Robotic Exoskeleton Gait Training in Stroke: An Electromyography-Based Evaluation

The recovery of symmetric and efficient walking is one of the key goals of a rehabilitation program in patients with stroke. The use of overground exoskeletons alongside conventional gait training might help foster rhythmic muscle activation in the gait cycle toward a more efficient gait. About twenty-nine patients with subacute stroke have been recruited and underwent either conventional gait training or experimental training, including overground gait training using a wearable powered exoskeleton alongside conventional therapy. Before and after the rehabilitation treatment, we assessed: (i) gait functionality by means of clinical scales combined to obtain a Capacity Score, and (ii) gait neuromuscular lower limbs pattern using superficial EMG signals. Both groups improved their ability to walk in terms of functional gait, as detected by the Capacity Score. However, only the group treated with the robotic exoskeleton regained a controlled rhythmic neuromuscular pattern in the proximal lower limb muscles, as observed by the muscular activation analysis. Coherence analysis suggested that the control group (CG) improvement was mediated mainly by spinal cord control, while experimental group improvements were mediated by cortical-driven control. In subacute stroke patients, we hypothesize that exoskeleton multijoint powered fine control overground gait training, alongside conventional care, may lead to a more fine-tuned and efficient gait pattern.

Knee-stretched walking with toe-off and heel-strike for a position-controlled humanoid robot based on model predictive control

Knee-stretched walking is considered to be a human-like and energy-efficient gait. The strategy of extending legs to obtain vertical center of mass trajectory is commonly used to avoid the problem of singularities in knee-stretched gait generation. However, knee-stretched gait generation utilizing this strategy with toe-off and heel-strike has kinematics conflicts at transition moments between single support and double support phases. In this article, a knee-stretched walking generation with toe-off and heel-strike for the position-controlled humanoid robot has been proposed. The position constraints of center of mass have been considered in the gait generation to avoid the kinematics conflicts based on model predictive control. The method has been verified in simulation and validated in experiment.

A grid gradient approximation method of energy-efficient gait planning for biped robots

In this article, an energy-efficient gait planning algorithm that utilizes both 3D body motion and an allowable zero moment point region (AZR) is presented for biped robots based on a five-mass inverted pendulum model. The product of the load torque and angular velocity of all joint motors is used as an energy index function (EIF) to evaluate the energy consumption during walking. The algorithm takes the coefficients of the finite-order Fourier series to represent the motion space of the robot body centroid, and the motion space is gridded by discretizing these coefficients. Based on the geometric structure of the leg joints, an inverse kinematics method for calculating grid intersection points is designed. Of the points that satisfy the AZR constraints, the point with the lowest EIF value in each network line is selected as the seed. In the neighborhood of the seed, the point with the minimum EIF value in the motion space is successively approximated by the gradient descent method, and the corresponding joint angle sequence is stored in the database. Given a distance to be traveled, our algorithm plans a complete walking trajectory, including two starting steps, multiple cyclic steps, and two stopping steps, while minimizing the energy consumption. According to the preset AZR, the joint angle sequences of the robot are read from the database, and these sequences are adjusted for each step according to the zero-moment-point feedback during walking. To determine the effectiveness of the proposed algorithm, both dynamic simulation and walking experiment in the real environment were carried out. The experimental results show that compared with algorithms based on the fixed body height or vertical body motion, our gait algorithm has a significant energy-saving effect.

An Efficient Automatic Gait Anomaly Detection Method Based on Semisupervised Clustering

The aim of this work is to develop a common automatic computer method to distinguish human individuals with abnormal gait patterns from those with normal gait patterns. As long as the silhouette gait images of the subjects are obtainable, the proposed method is capable of providing online anomaly gait detection result without additional work on analyzing the gait features of the target subjects before ahead. Moreover, the proposed method does not need any parameter settings by users and can start producing detection results under the work by only collecting a very small number of gait samples, even though none of those gait samples are abnormal. Therefore, the proposed method can provide fast and simple deployment for various anomaly gait detection application scenarios. The proposed method is composed of two main modules: (1) feature extraction from gait images and (2) anomaly detection via binary classification. In the first module, a new representation of the most frequently involved area of the silhouette gait images called full gait energy image (F-GEI) is proposed. Furthermore, based on the F-GEI, a novel and simple method characterizing individual walking properties is developed to extract gait features from individual subjects. In the second module, based on the very limited prior knowledge on the target dataset, a semisupervised clustering algorithm is proposed to perform the binary classification for detecting the gait anomaly of each subject. The performance of the proposed gait anomaly detection method was evaluated on the human gaits dataset in comparison with three state-of-the-art methods. The experiment results show that the proposed method is an effective and efficient gait anomaly detection method in terms of accuracy, robustness, and computational efficiency.

Wearable Robotic Ankle Resistance Training Improves Neuromuscular Control and Walking Efficiency in Cerebral Palsy

Cerebral palsy (CP) is characterized by deficits in motor function due to reduced neuromuscular control. We leveraged the guiding principles of motor learning theory to design a wearable robotic intervention intended to improve neuromuscular control of the ankle. The goal of this pilot clinical trial was to determine the response to four weeks of exoskeleton ankle resistance therapy (exo-therapy) in children with CP. Five children with CP (12 – 17 years, GMFCS I – II, four males and one female) were recruited for ten, 20-minute sessions of exo-therapy. Surface electromyography, three-dimensional kinematics, and metabolic data were collected at baseline and after training was complete. Changes in neural complexity (via muscle synergy analysis) and metabolic cost were compared to retrospective age- and GMFCS-matched controls who had undergone either single event multi-level orthopedic surgery (SEMLS) or selective dorsal rhizotomies (SDR). Participants displayed decreased co-contraction at the ankle (−29 ± 11%, p = 0.02) and a more typical plantar flexor activation profile (33 ± 13%, p = 0.01), and improvements in neuromuscular control led to a more mechanically-efficient gait pattern (58 ± 34%, p < 0.05) with a reduced metabolic cost of transport (−29 ± 15%, p = 0.02). There were significant increases in neural complexity (5 ± 3%, p = 0.03), where were significantly greater than those seen with SEMLS and SDR (p < 0.01 for both). Ankle exoskeleton resistance therapy shows promise for rapidly improving neuromuscular control for children with CP, and may serve as a meaningful rehabilitative complement to common surgical procedures.

Gait Recognition as an Authentication Method for Mobile Devices

With the rate at which smartphones are currently evolving, more and more of human life will be contained in these devices. At a time when data privacy is extremely important, it is crucial to protect one’s mobile device. In this paper, we propose a new non-intrusive gait recognition based mechanism that can enhance the security of smartphones by rapidly identifying users with a high degree of confidence and securing sensitive data in case of an attack, with a focus on a potential architecture for such an algorithm for the Android environment. The motion sensors on an Android device are used to create a statistical model of a user’s gait, which is later used for identification. Through experimental testing, we prove the capability of our proposed solution by correctly classifying individuals with an accuracy upwards of 90% when tested on data recorded during multiple activities. The experiments, conducted on a low sampling rate and at short time intervals, show the benefits of our solution and highlight the feasibility of an efficient gait recognition mechanism on modern smartphones.

Gait Recognition Using Deep Learning

Gait recognition mainly uses different postures of each individual to perform identity authentication. In the existing methods, the full-cycle gait images are used for feature extraction, but there are problems such as occlusion and frame loss in the actual scene. It is not easy to obtain a full-cycle gait image. Therefore, how to construct a highly efficient gait recognition algorithm framework based on a small number of gait images to improve the efficiency and accuracy of recognition has become the focus of gait recognition research. In this chapter, deep neural network CRBM+FC is created. Based on the characteristics of Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) fusion, a method of learning gait recognition from GEI to output is proposed. A brand-new gait recognition algorithm based on layered fu-sion of LBP and HOG is proposed. This chapter also proposes a feature learning network, which uses an unsupervised convolutionally constrained Boltzmann machine to train the Gait Energy Images (GEI).