Torque required at the knee joint of a robotic assistive device for its thigh to follow the parabolic trajectory generated by its hip joint during sit-to-stand posture

Akhil, V.M.; Varghese, Jobin; Rajendrakumar, P. K.; Sivanandan, K. S.

doi:10.1109/icieeimt.2017.8116815

Cited by 2 publications

(2 citation statements)

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“…e assistive torque generated by a motor mounted on the lower limb exoskeleton included supporting torque, gravity compensation torque, and correction torque [9]. An assistive torque device based on human motion prediction was introduced in [10], and this used a predictive artificial neural network (pANN). e human walking gait cycle and the CoP information were used as input signals to the system in order to generate assistive torque based on the user's intention to move.…”

Section: Introductionmentioning

confidence: 99%

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Use of the Human Walking Gait Cycle for Assistive Torque Generation for the Hip Joint Exoskeleton

Analia

Hong

Mangkey

et al. 2021

Journal of Robotics

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The development of an assistive robot to assist human beings in walking normally is a difficult task. One of the main challenges lies in understanding the intention to walk, as an initial phase before walking commences. In this work, we classify the human gait cycle based on data from an inertial moment unit sensor and information on the angle of the hip joint and use the results as initial signals to produce a suitable assistive torque for a lower limb exoskeleton. A neural network module is used as a prediction module to identify the intention to walk based on the gait cycle. A decision tree method is implemented in our system to generate the assistive torque, and a prediction of the human gait cycle is used as a reference signal. Real-time experiments are carried out to verify the performance of the proposed method, which can differentiate between various types of walking. The results show that the proposed method is able to predict the intention to walk as an initial phase and is also able to provide an assistive torque based on the information predicted for this phase.

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Section: Introductionmentioning

confidence: 99%

“…To provide assistive torque for a lower limb exoskeleton, motion analysis software called Kinovea can be used to measure the parameters of motion [10]. is measures the sitting-to-stance and the back-to-sit phases of the motion parameters.…”

Section: Introductionmentioning

confidence: 99%