Hand gesture recognition using MYO armband

He, Shunzhan; Yang, Chenguang; Wang, Min; Cheng, Long; Hu, Zedong

doi:10.1109/cac.2017.8243637

Cited by 30 publications

(16 citation statements)

References 18 publications

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“…To assess the reliability of the armband sensor data, features such as absolute acceleration, absolute angular velocity, and mean absolute value of EMGsum (sum of EMG values) were calculated from raw data [ 36 ]. These sensor features are widely used in activity/gesture/motion recognition applications [ 33 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. The acceleration along x, y, and z axes were used to compute the absolute acceleration or magnitude of the acceleration vector (Acc) at any given timestamp (t) using Equation (1) [ 31 , 43 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

Bangaru

Wang

Aghazadeh

2020

Sensors

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The workforce shortage is one of the significant problems in the construction industry. To overcome the challenges due to workforce shortage, various researchers have proposed wearable sensor-based systems in the area of construction safety and health. Although sensors provide rich and detailed information, not all sensors can be used for construction applications. This study evaluates the data quality and reliability of forearm electromyography (EMG) and inertial measurement unit (IMU) of armband sensors for construction activity classification. To achieve the proposed objective, the forearm EMG and IMU data collected from eight participants while performing construction activities such as screwing, wrenching, lifting, and carrying on two different days were used to analyze the data quality and reliability for activity recognition through seven different experiments. The results of these experiments show that the armband sensor data quality is comparable to the conventional EMG and IMU sensors with excellent relative and absolute reliability between trials for all the five activities. The activity classification results were highly reliable, with minimal change in classification accuracies for both the days. Moreover, the results conclude that the combined EMG and IMU models classify activities with higher accuracies compared to individual sensor models.

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Section: Methodsmentioning

confidence: 99%

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

Bangaru

Wang

Aghazadeh

2020

Sensors

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“…MYO BRACELET by Thalmic Labs proposes an armband to control interfaces with hand or finger movement detected via the electrical activities of activated muscles [74,81,82].…”

Section: Gesture Control Technologymentioning

confidence: 99%

Human–Machine Interface in Transport Systems: An Industrial Overview for More Extended Rail Applications

Enjalbert

Gandini

Baños³

et al. 2021

Machines

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This paper provides an overview of Human Machine Interface (HMI) design and command systems in commercial or experimental operation across transport modes. It presents and comments on different HMIs from the perspective of vehicle automation equipment and simulators of different application domains. Considering the fields of cognition and automation, this investigation highlights human factors and the experiences of different industries according to industrial and literature reviews. Moreover, to better focus the objectives and extend the investigated industrial panorama, the analysis covers the most effective simulators in operation across various transport modes for the training of operators as well as research in the fields of safety and ergonomics. Special focus is given to new technologies that are potentially applicable in future train cabins, e.g., visual displays and haptic-shared controls. Finally, a synthesis of human factors and their limits regarding support for monitoring or driving assistance is proposed.

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“…The accuracy of gesture recognition is 93%. However, by increasing the number of custom gestures affects the accuracy of the recognition process [3]. There are several devices for monitoring sEMG (or just EMG) signals, and one of them is the MYO armband available in the LIRKIS laboratory.…”

Section: Related Workmentioning

confidence: 99%

Gesture Interaction in the Lirkis Cave Based on Emg and Kinect Sensor

Pastornicky¹,

Chovanec²

2020

AEI

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The article deals with the possibilities of interaction in the virtual environment (VE) of the CAVE LIRKIS system. Commonly available peripherals are keyboard, mouse or gamepad. However, these do not provide a sufficient level of immersion, such as hand gestures. The MYO armband has proven to be one of the potential devices for improving interaction. Therefore, a system for editing an object transformation in VE was created using hand gestures and movements. Enhancing the natural user interface (NUI) of the system with additional interaction capabilities has proven the device to be suitable also for further development.

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Hand gesture recognition using MYO armband

Cited by 30 publications

References 18 publications

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

Human–Machine Interface in Transport Systems: An Industrial Overview for More Extended Rail Applications

Gesture Interaction in the Lirkis Cave Based on Emg and Kinect Sensor

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