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
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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. Walking impairment resulted by various chronic diseases, disorders and injuries have been investigated using recent emerging wearable technology, for instance, gait assessment using inertial body sensors in 6-minute walk (6MW) for persons with Multiple Sclerosis (PwMS) to identify spatiotemporal features useful to assess MS progression. However, most studies to date have investigated the features extracted from movements of the lower limbs and do not provide a holistic gait assessment. A recent pilot study demonstrated that the holistic gait assessment such as evaluating the associations among lower and upper limbs provided better discrimination between healthy controls and PwMS. This paper is motivated by this and further aim to answer the following question: can we identify the temporal gait patterns in terms of the holistic gait assessment? Traditionally this suffers from the statistical property of the causality inference method adopted by previous study. We proposed a deep convolutional neural network (CNN) to learn the temporal and spectral associations among the time-series motion data captured by the inertial body sensors. A simulated model was developed to train the CNN, and then the trained CNN was adopted to assess the gait performance from a pilot dataset with 41 subjects (28 PwMS and 13 healthy controls). Experimental results are reported to illustrate the performance of the proposed approach. Gait analysis is a domain of interest in clinical medical practice, both for neurological and non-neurological abnormal troubles. Marker-based systems are the most favored methods of human motion assessment and gait analysis, however, these systems require specific equipment and expertise and are cumbersome, costly and difficult to use. In this paper we compare two low-cost and marker-less systems that are: (1) A Kinect in front of a treadmill and (2) a set of two camcorders on the sides of the treadmill, used to reconstruct the skeleton of a subject during walk. We validated our method with ground truth data obtained with markers manually placed on the subject’s body. Finally, we present an application for asymmetric gait recognition. Our results on different subjects showed that, compared to the Kinect, the two-camcorder approach was very efficient and provided accurate measurements for gait assessment. In this paper, we propose a webcam-based system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed, step time, and step length from a 3-D voxel reconstruction, which is built from two calibrated webcam views. The gait parameters are validated with a GAITRite mat and a Vicon motion capture system in the laboratory with 13 participants and 44 tests, and again with GAITRite for 8 older adults in senior housing. Excellent agreement with intraclass correlation coefficients of 0.99 and repeatability coefficients between 0.7 and 6.6 was found for walking speed, step time, and step length given the limitation of frame rate and voxel resolution. The system was further tested with ten seniors in a scripted scenario representing everyday activities in an unstructured environment. The system results demonstrate the capability of being used as a daily gait assessment tool for fall risk assessment and other medical applications. Furthermore, we found that residents displayed different gait patterns during their clinical GAITRite tests compared to the realistic scenario, namely a mean increase of 21 in walking speed, a mean decrease of 12 in step time, and a mean increase of 6 in step length. These findings provide support for continuous gait assessment in the home for capturing habitual gait.