robô para um robô escalador de inspeção de cordões de solda em tanques de armazenamento na indústria petroquímica. Para realizar esta interface, primeiramente foi apresentada uma pesquisa do estado da arte em Interação Humano-Robô, Autonomia, Autonomia Variável e Modos de Autonomia em sistemas robóticos e sistemas autônomos em geral. Após isto, um sistema controlador de rádio frequência transmissor-receptor comumente utilizado para comandar gruas, guindastes e pontes rolantes foi especificado e adaptado para controlar o robô. um driver foi desenvolvido para tornar este controlador compatível com o sistema robótico ROS (Robot Operating System), utilizado neste trabalho. Para auxiliar no controle e adquirir mais dados de entrada do operador, foi adicionado um bracelete eletromiográfico comercial chamado Myo. Este bracelete é utilizado no antebraço e é capaz de detectar gestos do operador e ângulos de rotação do braço. As informações do controle industrial e do bracelete são utilizadas para comandar o robô através de um controlador Fuzzy. Este controlador atua na variação da autonomia durante a operação do robô, utilizando como entrada dados da velocidade angular aplicada no controle industrial, leitura de eletromiografia, posição do cordão de solda no tanque de armazenamento e os ângulos de rotação executados pelo antebraço do operador, gerando um sistema que é capaz de reconhecer a habilidade e corrigir erros do operador em tempo de operação. A saída do controlador Fuzzy é o modo de autonomia a ser aplicado no robô. Os modos aplicados foram: Manual, onde o operador controla a velocidade angular e linear do robô; Compartilhado, onde as velocidades angular e linear do robô são divididos entre o operador e o sistema autônomo do robô; Supervisório, onde o robô controla a velocidade angular, mantendo-se sobre o cordão de solda, e o operador controla a velocidade linear; e Autônomo, onde apenas o ponto final é definido pelo operador e o robô controla as velocidades linear e angular. Estes modos além do modo de Autonomia Variável foram analisados através de experimentos com o robô em ambiente simulado, demonstrando a utilidade de cada um destes modos em situações diversas.

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Section: Bracelete De Eletromiografiaunclassified

Interface de Controle por Métodos de Autonomia Adaptável Deslizante para Robôs de Inspeção

Palar¹

2021

Anais Estendidos Do XIII Simpósio Brasileiro De Robótica E XVIII Simpósio Latino Americano De Robótica (SBR/LARS Estendido 2021

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“…This armband holds eight electrodes that can perform surface electromyography in the arms and, through mathematical analysis, detect some standard hand signals or gestures made by a person, such as the rest position, closed fist, hand palm outside or inside, separated fingers, and thumb to little finger. This armband also contains an inertial measurement unit (IMU) that can measure the three Euler angles [ 35 ].…”

Section: Implementation Of the Human-robot Interactionmentioning

confidence: 99%

“…In addition, research studies have used the the raw data of the electromyographic sensors to detect other gestures apart from the standard ones. In the work of Krishnan et al [ 35 ], five gestures of the hand cricket game were detected using machine learning techniques, such as the support vector machine. In the work of Luh et al [ 42 ], 16 finger gestures were categorized with the aid of artificial neural networks.…”

Section: Implementation Of the Human-robot Interactionmentioning

confidence: 99%

Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods

Palar

Terres

Oliveira

2020

Sensors

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This work discusses a novel human–robot interface for a climbing robot for inspecting weld beads in storage tanks in the petrochemical industry. The approach aims to adapt robot autonomy in terms of the operator’s experience, where a remote industrial joystick works in conjunction with an electromyographic armband as inputs. This armband is worn on the forearm and can detect gestures from the operator and rotation angles from the arm. Information from the industrial joystick and the armband are used to control the robot via a Fuzzy controller. The controller works with sliding autonomy (using as inputs data from the angular velocity of the industrial controller, electromyography reading, weld bead position in the storage tank, and rotation angles executed by the operator’s arm) to generate a system capable of recognition of the operator’s skill and correction of mistakes from the operator in operating time. The output from the Fuzzy controller is the level of autonomy to be used by the robot. The levels implemented are Manual (operator controls the angular and linear velocities of the robot); Shared (speeds are shared between the operator and the autonomous system); Supervisory (robot controls the angular velocity to stay in the weld bead, and the operator controls the linear velocity); Autonomous (the operator defines endpoint and the robot controls both linear and angular velocities). These autonomy levels, along with the proposed sliding autonomy, are then analyzed through robot experiments in a simulated environment, showing each of these modes’ purposes. The proposed approach is evaluated in virtual industrial scenarios through real distinct operators.

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“…Recently, a technique accurately detecting human skin is very valuably used in diverse ranges of computer vision-related application fields such as face recognition and tacking, facial expression recognition, gesture analysis, adult image detection, healthcare, medical image analysis and content-based image retrieval [1][2][3][4]. The reason for this is because, in such application fields, a search space for detecting a target of interest such as face, hand and particular body part can be considerably reduced through skin region detection.…”

Section: Introductionmentioning

confidence: 99%

A Method for Obtaining Human Skin Color Pixels Based on Surrounding Factors

Jang¹,

Oh²

2019

IJAST

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Accurate skin color detection methods are very valuably used in diverse fields such as face recognition and tracking, facial expression recognition, adult image detection and healthcare. In this paper, the performances of diverse skin color detection algorithms were comparatively evaluated as the distance to the subject was changed and the color of the subject background was changed in the environment where normal light and indoor lighting were added. The test was conducted by selecting 2 males and 1 female having different skin tones as the subjects and dividing the background colors into white, black, orange, pink and yellow. The skin color extraction algorithms used for performance evaluation were Peer algorithm, NNYUV algorithm, NNHSV algorithm, LutYUV algorithm, and Kismet algorithm. The test was conducted by limiting the distance between camera and subject to 60cm to 120cm. As a result of conducting the performance measurement test, the performances of the algorithms showed differences according to the changes made to the subject background. In general, NNHSV and NNYUV algorithms using neural network and LutYUY algorithm showed stable results. As far as the remaining algorithms were concerned, their skin color detection rates were significantly influenced by the changes made to the background. This paper is expected to be effectively utilized in developing an adaptive and highly accurate environment-based skin color extraction algorithm suitable for actual environments where dynamic changes are made to the subject's surrounding environments.

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Recognition of human arm gestures using Myo armband for the game of hand cricket

Cited by 22 publications

References 7 publications

Interface de Controle por Métodos de Autonomia Adaptável Deslizante para Robôs de Inspeção

Interface de Controle por Métodos de Autonomia Adaptável Deslizante para Robôs de Inspeção

Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods

A Method for Obtaining Human Skin Color Pixels Based on Surrounding Factors

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