In this work, we present a novel framework for on-line human gait stability prediction of the elderly users of an intelligent robotic rollator using Long Short Term Memory (LSTM) networks, fusing multimodal RGB-D and Laser Range Finder (LRF) data from non-wearable sensors. A Deep Learning (DL) based approach is used for the upper body pose estimation. The detected pose is used for estimating the body Center of Mass (CoM) using Unscented Kalman Filter (UKF). An Augmented Gait State Estimation framework exploits the LRF data to estimate the legs' positions and the respective gait phase. These estimates are the inputs of an encoder-decoder sequence to sequence model which predicts the gait stability state as Safe or Fall Risk walking. It is validated with data from real patients, by exploring different network architectures, hyperparameter settings and by comparing the proposed method with other baselines. The presented LSTM-based human gait stability predictor is shown to provide robust predictions of the human stability state, and thus has the potential to be integrated into a general user-adaptive control architecture as a fall-risk alarm.
Fall detection and prevention is a hot topic in the field of assistive robotics @cite_22 . Most of the proposed control strategies for robotic assistive platforms in literature do not deal with the problem of fall prevention and research works focus on navigation and obstacle avoidance @cite_35 @cite_5 @cite_44 . However, there exist some targeted research focusing on incorporating strategies for preventing or detecting fall incidents and facilitating user's mobility. In @cite_9 @cite_43 the authors developed an admittance controller for a passive walker with a fall-prevention function considering the position and velocity of the user, utilizing data from two LRFs. They model the user as a solid body link, in order to compute the position of the center of gravity @cite_50 , based on which they applied a braking force on the rollator to prevent falls. A fall detection for a cane robot was presented in @cite_45 @cite_4 , that computes the zero moment point stability of the elderly, using on shoe sensors that provide ground force reactions.
@cite_35 @cite_4 @cite_22 @cite_9 @cite_44 @cite_43 @cite_45 @cite_50 @cite_5
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An intelligent walking-aid cane robot is developed for assisting the elderly and the physically challenged with walking. A motion control method is proposed for the cane robot based on human walking intention estimation. Moreover, the safety is investigated for both the cane robot and the elderly. The fall detection and prevention concepts are proposed to guarantee the safety of the elderly while walking with the cane robot. However, the deficiency of the cane robot is that it can be overturned easily because of its small size and light weight. Therefore, a controllable universal joint is designed for adjusting the tilted angle of its stick. The stability of the cane robot during the fall prevention procedure can then be enhanced by controlling the tilted angle of stick to an optimal position. A center of pressure (COP)-based fall detection (COP-FD) method is used to detect the risk of falling. In this method, the user's COP is calculated in real time using an integrated force sensory system, which comprises a six-axis force torque sensor and an inshoe load sensor. When the COP reaches the boundary of the specified safety area, i.e., the support polygon, it is assessed that the user is going to fall down. The COP-FD method can be used in various cases of falling. However, for cases of stumbling, a rapid fall detection method is proposed based on leg motion detection, and Dubois' fuzzy possibility theory is applied to adapt to different users. When the risk of falling has been detected, a fall prevention impedance control is executed considering both the interaction compliance and system stability. In the study, a control simulation platform was established to obtain the optimal controller parameters, and all the proposed methods were finally verified through simulations and experiments. An intelligent cane robot is designed for assisting the elderly or physically challenged people walking in daily life. The cane robot is driven by an Omni-directional mobile base; an aluminous stick is fixed on the base. A variety of sensors are installed on the cane robot for estimating the user's intention and status (normal walking or falling). The user's intentions of to which user want to move can be estimated by analyzing the active force on cane robot. As a nursing-care robot, the safety is a most important concern; before the elderly fall over, the cane robot should detect the sign of the falling and control the robot to assist the elderly to prevent it. Therefore a fall detection concept is proposed to estimate the risk of the falling based on the theory of zero moment point (ZMP) stability. An on-shoe sensor is designed to measure the ground reaction force and calculate the ZMP based on distributed force. The safety walking status is defined in the case of the ZMP is in the boundary of the support polygon. While the ZMP moving out of that boundary, the user will fall over. In this paper, we introduce a passive-type walker using servo brakes referred to as RT Walker. RT Walker realizes several functions such as obstacles steps avoidance function, path following function, gravity compensation function, variable motion characteristics function, etc., by controlling only servo brakes without using servo motors. These passive-type systems are dependable for using practically in real world environment, because of the passive dynamics with respect to the applied force moment, simple structure, lightweight, and so on. However, the most serious problem of them is the falling accident of user, because the passive-type systems are lightweight and move easily based on the small force moment applied by the user unintentionally. In this paper, we pay attention to a method for estimating the human state during the usage of the walker and propose a motion control algorithm for realizing a fall-prevention function based on its human state. We also implement the proposed control methods in RT Walker experimentally and illustrate the validity of them Humanoid robots are prone to fall caused by disturbances. If the disturbance is weak, a humanoid robot can avoid falling by using feedback control. If the disturbance is strong, humanoid robots can perform an Ukemi motion: an active shock-reducing motion. This research proposes a technique for selecting an optimal strategy to handle disturbances while walking, and describes a method of generating Ukemi motion. This technique detects disturbances using sensor data while walking steadily. Moreover, an experiment with a real robot was performed where fall detection was computed using discriminant analysis of walking data labelled as fall and non-fall. Furthermore, this study uses the three-dimensional linear inverted pendulum mode (3D-LIPM), which has been utilized in gait generation of humanoid robots, to generate a solution for the center of gravity for the contracting expanding fall correction movement. The effectiveness of the proposed technique was confirmed by the verification experiments.