LSTM-based Network for Human Gait Stability Prediction in an Intelligent Robotic Rollator

Chalvatzaki, Georgia; Koutras, Petros; Hadfield, Jack; Papageorgiou, Xanthi S.; Tzafestas, Costas S.; Maragos, Petros

doi:10.1109/icra.2019.8793899

Cited by 29 publications

(13 citation statements)

References 45 publications

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“…Advanced assistive robotic systems have been proposed with state-of-the-art technologies and novel sensors in recent times. For instance, the assistive robotic system iWalk that consists of an RGB-D RealSense camera for gait tracking, gait stability, and mobility assessment, has been proposed by Chalvatzaki et al [133,134]. iWalk employs an LRF sensor for gait phase estimation and microphones and speakers for speech recognition and voice feedback.…”

Section: Walking Assisting Robotsmentioning

confidence: 99%

A Survey of Robots in Healthcare

et al. 2021

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In recent years, with the current advancements in Robotics and Artificial Intelligence (AI), robots have the potential to support the field of healthcare. Robotic systems are often introduced in the care of the elderly, children, and persons with disabilities, in hospitals, in rehabilitation and walking assistance, and other healthcare situations. In this survey paper, the recent advances in robotic technology applied in the healthcare domain are discussed. The paper provides detailed information about state-of-the-art research in care, hospital, assistive, rehabilitation, and walking assisting robots. The paper also discusses the open challenges healthcare robots face to be integrated into our society.

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Section: Walking Assisting Robotsmentioning

confidence: 99%

A Survey of Robots in Healthcare

et al. 2021

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“…In our proposed approach, trajectory generation is to apply the interlimb synergy extracted from healthy participants by LSTM to generate a trajectory-based on gait data [34,35].…”

Section: Long-short Term Memory Network For Angle Predictionmentioning

confidence: 99%

An LSTM-Based Prediction Method for Lower Limb Intention Perception by Integrative Analysis of Kinect Visual Signal

Guo

Shao

et al. 2020

Journal of Healthcare Engineering

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Recently, computer vision and deep learning technology has been applied in various gait rehabilitation researches. Considering the long short-term memory (LSTM) network has been proved an excellent performance in learn sequence feature representations, we proposed a lower limb joint trajectory prediction method based on LSTM for conducting active rehabilitation on a rehabilitation robotic system. Our approach based on synergy theory exploits that the follow-up lower limb joint trajectory, i.e. limb intention, could be generated by joint angles of the previous swing process of upper limb which were acquired from Kinect platform, an advanced computer vision platform for motion tracking. A customize Kinect-Treadmill data acquisition platform was built for this study. With this platform, data acquisition on ten healthy subjects is processed in four different walking speeds to acquire the joint angles calculated by Kinect visual signals of upper and lower limb swing. Then, the angles of hip and knee in one side which were presented as lower limb intentions are predicted by the fore angles of the elbow and shoulder on the opposite side via a trained LSTM model. The results indicate that the trained LSTM model has a better estimation of predicting the lower limb intentions, and the feasibility of Kinect visual signals has been validated as well.

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“…The cyclic nature of the human gait has previously precluded the use of such networks (Martinez et al, 2017;Ferrari et al, 2019). Nevertheless, a novel LSTM-based framework has been proposed for predicting the gait stability of elderly users of an intelligent robotic rollator (Chalvatzaki et al, 2018), fusing multimodal RGB-D and laser rangefinder data from non-wearable sensors (Chalvatzaki et al, 2018). An LSTM network has also been used to model gait synchronization of legs using a basic off-the-shelf IMU configuration with six acceleration and rotation parameters (Romero-Hernandez et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Gait Neural Network for Human-Exoskeleton Interaction

et al. 2020

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Robotic exoskeletons are developed with the aim of enhancing convenience and physical possibilities in daily life. However, at present, these devices lack sufficient synchronization with human movements. To optimize human-exoskeleton interaction, this article proposes a gait recognition and prediction model, called the gait neural network (GNN), which is based on the temporal convolutional network. It consists of an intermediate network, a target network, and a recognition and prediction model. The novel structure of the algorithm can make full use of the historical information from sensors. The performance of the GNN is evaluated based on the publicly available HuGaDB dataset, as well as on data collected by an inertial-based wearable motion capture device. The results show that the proposed approach is highly effective and achieves superior performance compared with existing methods.

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LSTM-based Network for Human Gait Stability Prediction in an Intelligent Robotic Rollator

Cited by 29 publications

References 45 publications

A Survey of Robots in Healthcare

A Survey of Robots in Healthcare

An LSTM-Based Prediction Method for Lower Limb Intention Perception by Integrative Analysis of Kinect Visual Signal

Gait Neural Network for Human-Exoskeleton Interaction

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