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

QC 20210722
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.

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Reglerteknik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering hsv//eng
Legged locomotion
Electromyography
Stairs
Muscles
Feature extraction
Mechanical sensors
Exoskeletons
Intent recognition
locomotion modes identification
muscle synergies
sensor fusion
robotic exoskeletons

Wang, RuoliKTH,Biomekanik(Swepub:kth)u1nan6x1 (författare)
Gutierrez-Farewik, Elena,1973-KTH,BioMEx,Biomekanik(Swepub:kth)u1tekbf6 (författare)
KTHBiomekanik (creator_code:org_t)

Ingår i:IEEE transactions on neural systems and rehabilitation engineering: Institute of Electrical and Electronics Engineers (IEEE)29, s. 1089-10981534-43201558-0210

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