Development of a prototype of portable FES rehabilitation system for relearning of gait for hemiplegic subjects

A scoping review on recent trends in wearable sensors to analyze gait in people with stroke: From sensor placement to validation against gold-standard equipment

The purpose of the review is to evaluate wearable sensor placement, their impact and validation of wearable sensors on analyzing gait, primarily the postural instability in people with stroke. Databases, namely PubMed, Cochrane, SpringerLink, and IEEE Xplore were searched to identify related articles published since January 2005. The authors have selected the articles by considering patient characteristics, intervention details, and outcome measurements by following the priorly set inclusion and exclusion criteria. From a total of 1077 articles, 142 were included in this study and classified into functional fields, namely postural stability (PS) assessments, physical activity monitoring (PA), gait pattern classification (GPC), and foot drop correction (FDC). The review covers the types of wearable sensors, their placement, and their performance in terms of reliability and validity. When employing a single wearable sensor, the pelvis and foot were the most used locations for detecting gait asymmetry and kinetic parameters, respectively. Multiple Inertial Measurement Units placed at different body parts were effectively used to estimate postural stability and gait pattern. This review article has compared results of placement of sensors at different locations helping researchers and clinicians to identify the best possible placement for sensors to measure specific kinematic and kinetic parameters in persons with stroke.

Ankle dorsiflexion assist using a single sensor-based FES: Results from clinical study on patients with stroke

Objective

Ankle foot orthosis (AFO) commonly prescribed to manage foot-drop following stroke restricts ankle mobility. Commercially available functional electrical stimulation (FES) is an expensive alternative to achieve desired dorsiflexion during swing phase of the gait cycle. An in-house cost-effective innovative solution was designed and developed to address this problem.The aim of the study was to compare spatiotemporal gait characteristics of patients with foot-drop following stroke using commercially available FES against in-house developed versatile single sensor-based FES.

Material and Methods

Ten patients with cerebrovascular accident of at least 3 months duration and ambulant with/without AFO were recruited prospectively. They were trained with Device-1 (Commercial Device) and Device-2 (In-house developed, Re-Lift) for 7 h over 3 consecutive days with each device. Outcome measures included timed-up-and-go-test (TUG), six-minute-walk-test (6MWT), ten-meter-walk-test (10MWT), physiological cost index (PCI), instrumented gait analysis derived spatiotemporal parameters, and patient satisfaction feedback questionnaire. We calculated intraclass correlation between devices and median interquartile range. Statistical analysis included Wilcoxon-signed-rank-test and F-test (P < 0.05 was considered statistically significant). Bland Altman and scatter plots were plotted for both devices.

Results

Intraclass correlation coefficient for 6MWT (0.96), 10MWT (0.97), TUG test (0.99), and PCI (0.88) reflected high agreement between the two devices. Scatter plot and Bland Altman plots for the outcome parameters showed good correlation between two FES devices. Patient satisfaction scores were equal for both Device-1 and Device-2. There was statistically significant change in swing phase ankle dorsiflexion.

Conclusions

The study demonstrated good correlation between commercial FES and Re-Lift suggestive of the utility of low-cost FES device in clinical setting.

Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals

BACKGROUND

Machine learning models were satisfactorily implemented for estimating gait events from surface electromyographic (sEMG) signals during walking. Most of them are based on inter-subject approaches for data preparation. Aim of the study is to propose an intra-subject approach for binary classifying gait phases and predicting gait events based on neural network interpretation of sEMG signals and to test the hypothesis that the intra-subject approach is able to achieve better performances compared to an inter-subject one. To this aim, sEMG signals were acquired from 10 leg muscles in about 10.000 strides from 23 healthy adults, during ground walking, and a multi-layer perceptron (MLP) architecture was implemented.

RESULTS

Classification/prediction accuracy was tested vs. the ground truth, represented by the foot-floor-contact signal provided by three foot-switches, through samples not used during training phase. Average classification accuracy of 96.1 ± 1.9% and mean absolute value (MAE) of 14.4 ± 4.7 ms and 23.7 ± 11.3 ms in predicting heel-strike (HS) and toe-off (TO) timing were provided. Performances of the proposed approach were tested by a direct comparison with performances provided by the inter-subject approach in the same population. Comparison results showed 1.4% improvement of mean classification accuracy and a significant (p < 0.05) decrease of MAE in predicting HS and TO timing (23% and 33% reduction, respectively).

CONCLUSIONS

The study developed an accurate methodology for classification and prediction of gait events, based on neural network interpretation of intra-subject sEMG data, able to outperform more typical inter-subject approaches. The clinically useful contribution consists in predicting gait events from only EMG signals from a single subject, contributing to remove the need of further sensors for the direct measurement of temporal data.

Advances in neuroprosthetic management of foot drop: a review

This paper reviews the technological advances and clinical results obtained in the neuroprosthetic management of foot drop. Functional electrical stimulation has been widely applied owing to its corrective abilities in patients suffering from a stroke, multiple sclerosis, or spinal cord injury among other pathologies. This review aims at identifying the progress made in this area over the last two decades, addressing two main questions: What is the status of neuroprosthetic technology in terms of architecture, sensorization, and control algorithms?. What is the current evidence on its functional and clinical efficacy? The results reveal the importance of systems capable of self-adjustment and the need for closed-loop control systems to adequately modulate assistance in individual conditions. Other advanced strategies, such as combining variable and constant frequency pulses, could also play an important role in reducing fatigue and obtaining better therapeutic results. The field not only would benefit from a deeper understanding of the kinematic, kinetic and neuromuscular implications and effects of more promising assistance strategies, but also there is a clear lack of long-term clinical studies addressing the therapeutic potential of these systems. This review paper provides an overview of current system design and control architectures choices with regard to their clinical effectiveness. Shortcomings and recommendations for future directions are identified.

Recognition of Gait Phases with a Single Knee Electrogoniometer: A Deep Learning Approach

Artificial neural networks were satisfactorily implemented for assessing gait events from different walking data. This study aims to propose a novel approach for recognizing gait phases and events, based on deep-learning analysis of only sagittal knee-joint angle measured by a single electrogoniometer per leg. Promising classification/prediction performances have been previously achieved by surface-EMG studies; thus, a further aim is to test if adding electrogoniometer data could improve classification performances of state-of-the-art methods. Gait data are measured in about 10,000 strides from 23 healthy adults, during ground walking. A multi-layer perceptron model is implemented, composed of three hidden layers and a one-dimensional output. Classification/prediction accuracy is tested vs. ground truth represented by foot–floor-contact signals, through samples acquired from subjects not seen during training phase. Average classification-accuracy of 90.6 ± 2.9% and mean absolute value (MAE) of 29.4 ± 13.7 and 99.5 ± 28.9 ms in assessing heel-strike and toe-off timing are achieved in unseen subjects. Improvement of classification-accuracy (four points) and reduction of MAE (at least 35%) are achieved when knee-angle data are used to enhance sEMG-data prediction. Comparison of the two approaches shows as the reduction of set-up complexity implies a worsening of mainly toe-off prediction. Thus, the present electrogoniometer approach is particularly suitable for the classification tasks where only heel-strike event is involved, such as stride recognition, stride-time computation, and identification of toe walking.

A Basic Study on Detection of Movement State in Stride by Artificial Neural Network for Estimating Stride Length of Hemiplegic Gait Using IMU

In order to perform rehabilitation training for improving motor function, measurement of movements and evaluation of motor function become effective. In our research group, the method of estimating stride length during walking by using an inertial sensor attached to the foot was developed. However, since the method used thresholds to detect movement state in each stride for calculation of stride length, there was a difficulty in determination of threshold values for each subject and each stride with hemiplegic subjects in some cases. This study aimed at developing an automatic detection method of movement state in stride by artificial neural network (ANN) for hemiplegic gait. In this paper, three-layer ANN and four-layer ANN with feature extraction layers by autoencoder were tested. Teacher signals were obtained from measured sensor signals by the threshold-based method. The ANN with feature extraction layers was shown to be effective for detecting the movement state of healthy subjects and a hemiplegic subject. The movement state detected by ANN was also suggested to be effective in stride length estimation. It is expected to evaluate the ANN-based method using data measured with more hemiplegic subjects.

Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method

The gait phase classification method is a key technique to control an exoskeleton robot. Different people have different gait features while wearing an exoskeleton robot due to the gap between the exoskeleton and the wearer and their operation habits, such as the correspondence between the joint angle and the moment at which the foot contacts the ground, the amplitude of the joint angle and others. In order to enhance the performance of the gait phase classification in an exoskeleton robot using only the angle of hip and knee joints, a kernel recursive least-squares (KRLS) algorithm is introduced to build a gait phase classification model. We also build an assist torque predictor based on the KRLS algorithm in this work considering the adaptation of unique gait features. In this paper, we evaluate the classification performance of the KRLS model by comparing with two other commonly used gait recognition methods-the multi-layer perceptron neural network (MLPNN) method and the support vector machine (SVM) algorithm. In this experiment, the training and testing datasets for the models built by KRLS, MLPNN and SVM were collected from 10 healthy volunteers. The gait data are collected from the exoskeleton robot that we designed rather than collected from the human body. These data depict the human-robot coupling gait that includes unique gait features. The KRLS classification results are in average 3% higher than MLPNN and SVM. The testing average accuracy of KRLS is about 86%. The prediction results of KRLS are twice as good as MLPNN in assist torque prediction experiments. The KRLS performs in a good, stable, and robust way and shows model generalization abilities.