Dynamic control of robotic exoskeletons is paramount to ensuring safe, synergistic assistive action of functional benefit to users. To date, exoskeleton controllers have excelled in rhythmic and quasi-rhythmic tasks, whereas control methods for assisting discrete movements remain limited by their task-specificity. Inspired by neurophysiological dynamic movement primitives (DMPs), we formulated a novel controller that facilitated a variety of lifting movements using a single adaptive DMP (aDMP), for wearable robotic assistance of discrete movements. For a variety of load lifting tasks, we first benchmarked our method's trajectory prediction accuracy against the state-of-the-art DMP using passively recorded exoskeleton sensor data (offline), followed by a functional validation of online aDMP trajectory estimates. Finally, we assessed the functional effects of aDMP-based exoskeletal assistance on joint kinematics and muscular activity during repetitive lifting. The new aDMP method accurately predicted and smoothly synchronized robotic assistance with variable movement trajectories, resulting in reduced muscular activation of the erector spinae muscles (up to 47.6%) while preserving lower-limb joint kinematics and reducing the extension time by 15.5% compared to unassisted conditions. This method holds promise for use in a wide range of wearable robotic applications, including both clinical rehabilitation and user assistance in activities of daily living and/or manual labor.

Adaptive Control Method for Dynamic Synchronization of Wearable Robotic Assistance to Discrete Movements: Validation for Use Case of Lifting Tasks

Lanotte F.;McKinney Z.;Grazi L.;Chen B.;Crea S.;Vitiello N.
2021-01-01

Abstract

Dynamic control of robotic exoskeletons is paramount to ensuring safe, synergistic assistive action of functional benefit to users. To date, exoskeleton controllers have excelled in rhythmic and quasi-rhythmic tasks, whereas control methods for assisting discrete movements remain limited by their task-specificity. Inspired by neurophysiological dynamic movement primitives (DMPs), we formulated a novel controller that facilitated a variety of lifting movements using a single adaptive DMP (aDMP), for wearable robotic assistance of discrete movements. For a variety of load lifting tasks, we first benchmarked our method's trajectory prediction accuracy against the state-of-the-art DMP using passively recorded exoskeleton sensor data (offline), followed by a functional validation of online aDMP trajectory estimates. Finally, we assessed the functional effects of aDMP-based exoskeletal assistance on joint kinematics and muscular activity during repetitive lifting. The new aDMP method accurately predicted and smoothly synchronized robotic assistance with variable movement trajectories, resulting in reduced muscular activation of the erector spinae muscles (up to 47.6%) while preserving lower-limb joint kinematics and reducing the extension time by 15.5% compared to unassisted conditions. This method holds promise for use in a wide range of wearable robotic applications, including both clinical rehabilitation and user assistance in activities of daily living and/or manual labor.
2021
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11382/544491
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 31
social impact