Deep Learning-Driven Analysis of a Six-Bar Mechanism for Personalized Gait Rehabilitation DOI
Naveed Ahmad Khan, Shahid Hussain, Wayne Spratford

et al.

Journal of Computing and Information Science in Engineering, Journal Year: 2024, Volume and Issue: 25(1)

Published: Oct. 14, 2024

Abstract Recent advances in robotics and artificial intelligence have highlighted the potential for integration of computational enhancing functionality adaptability robotic systems, particularly rehabilitation. Designing exoskeletons lower limb rehabilitation post-stroke patients requires frequent adjustments to accommodate individual differences leg anatomy. This complex engineering challenge necessitates a deep understanding human physiology, robotics, optimization develop adaptive systems also swiftly quantify required implement them each patient. The conventional approaches, which mostly rely on heuristics manual tuning, often struggle achieve optimal results. paper presents novel method that integrates genetic algorithm with learning approach generate gait trajectory ankle joint from six-bar linkage mechanism fixed dimensions. Later, using same approach, inverse kinematics solution this is devised whereby, set link dimensions obtained given an customization. We simulated kinematic behavior within defined mechanical constraints utilized generated data training feedforward neural network long short-term memory models. proposed model, when trained, can produce accurate lengths desired trajectories sagittal plane vice versa, further validates our solution. Moreover, evaluate efficiency models, we conducted extensive error-based, comparative, sensitivity analysis different performance indices. results highlight deep-learning-driven design robots.

Language: Английский

Deep Learning-Driven Analysis of a Six-Bar Mechanism for Personalized Gait Rehabilitation DOI
Naveed Ahmad Khan, Shahid Hussain, Wayne Spratford

et al.

Journal of Computing and Information Science in Engineering, Journal Year: 2024, Volume and Issue: 25(1)

Published: Oct. 14, 2024

Abstract Recent advances in robotics and artificial intelligence have highlighted the potential for integration of computational enhancing functionality adaptability robotic systems, particularly rehabilitation. Designing exoskeletons lower limb rehabilitation post-stroke patients requires frequent adjustments to accommodate individual differences leg anatomy. This complex engineering challenge necessitates a deep understanding human physiology, robotics, optimization develop adaptive systems also swiftly quantify required implement them each patient. The conventional approaches, which mostly rely on heuristics manual tuning, often struggle achieve optimal results. paper presents novel method that integrates genetic algorithm with learning approach generate gait trajectory ankle joint from six-bar linkage mechanism fixed dimensions. Later, using same approach, inverse kinematics solution this is devised whereby, set link dimensions obtained given an customization. We simulated kinematic behavior within defined mechanical constraints utilized generated data training feedforward neural network long short-term memory models. proposed model, when trained, can produce accurate lengths desired trajectories sagittal plane vice versa, further validates our solution. Moreover, evaluate efficiency models, we conducted extensive error-based, comparative, sensitivity analysis different performance indices. results highlight deep-learning-driven design robots.

Language: Английский

Citations

6