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- W4313445025 abstract "Monitoring workers’ status is crucial to prevent work-related musculoskeletal disorders and to enable a safe human-robot interaction. This is typically achieved relying on muscle activation recordings, commonly performed via wearable electromyographic EMG sensors. However, to properly acquire whole-body muscular status, a large number of sensors is needed. This represents a limitation for a real deployment of wearable acquisition systems, due to cost and wearability constraints. To overcome this problem, we propose a solution to provide a reliable muscles estimation from a limited number of EMG recordings. Our method exploits the covariation patterns between muscles activation to complement the recordings coming from a reduced set of optimally placed sensors, minimizing the estimation uncertainty. Using a dataset of EMG data recorded from 10 subjects, we demonstrate that it is possible to reconstruct the temporal evolution of 10 whole-body muscles with a maximum normalized estimation error of 13%, using only 7 EMG sensors." @default.
- W4313445025 created "2023-01-06" @default.
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- W4313445025 date "2023-01-01" @default.
- W4313445025 modified "2023-10-18" @default.
- W4313445025 title "Estimation of Whole-Body Muscular Activation from an Optimal Set of Scarce Electromyographic Recordings" @default.
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- W4313445025 doi "https://doi.org/10.1007/978-3-031-22731-8_9" @default.
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