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Session: Wearable
Session starts: Friday 27 January, 11:30
Presentation starts: 12:30
Room: Room 531

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Donatella Simonetti (University of Twente)
Bart Koopman (University of Twente)
Massimo Sartori (University of Twente)

Abstract:
Introduction: Clinical decision-making requires above all rapidity. Currently, motor deficit evaluation is based on a simplistic and subjective assessment, i.e. a simple 10m walk. It fulfills the main requirement, but it is not accurate, and it is just based on the evaluator's knowledge and experience. Greater accuracy is achieved in biomechanical laboratories where advanced technology together with neuro-musculoskeletal modeling allows to quantify the subject impairment. However, this is made at the expense of rapidity. Our work is aimed to balance clinical rapidity and biomechanical accuracy. We propose to use advanced signal processing techniques and real-time neuro-musculoskeletal modeling integrated into a smart wearable garment. A simple leg sock instrumented with a large-scale multi-electromyography (EMG, 64 channels) grid and inertial sensors (IMUs) allowing to get over the lengthy setup and to prevent human error in the manual electrodes’ placement. The smart clothing together with advanced signal processing techniques provides muscle activation and muscle-tendon unit (MTU) kinematics necessary to finally model the subject-specific musculoskeletal properties. Methods: 8 healthy subjects were equipped with 33 reflective markers and a leg flexible garment instrumented with 64 equally distributed EMG monopolar electrodes. The 64-electrode space is reduced in 5 muscle-specific clusters by applying iteratively the non-negative matrix factorization (NNMF) [1] during slow locomotion at 1km/h. Afterward, 5 average muscle activations were extracted during locomotion at different speeds 1, 3, and 5 km/h, and used as input to an offline EMG-driven musculoskeletal model to estimate ankle torque. Results: The NNMF-based approach was able to locate the muscles and extract averaged activations during each locomotion speed that resembled with good accuracy the activation recorded with bipolar EMG. Afterward, the musculoskeletal model driven by the automatically extracted muscle-specific activation reproduced experimental ankle torques during gait at different speeds. Conclusions: The combination of a soft sensorized garment and the automatic procedure for the extraction of muscle activations added to the framework for neuromuscular modeling has a good potential to become a resource for fast and more accurate clinical decision-making. Acknowledgment This work was founded by EFRO Op Oost GUTs (20913301). The garment is developed in collaboration with TMSi and Bard.zo.