Recognizing Hand and Finger Gestures with IMU based Motion and EMG based Muscle Activity Sensing

Georgi, Marcus; Amma, Christoph; Schultz, Tanja

doi:10.5220/0005276900990108

Cited by 115 publications

(57 citation statements)

References 17 publications

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“…Typically, HD-sEMG employs a large two-dimensional (2D) array of closely spaced electrodes with small size. The total number of electrodes that has been proposed for HD-sEMG is in the range of 32 [12] to over 350 [42], while the maximum number of electrodes for typical EMG armbands is 16 (Tables 1 and 2). The existing shared HD-sEMG data sets, which use electrode arrays of 32, 128, and 192, are listed in Table 3.…”

Section: High-density Surface Emgmentioning

confidence: 99%

“…Several factors have contributed to the recent expansion of EMG data resources such that big data approaches are beginning to be viable. First, EMG data sets collected as part of individual research studies are now being made available online instead of residing solely on hard drives within the laboratories of individual researchers (e.g., [10][11][12]). Secondly, as in other research communities, the availability of benchmark EMG databases has been critical to the growth of the field [13].…”

Section: Introductionmentioning

confidence: 99%

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EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

191

105

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Abstract:The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle "big data". Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on "deep learning". Finally, directions for future research in EMG pattern recognition are outlined and discussed.

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Section: High-density Surface Emgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

191

105

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“…Gesture classification was performed by two artificial neural net classifiers obtaining a 95% accuracy. Later systems such as [24] have displayed similar gains in accuracy.…”

Section: Related Workmentioning

confidence: 82%

Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove

et al. 2018

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This article focuses on the use of data gloves for human-computer interaction concepts, where external sensors cannot always fully observe the user's hand. A good concept hereby allows to intuitively switch the interaction context on demand by using different hand gestures. The recognition of various, possibly complex hand gestures, however, introduces unintentional overhead to the system. Consequently, we present a data glove prototype comprising a glove-embedded gesture classifier utilizing data from Inertial Measurement Units (IMUs) in the fingertips. In an extensive set of experiments with 57 participants, our system was tested with 22 hand gestures, all taken from the French Sign Language (LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, using complementary filter with a gyroscope-to-accelerometer ratio of 93%. Our approach has also been compared to the local fusion algorithm on an IMU motion sensor, showing faster settling times and less delays after gesture changes. Real-time performance of the recognition is shown to occur within 63 milliseconds, allowing fluent use of the gestures via Bluetooth-connected systems.

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“…Gesture recognition has applications in a wide range of areas, such as human-computer interaction, sign language recognition, gaming, household device control, and robot control [6]. Most approaches in the field of gesture recognition are based on vision, IMU, and Electromyography (EMG) signals [7], [8], [9]. Furthermore, depending on the gesture types, gesture recognition techniques can be divided into static and dynamic gesture recognition.…”

Section: Related Workmentioning

confidence: 99%

Mobile manipulator control through gesture recognition using IMUs and Online Lazy Neighborhood Graph search

et al. 2019

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Gesture-based control potentially eliminates the need for wearisome physical controls and facilitates easy interaction between a human and a robot. At the same time, it is intuitive and enables a natural means of control. In this paper, we present and evaluate a framework for gesture recognition using four wearable Inertial Measurement Units (IMUs) to indirectly control a mobile robot. Six gestures involving different hand and arm motions are defined. A novel algorithm based on an Online Lazy Neighborhood Graph (OLNG) search is used to recognise and classify the gestures online. A software framework is developed to control a robotic platform through integrating our gesture recognition algorithm with a Robot Operating System (ROS), which is in turn used to trigger predefined robot behaviours. Experiments show that the framework is able to correctly detect and classify six different gestures in real time with average success rates of 81.61 % and 81.67 %, while keeping the false-positive rate low by designing and using only 126 training samples.

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Recognizing Hand and Finger Gestures with IMU based Motion and EMG based Muscle Activity Sensing

Cited by 115 publications

References 17 publications

EMG Pattern Recognition in the Era of Big Data and Deep Learning

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove

Mobile manipulator control through gesture recognition using IMUs and Online Lazy Neighborhood Graph search

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