A Muscle Synergy-Inspired Method of Detecting Human Movement Intentions Based on Wearable Sensor Fusion

Detecting human movement intentions is fundamental to neural control of robotic exoskeletons, as it is essential for achieving seamless transitions between different locomotion modes. In this study, we enhanced a muscle synergy-inspired method of locomotion mode identification by fusing the electrom...

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Veröffentlicht in:IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING Jg. 29; S. 1089 - 1098
Hauptverfasser: Liu, Yi-Xing, Wang, Ruoli, Gutierrez-Farewik, Elena M.
Format: Journal Article Verlag
Sprache:Englisch
Veröffentlicht: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Acceleration
Accuracy
Angular velocity
Ascent
Classification
Electromyography
Exoskeleton
Exoskeletons
Feature extraction
Finite state machines
Human motion
Inclination angle
Inertial platforms
Inertial sensing devices
Intent recognition
Legged locomotion
Locomotion
locomotion modes identification
Mechanical sensors
Motion perception
Multisensor fusion
muscle synergies
Muscles
Robot control
robotic exoskeletons
sensor fusion
Sensors
Stairs
Stairways
Velocity
Walking
Wearable technology
ISSN:1534-4320, 1558-0210, 1558-0210
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Zusammenfassung:Detecting human movement intentions is fundamental to neural control of robotic exoskeletons, as it is essential for achieving seamless transitions between different locomotion modes. In this study, we enhanced a muscle synergy-inspired method of locomotion mode identification by fusing the electromyography data with two types of data from wearable sensors (inertial measurement units), namely linear acceleration and angular velocity. From the finite state machine perspective, the enhanced method was used to systematically identify 2 static modes, 7 dynamic modes, and 27 transitions among them. In addition to the five broadly studied modes (level ground walking, ramps ascent/descent, stairs ascent/descent), we identified the transition between different walking speeds and modes of ramp walking at different inclination angles. Seven combinations of sensor fusion were conducted, on experimental data from 8 able-bodied adult subjects, and their classification accuracy and prediction time were compared. Prediction based on a fusion of electromyography and gyroscope (angular velocity) data predicted transitions earlier and with higher accuracy. All transitions and modes were identified with a total average classification accuracy of 94.5% with fused sensor data. For nearly all transitions, we were able to predict the next locomotion mode 300-500ms prior to the step into that mode.
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2021.3087135