Lower Limb Movement Research Articles

A Wearable Soft Robotic Exoskeleton for Hip Flexion Rehabilitation.

Leg motion is essential to everyday tasks, yet many face a daily struggle due to leg motion impairment. Traditional robotic solutions for lower limb rehabilitation have arisen, but they may bare some limitations due to their cost. Soft robotics utilizes soft, pliable materials which may afford a less costly robotic solution. This work presents a soft-pneumatic-actuator-driven exoskeleton for hip flexion rehabilitation. An array of soft pneumatic rotary actuators is used for torque generation. An analytical model of the actuators is validated and used to determine actuator parameters for the target application of hip flexion. The performance of the assembly is assessed, and it is found capable of the target torque for hip flexion, 19.8 Nm at 30°, requiring 86 kPa to reach that torque output. The assembly exhibits a maximum torque of 31 Nm under the conditions tested. The full exoskeleton assembly is then assessed with healthy human subjects as they perform a set of lower limb motions. For one motion, the Leg Raise, a muscle signal reduction of 43.5% is observed during device assistance, as compared to not wearing the device. This reduction in muscle effort indicates that the device is effective in providing hip flexion assistance and suggests that pneumatic-rotary-actuator-driven exoskeletons are a viable solution to realize more accessible options for those who suffer from lower limb immobility.

Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction Through a Dense Co-Attention Symmetric Mechanism.

A challenging task for the biological neural signal-based human-exoskeleton interface is to achieve accurate lower limb movement prediction of patients with hemiplegia in rehabilitation training scenarios. The human-exoskeleton interface based on single-modal biological signals such as electroencephalogram (EEG) is currently not mature in predicting movements, due to its unreliability. The multimodal human-exoskeleton interface is a very novel solution to this problem. This kind of interface normally combines the EEG signal with surface electromyography (sEMG) signal. However, their use for the lower limb movement prediction is still limited—the connection between sEMG and EEG signals and the deep feature fusion between them are ignored. In this article, a Dense con-attention mechanism-based Multimodal Enhance Fusion Network (DMEFNet) is proposed for predicting lower limb movement of patients with hemiplegia. The DMEFNet introduces the con-attention structure to extract the common attention between sEMG and EEG signal features. To verify the effectiveness of DMEFNet, an sEMG and EEG data acquisition experiment and an incomplete asynchronous data collection paradigm are designed. The experimental results show that DMEFNet has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 82.96 and 88.44%, respectively.

Spinal motor atrophy in a floppy infant: a case report

Spinal muscular atrophy (SMA) is a progressive neuromuscular condition typically due to homozygous absence of the survival motor neuron gene (SMN1). It is characterized by progressive muscle weakness that limits motor development. Muscle weakness is associated with muscle atrophy and hypotonia, absence or marked decrease of deep tendon reflexes. Proximal muscles are more affected than distal muscles. Contractures and spinal deformity are a common impairment. Child with SMA gradually lose function over time. Electromyelography (EMG) and nerve conduction velocity shows anterior horn cells abnormality. No specific medical management is available for children with SMA. Interventions are usually supportive and may include physical therapy, occupational therapy, speech therapy, nutrition, orthotic management and possible surgery. This case report was of a 4 and half month-old female child with paucity of movement of lower limb since birth along with developmental delay.

Design and Control of a Flexible Exoskeleton to Generate a Natural Full Gait for Lower-Limb Rehabilitation

Abstract Strokes caused by cerebrovascular diseases often result in severe damage to locomotion, movement, and cognitive functions. Rehabilitation is an effective way to regain locomotion ability in daily life. However, there are mismatching biological joints, non-full rehabilitation gaits, and indirect joint feedback among conventional exoskeletons or devices. Hence, this paper proposed a novel flexible Bowden cable driving exoskeleton to achieve natural full walking gaits utilizing direct lower-limb movement feedback. First, complete flexible joint rotation kinematics was proposed by using a pair of agonism–antagonism flexible Bowden cables. Then, a personalized rehabilitation walking gait and feedforward proportional-integral-differential controller were adopted to drive the lower limbs to realize complete joint movement and natural full gait locomotion. Finally, a prototype was designed to verify the effectiveness. Mannequin and human tests were conducted, and the results show that the real walking gait is consistent with the reference natural human gait and that assistive torque was applied to humans to reduce muscle activation without significantly changing the foot pressure.

Relationship between Trunk Performance and the Stair Ascending and Descending in Post Stroke Subjects

Purpose and Objective:- Trunk being the central keypoint of the body, good trunk stability is essential for reaching and grasping and proximal trunk control is a prerequisite for distal lower limb movement control in functional activities like walking and stair climbing. There are several clinical tools to measure trunk performance such as The sitting balance item of the Motor assessment scale, The trunk control test, leg and trunk scale of the Rivermead Motor Assessment Scale. The Trunk Impairment Scale (TIS) a new tool to measure motor impairment of trunk after stroke was developed in 2004 by G Verheyden et al. Studies were conducted to correlate the TIS with Tinette, TUG test, 10 min walk test. However studies were not conducted to show the relationship between the trunk impairment and stair climbing in stroke subject. Design:- Correlational study Settings:-HOSMAT Hospital, BangaloreSubjects:-A total of 32 stroke subjects with a mean (SD) age of 53.90 ± 13.07 yrs. 21 males and 11 females participated.Method:-All the 32 subjects were administered with TIS and then followed by stair ascend and descend test. Outcome measures:- TIS, Stair ascend and descend test.Results:-The median scores of static, dynamic sitting balance and coordination ( 5, 6, 1) respectively. Mean score of stair ascend - 18.87±11.05 and for stair descend - 20.60±12.43.There was a moderate negative correlation between TIS and stair ascend with rs= -0.54 (P=0.001), moderate negative correlation between TIS and stair descend with rs=-0.63(P=0.001) and also moderate negative correlation between TIS and stair ascend + descend rs= 0.58(P=0.001).Conclusion:-The Trunk impairment scale shows moderate correlation with the stair ascend and descend test, which suggests that a person scoring less on the Trunk Impairment Scale (TIS ) takes more time to ascend and descend the stairs.

Computation of Fetal Kicking in Various Fetal Health Examinations: A Systematic Review.

Fetal movement has always been considered an essential indicator to evaluate the health of the unborn fetus. Many factors affect fetal movement. The frequency of fetal kicking is an important measurement of whether fetal development is progressing and healthy. Various instruments and methods of detecting fetal movement have been used and each method has its advantages and disadvantages. Although limited by the fetal environment in utero, the finite element method and musculoskeletal model can be used to calculate fetal lower limb movement. This review aims to summarize the current detection techniques for fetal movement, especially in the lower limbs. These will be outlined by describing the different measurements of fetal movement, and the related biomechanical analyses of fetal lower limb skeletogenesis and the associated muscular development to better evaluate and calculate the movements of the fetus in the womb.

Lower Limb Motion Intention Recognition Based on sEMG Fusion Features

Surface Electromyography (sEMG) has been extensively used for gait analysis and robotic control. Since the low accuracy of intention recognition limits the development of exoskeleton robots, a dynamic adaptive neural network algorithm based on the multi-feature fusion of sEMG signals was proposed to realize the accurate identification of eight lower limb movements. Firstly, the original sEMG signals were collected from eighty volunteers through gait experiments. Secondly, the feature parameters of time domain, frequency domain, and sample entropy were extracted and merged. Then, the improved differential evolution (DE) algorithm was proposed to optimize the weight values of each feature for data fusion. Thirdly, the backpropagation neural network(BPNN) used in this study is the most common model for intention recognition. However, BPNN has the problems of low accuracy and slow convergence speed, this paper proposed a novel dynamic adaptive neural network model (GA-DANN). The novel model uses genetic algorithm (GA) to optimize the weights of the BPNN to accelerate convergence. The dynamic adaptive learning rate was introduced to overcome the difficulty of determining the learning rate. The experimental results show that the proposed algorithm with multi-feature fusion information could accurately distinguish eight lower limb motion intentions, and the averaged recognition accuracy reaches 94.89%, which is 10% higher than the traditional BPNN. The averaged recognition time is only 109.67ms. This research will provide technical support for the application of rehabilitation robots.

Continuous motion estimation of lower limbs based on deep belief networks and random forest.

Due to the lag problem of traditional sensor acquisition data, the following movement of exoskeleton robots can affect the comfort of the wearer and even the normal movement pattern of the wearer. In order to solve the problem of lag in exoskeleton motion control, this paper designs a continuous motion estimation method for lower limbs based on the human surface electromyographic (sEMG) signal and achieves the recognition of the motion intention of the wearer through a combination of the deep belief network (DBN) and random forest (RF) algorithm. First, the motion characteristics of human lower limbs are analyzed, and the hip-knee angle and sEMG signal related to lower limb motion are collected and extracted; then, the DBN is used in the dimensionality reduction of the sEMG signal feature values; finally, the motion intention of the wearer is predicted using the RF model optimized by the genetic algorithm. The experimental results show that the root mean square error of knee and hip prediction results of the combined algorithm proposed in this article improved by 0.2573° and 0.3375°, respectively, compared to the algorithm with dimensionality reduction by principal component analysis, and the single prediction time is 0.28 ms less than that before dimensionality reduction, provided that other conditions are exactly the same.

Impedance Modulation Control of a Lower-Limb Exoskeleton to Assist Sit-to-Stand Movements

As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facing elderly and dependent people. In this article, a novel impedance modulation strategy of a lower-limb exoskeleton is proposed to provide appropriate power and balance assistance during STS movements while preserving the wearer’s control priority. The impedance modulation control strategy ensures adaptation of the mechanical impedance of the human–exoskeleton system toward a desired one requiring less wearer’s effect while reinforcing the wearer’s balance control ability during STS movements. A human joint torque observer is designed to estimate the joint torques developed by the wearer using joint position kinematics instead of electromyography or force sensors; a time-varying desired impedance model is proposed according to the wearer’s lower-limb motion ability. A virtual environmental force is designed for balance reinforcement control. Stability and robustness of the proposed method are theoretically analyzed. Simulations are implemented to illustrate the characteristics and performance of the proposed approach. Experiments with four healthy subjects are carried out to evaluate the effectiveness of the proposed method and show satisfactory results in terms of appropriate power assist and balance reinforcement.

Recognition and Prediction of Badminton Attitude Based on Video Image Analysis

Among many sports, badminton is one of the most popular events and it is deeply loved by people. However, there is relatively little research on pose recognition and prediction of badminton, so this paper uses video image analysis to perform badminton action recognition prediction and action classification. In order to better realize the lower limb movement pattern recognition and motion posture prediction of badminton players, this paper chooses BP neural network algorithm to establish a badminton motion posture recognition and prediction model based on video image analysis. The simulation results of the models are compared, and it is found that the recognition of the motion pose and the prediction model established by the neural network are accurate, and they almost coincide with the actual motion trend. In this paper, a face detection and recognition framework is established. The algorithm is used to filter the pictures of the input recognition network. Through calculation, the average accuracy rate of the face detection algorithm realized in this paper can reach 92.6%. This shows that the face detection algorithm implemented in this paper basically meets the standards. In this paper, a single inertial sensor is used to classify and recognize badminton movements using sensors located on the right wrist, left wrist, waist, and right ankle. This shows that the right wrist is the best position and achieves different strokes.

Greater prefrontal activation during sitting toe tapping predicts severer freezing of gait in Parkinson's disease: an fNIRS study.

Previous studies have revealed that, compared with Parkinson's disease (PD) patients without freezing of gait (FoG), the ones with FoG showed greater prefrontal activation while doing lower-limb movements involving standing, walking and turning, which require both locomotor and balance control. However, the relation between FoG and pure locomotor control as well as its underlying mechanism remain unclear. A total of 56 PD subjects were recruited and allocated to PD-FoG and PD-noFoG subgroups, and 34 age-matched heathy adults were included as heathy control (HC). Functional near-infrared spectroscopy was used to measure their prefrontal activation in a sitting lower-limb movement task, wherein subjects were asked to sit and tap their right toes as big and as fast as possible. Result of one-way ANOVA (Group: PD-FoG vs. PD-noFoG vs. HC) revealed greater activation in the right prefrontal cortex in the PD-FoG group than in the other 2 groups. Linear mixed-effects model showed consistent result. Furthermore, the right prefrontal activation positively correlated with the severity of FoG symptoms in PD-FoG patients. These findings suggested that PD patients with FoG require additional cognitive resources to compensate their damaged automaticity in locomotor control, which is more pronounced in severe FoG patients than milder ones.

Modification of Hand Muscular Synergies in Stroke Patients after Robot-Aided Rehabilitation

The central nervous system (CNS) is able to control a very high number of degrees of freedom to perform complex movements of both upper and lower limbs. However, what strategies the CNS adopts to perform complex tasks are not completely clear and are still being studied. Recent studies confirm that stroke subjects with mild and moderate impairment show altered upper limb muscle patterns, but the muscular patterns of the hand have not completely investigated, although the hand represents a paramount tool for performing activities of daily living (ADLs) and stroke can significantly alter the mobilization of this part of the body. In this context, this study aims at investigating hand muscular synergies in chronic stroke patients and evaluating some possible benefits in the robot-aided rehabilitation treatment of the hand in these subjects. Seven chronic stroke patients with mild-to-moderate impairment (FM>30) were involved in this study. They received a 5-week robot-aided rehabilitation treatment with the Gloreha hand exoskeleton, and muscle synergies of both the healthy and injured hand were evaluated at the beginning and at the end of the treatment. The performed analysis showed a very high degree of similarity of the involved synergies between the healthy and the injured limb both before and after the rehabilitation treatment (mean similarity index values: H-BR: 0.88±0.03, H-AR: 0.94±0.03, BR-AR: 0.89±0.05). The increasing similarity is regarded as an effect of the robot-aided rehabilitation treatment and future activities will be performed to increase the population involved in the study.

Both Upper and Lower Limb Movements Contribute to Aesthetics of the Piqué Arabesque in Ballet.

This study aimed to identify the factor structures-which are the predominant frameworks for describing the basic dimensions of a concept-that contribute to the aesthetics of body position in ballet. This study was composed of three-dimensional movement analysis and subjective evaluation. Fourteen ballet dancers participated in the three-dimensional movement analysis. Thirty-six reflective markers were attached to the dancers' bodies, after which the dancers performed a piqué arabesque, a position in which the weight transfers from one leg to the other. This movement was captured using eight optical cameras and one video camera. Biomechanical parameters, such as the joint angle and velocity of each body part, were calculated from the coordinates of the markers. Twenty-eight videos recorded in the three-dimensional movement analysis were viewed by 51 observers with ballet experience. The observers subjectively evaluated the videos through four category pairs-"beautiful-ugly," "like-dislike," "interesting-not interesting," and "good-bad"-on a five-point semantic differential scale. Two groups, the top and bottom 30%, were extracted based on the "beautiful-ugly" rating and compared using an independent t-test. In addition, exploratory factor analysis was performed on the biomechanical parameters that showed significant differences. Five factors were identified: "stability of the right distal upper limb and upper body," "torso displacement speed," "stability of the left distal upper limb and line of the support leg," "height of the gesture leg," and "stability of the support leg around the hip joint and line of the limbs on the gesture leg side." These results indicate that the movements of both upper and lower limbs contributed to the aesthetics of the ballet position of piqué arabesque. These findings will be useful for ballet teachers and dancers to understand the intrinsic aesthetics of movements.

Comparison of the effect and durability of foot massage methods on the balance of the blindness

Background and Aim:Weakness in the sensory-deep receptors of the sole of the foot is one of the important factors in lower limb movement disorders and balance in the blindness. Therefore, the aim of this study was to compare the effect and durability of foot massage techniques on the balance of the blindness Methods:The present study was a one-way blind with a pretest-posttest design. The statistical sample consisted of 30 blind boys aged 20-30 years who were randomly divided into three groups of 10 people. The protocol of massage methods was performed on the samples for 21 days. Biodex balance system was used to evaluate the balance. The normality of the data was analyzed using Shapirowilk test and the analysis of variance with repeated measures was used to analyze the differences within the group and the ANOVA test was used to examine the differences between the groups at the significance level of 0.05. Results:The results of intra-group test showed that all three methods of foot massage improved static and dynamic balance in the blind (P <0.001). The results of ANOVA test for comparison between groups showed that there is no significant difference between the three methods of foot massage on static balance and posterior anterior index of dynamic surface and internal-external index of dynamic surface (P> 0.05) and only on the general surface index. There is a significant difference in the dynamics of hand massage compared to the other two massages (P <0.001). Conclusion:Due to the significant effect of foot massage methods on the balance of the blind, foot massage can be used as an effective factor in strengthening the sensory receptors of the foot and ultimately improving the balance of the blind.

Binary Particle Swarm Optimization-Based Feature Selection for Predicting the Class of the Knee Angle from EMG Signals in Lower Limb Movements

Binary Particle Swarm Optimization-Based Feature Selection for Predicting the Class of the Knee Angle from EMG Signals in Lower Limb Movements

Analysis of the relationship between muscular strength and joint stiffness in children with Down syndrome during drop landing.

BACKGROUND: Children with Down syndrome (DS) have critical biomechanical impairments such as increased ligamentous laxity, muscle hypotonia, and dysfunctional motor coordination, which makes performing everyday tasks challenging.OBJECTIVE: The purpose of the study was to explore the differences in the vertical joint stiffness, plantar force, and range of motion during drop landing for DS and age-matched typically developing children.METHODS: Six young male children with DS and age-matched seven healthy typically developing children (TD) assessed joint strength using an isokinetic dynamometer and performed five trials of single-leg drop jump using force platform and motion capture system.RESULTS: The peak vertical ground reaction force (VGRF), Range of motion (ROM), joint stiffness, and joint strength of lower limb were calculated and compared across DS and TD groups. The results revealed a significantly larger peak VGRF [2.857, 0.001] values for the DS group compared to the TD groups. The results of Spearman’s correlation analysis showed a negative correlation between hip joint stiffness and knee joint ROM [0.886, 0.05] and ankle joint stiffness and knee joint ROM [0.829, 0.05] for DS.CONCLUSIONS: The abnormal movements observed among DS was not due to the difference in stiffness of the lower extremity but due to the utilization of different landing mechanisms with changes in ROM.

A pelvic support weight rehabilitation system tracing the human center of mass height

The body weight support rehabilitation training system has now become an important treatment method for the rehabilitation of lower limb motor dysfunction. In this paper, a pelvic brace body weight support rehabilitation system is proposed, which follows the center of mass height (CoMH) of the human body. It aims to address the problems that the existing pelvic brace body weight support rehabilitation system with constant impedance provides a fixed motion trajectory for the pelvic mechanism during the rehabilitation training and that the patients have low participation in rehabilitation training. The system collectes human lower limb motion information through inertial measurement unit and predicts CoMH through artificial neural network to realize the tracking control of pelvic brace height. The proposed CoMH model was tested through rehabilitation training of hemiplegic patients. The results showed that the range of motion of the hip and knee joints on the affected side of the patient was improved by 25.0% and 31.4%, respectively, and the ratio of swing phase to support phase on the affected side was closer to that of the gait phase on the healthy side, as opposed to the traditional body weight support rehabilitation training model with fixed motion trajectory of pelvic brace. The motion trajectory of the pelvic brace in CoMH mode depends on the current state of the trainer so as to realize the walking training guided by active movement on the healthy side of hemiplegia patients. The strategy of dynamically adjustment of body weight support is more helpful to improve the efficiency of walking rehabilitation training.

Inter-Limb Asymmetry of Leg Stiffness in National Second-League Basketball Players during Countermovement Jumps

Assessment of the inter-limb asymmetry of leg stiffness is carried out using one-legged jumping tasks. However, the level of asymmetry may vary depending on the performance on one or both lower limbs. Therefore, the purpose of this study was to identify the differences in leg stiffness between the dominant and non-dominant lower limb during a two-legged countermovement jump. The research was conducted on 35 s-league basketball players (body height: 1.90 ± 0.08 m, body mass: 81.9 ± 10 kg, age: 19.5 ± 1.7 years). Each participant performed three countermovement jumps with arm swing to the maximum height. Measurements employed a BTS SMART motion analysis system and two Kistler force plates. Statistically significant differences were found during the comparison of leg stiffness in the dominant and non-dominant lower limbs. Inter-limb asymmetry of leg stiffness reached 22.0% in the countermovement phase and 8.9% in the take-off phase. Significant inter-limb asymmetry of leg stiffness might lead to injury or considerably reduce the performance of athletes. Therefore, an important role is to conduct strength and speed-strength trainings with proper loads to both body sides. Coaches should pay more attention to similar lower limbs movement patterns during two-legged exercises and bilateral strength development.

A Motion Capture Data-Driven Automatic Labanotation Generation Model Using the Convolutional Neural Network Algorithm

All human movements can be effectively represented with labanotation, which is simple to read and preserve. However, manually recording the labanotation takes a long time, so figuring out how to use the labanotation to accurately and quickly record and preserve traditional dance movements is a key research question. An automatic labanotation generation algorithm based on DL (deep learning) is proposed in this study. The BVH file is first analyzed, and the data are then converted. On this foundation, a CNN (convolutional neural network) algorithm for generating the dance spectrum of human lower-limb movements is proposed, which is very good at learning action space information. The algorithm performs admirably in terms of classification and recognition. Finally, a spatial segmentation-based automatic labanotation generation algorithm is proposed. To begin, every frame of data is converted into a symbol sequence using spatial law, resulting in a very dense motion sequence. The motion sequence is then regulated according to the minimum beat of motion obtained through wavelet analysis. To arrive at the final result, the classifier is used to determine whether each symbol is reserved or not. As a result, we will be able to create more accurate dance music for simple human movements.

A Lower Limb Rehabilitation Assistance Training Robot System Driven by an Innovative Pneumatic Artificial Muscle System.

This study aims to develop the application of pneumatic artificial muscle (PAM) for a 2-degrees of freedom (2-DOF) lower limb rehabilitation assistance training robot system. The proposed lower limb robot system can be divided into two axes, such as hip joint and knee joint. Each joint contains a pneumatic proportional valve to control a single-PAM with a torsion spring to simulate joint extension and flexion bionics characteristics and achieve a human-like 2-DOF lower limb robot system design and experimental prototype system. By analyzing the kinematics, the derived kinematics conforms to the lower limb motion pattern of the moving human body. Single PAM is difficult to achieve high accuracy control due to the different characteristics between extension and contraction. In our previous research, dual PAMs have been developed to drive a rotational joint which can achieve better control accuracy, however, cannot be suitable for multiaxial robotic design. The mechanism will become very complex and result in lower response and control accuracy. Thus, in this article the novel concept using single PAM with torsion spring was proposed to drive a joint to achieve two-axial robotic design. It has the advantage of multiaxial mechanism design, but the difficulty in joint control due to motion nonlinearity between contraction and extension. The torsion spring can improve motion nonlinearity between contraction and extension partly. Thus, the joint controller using adaptive self-organizing fuzzy sliding mode controller (ASOFSMC) was developed to solve this problem and achieve the required control performance for the joint angle positioning and gait planning control. Through the novel combination of single PAM, torsion spring, and the ASOFSMC joint controller with novel mechanism design and controller design, the two-axial robot mechanism designs and achieves the required control accuracy. The experimental results show that ASOFSMC can effectively control a 2-DOF lower limb robot system, and can modify fuzzy rules online, and adapt to rapid changes in the external environment and load to improve system control performance. The results prove that the proposed innovative single-PAM with a torsion spring and the control strategy can achieve the performance of 2-DOF lower limb rehabilitation assistance training robot system.