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.
Gait stability is mostly analysed by using wearable sensors @cite_31 , like motion markers placed on the human body to calculate the body's CoM and the foot placements @cite_42 , and force sensors to estimate the center of pressure of the feet @cite_34 . Gait stability analysis for walking aid users can be found in @cite_3 . Regarding stability classification, an early approach can be found in @cite_46 , where the authors use the body skeleton provided by the RGB-D Kinect sensor as input and perform action classification to detect four classes of falling scenarios and sitting. However, the system in tested only with a physical therapist performing different walking problems.
@cite_31 @cite_42 @cite_3 @cite_46 @cite_34
2503939313 1983935089 2724898217 2620846147 2162455072
Smart walkers aimed at providing better support than conventional walker. These devices have synchroneous movements with users but they have to act differently in case of unbalanced gait. The available discriminatory factors used in smart walkers are generally based only on position data. These static data cannot be representative of the dynamical process of falling. This paper proposes three methods that could be used to detect unbalanced gait. They are based on different kinematic data: forward velocity, angular velocity around transverse axis and eXtrapolated Center Of Mass (XCOM) with stability margins. These methods are evaluated and compared experimentally on 4 young healthy subjects experiencing unexpected unbalanced gait when using a robotic walker. During gait the body is in a continuous state of imbalance, with each subsequent step preventing a fall. Gait balance is maintained by regulating the interactions between the center of mass (CoM) and base of support (BoS). The purpose of this study was to investigate the interaction of the CoM position and velocity (CoMv) in relation to the dynamically changing BoS throughout gait. This was quantified using: (1) The shortest distance from the CoM to the boundary of the BoS; (2) The distance from the CoM to the centroid of the BoS; and (3) The distance from the CoM to the BoS along the direction of the CoMv. These interactions were investigated in healthy young adults, healthy older adults, and elderly fallers, who performed level walking at a self-selected speed. Elderly fallers demonstrated a conservative CoM–BoS separation at toe off and reduced balance control ability, specifically a decreased time to contact, when compared to healthy young adults at heel strike. Decreased time available in responding to perturbations might result in a greater number of falls. Understanding foot position and CoM trajectories might allow for appropriate rehabilitation practices. Abstract To assist balance and mobility, older adults are often prescribed walking aids. Nevertheless, surprisingly their use has been associated with increased falls-risk. To address this finding we first need to characterise a person's stability while using a walking aid. Therefore, we present a generalisable method for the assessment of stability of walking frame (WF) users. Our method, for the first time, considers user and device as a combined system. We define the combined centre of pressure (CoP system ) of user and WF to be the point through which the resultant ground reaction force for all feet of both the WF and user acts if theresultant moment acts only around an axisperpendicular tothe ground plane. We also define the combined base of support (BoS system ) to be the convex polygon formed by the boundaries of the anatomical and WF feet in contact with the ground and interconnecting lines between them. To measure these parameters we have developed an instrumented WF with a load cell in each foot which we use together with pressure-sensing insoles and a camera system, the latter providing the relative position of the WF and anatomical feet. Software uses the resulting data to calculate the stability margin of the combined system, defined as the distance between CoP system and the nearest edge of BoS system . Our software also calculates the weight supported through the frame and when each foot (of user and or frame) is on the floor. Finally, we present experimental work demonstrating the value of our approach. Human action behavior classification plays an important role for controlling systems having interaction with human users. Safety and dependability of such systems are crucial especially for walking assist systems. In this paper, upper body joint model of a user of a walking assist system is extracted using a depth sensor and a probabilistic model is proposed to detect possible non-walking states that might happen to the user. The 3D model of upper body skeleton, is reduced in dimension by applying Principal Component Analysis (PCA). The principal components are tested to have a normal distribution allowing a multivariate normal distribution fitting for walking data. The model is shown to be capable of recognizing four different falling scenarios and sitting. In these non-walking states, the motion of a passive-type walker called “RT Walker”, is controlled by generating brake force to assure fall prevention and sitting standing up support. The experimental data is gathered from an experienced physical therapist capable of imitating different walking problems. To assess the improvement of human body balance, a low cost and portable measuring device of center of pressure (COP), known as center of pressure and complexity monitoring system (CPCMS), has been developed for data logging and analysis. In order to prove that the system can estimate the different magnitude of different sways in comparison with the commercial Advanced Mechanical Technology Incorporation (AMTI) system, four sway tests have been developed (i.e., eyes open, eyes closed, eyes open with water pad, and eyes closed with water pad) to produce different sway displacements. Firstly, static and dynamic tests were conducted to investigate the feasibility of the system. Then, correlation tests of the CPCMS and AMTI systems have been compared with four sway tests. The results are within the acceptable range. Furthermore, multivariate empirical mode decomposition (MEMD) and enhanced multivariate multiscale entropy (MMSE) analysis methods have been used to analyze COP data reported by the CPCMS and compare it with the AMTI system. The improvements of the CPCMS are 35 to 70 (open eyes test) and 60 to 70 (eyes closed test) with and without water pad. The AMTI system has shown an improvement of 40 to 80 (open eyes test) and 65 to 75 (closed eyes test). The results indicate that the CPCMS system can achieve similar results to the commercial product so it can determine the balance.