[go: up one dir, main page]

Sacchi et al., 2021 - Google Patents

Deep reinforcement learning of robotic prosthesis for gait symmetry in trans-femoral amputated patients

Sacchi et al., 2021

View PDF
Document ID
9414937555698142673
Author
Sacchi N
Incremona G
Ferrara A
Publication year
Publication venue
2021 29th Mediterranean Conference on Control and Automation (MED)

External Links

Snippet

This work proposes a novel control methodology to achieve gait symmetry in trans-femoral amputated patients with prostheses. The proposed approach allows to overcome the limits of classical model-based control strategies by introducing a Deep Reinforcement Learning …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, E.G. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/68—Operating or control means
    • A61F2/70—Operating or control means electrical
    • A61F2002/704—Operating or control means electrical computer-controlled, e.g. robotic control
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, E.G. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/54—Artificial arms or hands or parts thereof
    • A61F2/58—Elbows; Wrists; Other joints; Hands

Similar Documents

Publication Publication Date Title
Li et al. Toward expedited impedance tuning of a robotic prosthesis for personalized gait assistance by reinforcement learning control
Wen et al. A new powered lower limb prosthesis control framework based on adaptive dynamic programming
Aghasadeghi et al. Learning impedance controller parameters for lower-limb prostheses
Huang et al. Optimisation of reference gait trajectory of a lower limb exoskeleton
Rose et al. End-to-end deep reinforcement learning for exoskeleton control
Castillo et al. Reinforcement learning meets hybrid zero dynamics: A case study for rabbit
Sacchi et al. Deep reinforcement learning of robotic prosthesis for gait symmetry in trans-femoral amputated patients
Rai et al. Mode-free control of prosthetic lower limbs
Singh et al. Learning bipedal walking on planned footsteps for humanoid robots
Zhao et al. Quadratic programming and impedance control for transfemoral prosthesis
Liu et al. A new robotic knee impedance control parameter optimization method facilitated by inverse reinforcement learning
Wu et al. Human-robotic prosthesis as collaborating agents for symmetrical walking
Huang et al. Adaptive Gait Planning with Dynamic Movement Primitives for Walking Assistance Lower Exoskeleton in Uphill Slopes.
Horn et al. Nonholonomic virtual constraints for control of powered prostheses across walking speeds
Clark et al. Learning ergonomic control in human–robot symbiotic walking
Ma et al. Robotic leg prosthesis: A survey from dynamic model to adaptive control for gait coordination
Zhao et al. A hybrid systems and optimization-based control approach to realizing multi-contact locomotion on transfemoral prostheses
Rose et al. Designing a Biped Robot's Gait using Reinforcement Learning's-Actor Critic Method
Marjaninejad et al. Model-free control of movement in a tendon-driven limb via a modified genetic algorithm
Kalani et al. Application of DQN learning for delayed output feedback control of a gait-assist hip exoskeleton
Rai et al. Continuous and unified modeling of joint kinematics for multiple activities
Clark et al. Learning predictive models for ergonomic control of prosthetic devices
Eslamy et al. Motion planning for active prosthetic knees
Apostolopoulos et al. Energy efficient and robust balancing with motion primitive switching
Zhang et al. Flat-Upstairs Gait Switching of Lower Limb Prosthesis via Gaussian Process and Improved Kalman Filter