Plug-and-Play Gesture Control Using Muscle and Motion Sensors

As the capacity for machines to extend human capabilities continues to grow, the communication channels used must also expand. Allowing machines to interpret nonverbal commands such as gestures can help make interactions more similar to interactions with another person. Yet to be pervasive and effec...

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Vydané v:2020 15th ACM/IEEE International Conference on Human-Robot Interaction (HRI) s. 439 - 448
Hlavní autori: DelPreto, Joseph, Rus, Daniela
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: New York, NY, USA ACM 09.03.2020
Edícia:ACM Conferences
Predmet:
Computer systems organization > Embedded and cyber-physical systems > Robotics
Computing methodologies > Machine learning
EMG
Gestures
Human computer interaction
Human-centered computing > Human computer interaction (HCI)
Human-centered computing > Human computer interaction (HCI) > Interaction techniques > Gestural input
Human-robot interaction
IMU
Machine Learning
Muscles
Neural networks
Pipelines
Plug-and-Play
Robotics
Training data
Vocabulary
imu
human-robot interaction
plug-and-play
robotics
teleoperation
wearable sensors
machine learning
emg
gestures
ISBN:1450367461, 9781450367462
ISSN:2167-2148
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Abstract As the capacity for machines to extend human capabilities continues to grow, the communication channels used must also expand. Allowing machines to interpret nonverbal commands such as gestures can help make interactions more similar to interactions with another person. Yet to be pervasive and effective in realistic scenarios, such interfaces should not require significant sensing infrastructure or per-user setup time. The presented work takes a step towards these goals by using wearable muscle and motion sensors to detect gestures without dedicated calibration or training procedures. An algorithm is presented for clustering unlabeled streaming data in real time, and it is applied to adaptively thresholding muscle and motion signals acquired via electromyography (EMG) and an inertial measurement unit (IMU). This enables plug-and-play online detection of arm stiffening, fist clenching, rotation gestures, and forearm activation. It also augments a neural network pipeline, trained only on strategically chosen training data from previous users, to detect left, right, up, and down gestures. Together, these pipelines offer a plug-and-play gesture vocabulary suitable for remotely controlling a robot. Experiments with 6 subjects evaluate classifier performance and interface efficacy. Classifiers correctly identified 97.6% of 1,200 cued gestures, and a drone correctly responded to 81.6% of 1,535 unstructured gestures as subjects remotely controlled it through target hoops during 119 minutes of total flight time.
AbstractList As the capacity for machines to extend human capabilities continues to grow, the communication channels used must also expand. Allowing machines to interpret nonverbal commands such as gestures can help make interactions more similar to interactions with another person. Yet to be pervasive and effective in realistic scenarios, such interfaces should not require significant sensing infrastructure or per-user setup time. The presented work takes a step towards these goals by using wearable muscle and motion sensors to detect gestures without dedicated calibration or training procedures. An algorithm is presented for clustering unlabeled streaming data in real time, and it is applied to adaptively thresholding muscle and motion signals acquired via electromyography (EMG) and an inertial measurement unit (IMU). This enables plug-and-play online detection of arm stiffening, fist clenching, rotation gestures, and forearm activation. It also augments a neural network pipeline, trained only on strategically chosen training data from previous users, to detect left, right, up, and down gestures. Together, these pipelines offer a plug-and-play gesture vocabulary suitable for remotely controlling a robot. Experiments with 6 subjects evaluate classifier performance and interface efficacy. Classifiers correctly identified 97.6% of 1,200 cued gestures, and a drone correctly responded to 81.6% of 1,535 unstructured gestures as subjects remotely controlled it through target hoops during 119 minutes of total flight time. CCS CONCEPTS * Human-centered computing → Human computer interaction (HCI); Gestural input; * Computer systems organization →Robotics; * Computing methodologies→Machine learning. ACM Reference Format: Joseph DelPreto and Daniela Rus. 2020. Plug-and-Play Gesture Control Using Muscle and Motion Sensors. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI '20), March 23-26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3319502.3374823
As the capacity for machines to extend human capabilities continues to grow, the communication channels used must also expand. Allowing machines to interpret nonverbal commands such as gestures can help make interactions more similar to interactions with another person. Yet to be pervasive and effective in realistic scenarios, such interfaces should not require significant sensing infrastructure or per-user setup time. The presented work takes a step towards these goals by using wearable muscle and motion sensors to detect gestures without dedicated calibration or training procedures. An algorithm is presented for clustering unlabeled streaming data in real time, and it is applied to adaptively thresholding muscle and motion signals acquired via electromyography (EMG) and an inertial measurement unit (IMU). This enables plug-and-play online detection of arm stiffening, fist clenching, rotation gestures, and forearm activation. It also augments a neural network pipeline, trained only on strategically chosen training data from previous users, to detect left, right, up, and down gestures. Together, these pipelines offer a plug-and-play gesture vocabulary suitable for remotely controlling a robot. Experiments with 6 subjects evaluate classifier performance and interface efficacy. Classifiers correctly identified 97.6% of 1,200 cued gestures, and a drone correctly responded to 81.6% of 1,535 unstructured gestures as subjects remotely controlled it through target hoops during 119 minutes of total flight time.
Author DelPreto, Joseph
Rus, Daniela
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  email: rus@csail.mit.edu
  organization: Massachusetts Institute of Technology, Cambridge, MA, USA
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Keywords imu
human-robot interaction
plug-and-play
robotics
teleoperation
wearable sensors
machine learning
emg
gestures
Language English
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Snippet As the capacity for machines to extend human capabilities continues to grow, the communication channels used must also expand. Allowing machines to interpret...
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StartPage 439
SubjectTerms Computer systems organization -- Embedded and cyber-physical systems -- Robotics
Computing methodologies -- Machine learning
EMG
Gestures
Human computer interaction
Human-centered computing -- Human computer interaction (HCI)
Human-centered computing -- Human computer interaction (HCI) -- Interaction techniques -- Gestural input
Human-robot interaction
IMU
Machine Learning
Muscles
Neural networks
Pipelines
Plug-and-Play
Robotics
Training data
Vocabulary
Title Plug-and-Play Gesture Control Using Muscle and Motion Sensors
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