Lower Limb Movement Research Articles

Sex differences in factors of pelvis and lower-limb joint kinematics influencing dynamic knee valgus during single-leg squat.

Dynamic knee valgus (DKV) during dynamic activity is associated with patellofemoral pain, one of the most common sports disorders, especially in females. No studies have examined the factors that can lead to DKV in females as compared to males while addressing the influence of factors on each other. To identify factors of pelvis and lower joints kinematics affecting DKV during single-leg squat (SLS) in females compared to males with multiple regression analysis. Forty-four healthy young people (24 females, 20 males) performed SLS, and pelvis, hip, knee, and ankle 3D kinematics were recorded. The 2D knee frontal plane projection angle (FPPA) was adopted as the DKV measurement. Multiple regression analysis was performed to determine the relationship between FPPA and the kinematics of each joint and segment. Hip adduction and pelvic contralateral rotation were explanatory factors for FPPA in females. Hip adduction, hip internal rotation, and knee external rotation were explanatory factors in males. The lower limb or pelvic motion factors affecting DKV during SLS differ between the sexes, with pelvic contralateral rotation being extracted only in women, possibly due to differences in pelvic width.

Executive Functions Assessment Based on Wireless EEG and 3D Gait Analysis During Dual-Task: A Feasibility Study.

Executive functions (EFs) are neurocognitive processes planning and regulating daily life actions. Performance of two simultaneous tasks, requiring the same cognitive resources, lead to a cognitive fatigue. Several studies investigated cognitive-motor task and the interference during walking, highlighting an increasing risk of falls especially in elderly and people with neurological diseases. A few studies instrumentally explored relationship between activation-no-activation of two EFs (working memory and inhibition) and spatial-temporal gait parameters. Aim of our study was to detect activation of inhibition and working memory during progressive difficulty levels of cognitive tasks and spontaneous walking using, respectively, wireless electroencephalography (EEG) and 3D-gait analysis. Thirteen healthy subjects were recruited. Two cognitive tasks were performed, activating inhibition (Go-NoGo) and working memory (N-back). EEG features (absolute and relative power in different bands) and kinematic parameters (7 spatial-temporal ones and Gait Variable Score for 9 range of motion of lower limbs) were analyzed. A significant decrease of stride length and an increase of external-rotation of foot progression were found during dual task with Go-NoGo. Moreover, a significant correlation was found between the relative power in the delta band at channels Fz, C4 and progressive difficulty levels of Go-NoGo (activating inhibition) during walking, whereas working memory showed no correlation. This study reinforces the hypothesis of the prevalent involvement of inhibition with respect to working memory during dual task walking and reveals specific kinematic adaptations. The foundations for EEG-based monitoring of cognitive processes involved in gait are laid. Clinical and Translational Impact Statement: Clinical and instrumental evaluation and training of executive functions (as inhibition), during cognitive-motor task, could be useful for rehabilitation treatment of gait disorder in elderly and people with neurological disease.

Evaluation Model of Athletes’ Lower Extremity Training Ability Based on LSTM Algorithm

To achieve intelligent evaluation of the lower limb movement ability of athletes with sports disabilities, this article selects young athletes and middle-aged and young athletes with sports disabilities as the research objects, and healthy young athletes as the control group. Gait videos, GRF and knee angles of the subjects were collected to extract and analyse gait contours and features. The improved Visual Background extractor (ViBe) algorithm has the highest accuracy of 0.978 in PETS2006 video sequences; the recall rate of three algorithms in Highway video sequence is the highest, and the recall rate of improved ViBe algorithm is the highest, up to 0.965. AtƐ=0.7, the accuracy of the improved myloss training set is higher than that corresponding to other values; when the number of iterations is 98, the accuracy rate of improved myloss training set is 0.963, while the accuracy rate corresponding to the cross entropy loss function is 0.945. When the number of iterations is 151, the accuracy rate of the Xeption LSTM model is 0.956, higher than that of other models. Among corresponding mean ± standard deviation (GSA-MS) indicators, the GSA-MS values of Group L are significantly higher than those of group N and group Z (P<0.001). The correlation between GAS indicators and Gait Abnormality Rating Scale (GARS-M) is strong, with a correlation coefficient of 0.90.

Research on Lower Limb Movement Rehabilitation Assessment based on Graph Convolutional Network

Research on Lower Limb Movement Rehabilitation Assessment based on Graph Convolutional Network

Selective orthotic constraint of lower limb movement during walking reveals new insights into neuromuscular adaptation.

A concern expressed by the clinical community is that the constraint of motion provided by an ankle foot orthosis (AFO) may lead the user to become dependent on its stiffness, leading to learned non-use. To examine this, we hypothesized that using an experimental AFO-footwear combination (exAFO-FC) that constrains ankle motion during walking would result in reduced soleus and tibialis anterior EMG compared to free (exAFO-FC) and control (no AFO, footwear only) conditions. A total of 14 healthy subjects walked at their preferred speed (1.34 ± 0.09 m·s-1) for 15 min, in three conditions, namely, control, free, and stop. During the stance phase of walking in the stop condition, ipsilateral soleus integrated EMG (iEMG) declined linearly, culminating in a 32.1% reduction compared to the control condition in the final 5 min interval of the protocol. In contrast, ipsilateral tibialis anterior iEMG declined in a variable fashion culminating in an 11.2% reduction compared to control in the final 5 min interval. During the swing phase, the tibialis anterior iEMG increased by 6.6% compared to the control condition during the final 5 min interval. The contralateral soleus and tibialis anterior exhibited increased iEMG in the stop condition. An AFO-FC functions as a biomechanical motion control device that influences the neural control system and alters the output of muscles experiencing constraints of motion.

Variational Mode Decomposition and Empirical Wavelet Transform-Based Feature Extraction and Ensemble Classifier for Lower Limb Movement Prediction With Surface Electromyography Signal

Variational Mode Decomposition and Empirical Wavelet Transform-Based Feature Extraction and Ensemble Classifier for Lower Limb Movement Prediction With Surface Electromyography Signal

A Potential-Real-Time Thigh Orientation Prediction Method Based on Two Shanks-Mounted IMUs and Its Clinical Application

The detection and evaluation of gait kinematics is vital for patient diagnosis and rehabilitation. Aiming at limitations of commonly used optical capture and wearable sensing systems in clinical applications, this paper proposes a thigh attitude angle prediction method based on the hip error tolerance from the kinematic data of two shank-mounted IMUs (Inertial Measurement Unit). The novelties of the proposed method are summarized as follows: i) It develops a parallel approach to regress variation of hip error tolerance for different subjects. This parallel approach, by simultaneously deconstructing the shank kinematics data via different regression algorithm including support vector machine, boosting tree, and stepwise linearity regression, is able to well accommodate the characteristics of both health subjects and patients. ii) It develops some evaluation indices based on gait symmetry, consistency, and activity to fully evaluate the human lower limbs motion performance in gait by only two IMUs. The effectiveness of the proposed method is verified by the experimental results among 8 healthy subjects and 16 cerebral infarction patients. For the healthy subjects, the estimated error of thigh prediction compared with Xsens and Vicon are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.3 \pm {0.3^ \circ }$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.5 \pm {0.7^ \circ }$</tex-math> </inline-formula> , respectively. For the patients, the estimated error compared with Xsens-measured angle is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.8 \pm {1.7^ \circ }$</tex-math> </inline-formula> . Its broad significance in actual intelligent healthcare and robotics-assisted rehabilitation is three-fold: First, it is a recursive real-time method as it only needs data from the previous gait cycle to predict the thigh angle. Second, its accuracy meets the actual clinical needs. Third, it is a low-cost method that only needs two IMUs and has high potentials of clinical applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Gait is of great significance to quantify the degree of movement disorders in clinical practice, and the existing equipment is rarely able to meet the needs of full dimension, low cost, and low place constraints in clinical practice. This paper presents an innovative gait kinematic prediction method, which only uses two IMUs attached to the shanks to predict the orientation of thigh. Compared with related works, the proposed methods have achieved high-precision, real-time and low-cost acquisition. This paper is inspired by the problems of large number, high motion interference, and high cost of wearable gait measurement devices in clinical practice. The proposed method have potential to be integrated into exoskeletons and medical walking AIDS, which could greatly improve the comfort and control precision of the wearable system.

Deep Learning for Electromyographic Lower-Limb Motion Signal Classification Using Residual Learning.

Electromyographic (EMG) signals have gained popularity for controlling prostheses and exoskeletons, particularly in the field of upper limbs for stroke patients. However, there is a lack of research in the lower limb area, and standardized open-source datasets of lower limb EMG signals, especially recording data of Asian race features, are scarce. Additionally, deep learning algorithms are rarely used for human motion intention recognition based on EMG, especially in the lower limb area. In response to these gaps, we present an open-source benchmark dataset of lower limb EMG with Asian race characteristics and large data volume, the JJ dataset, which includes approximately 13,350 clean EMG segments of 10 gait phases from 15 people. This is the first dataset of its kind to include the nine main muscles of human gait when walking. We used the processed time-domain signal as input and adjusted ResNet-18 as the classification tool. Our research explores and compares multiple key issues in this area, including the comparison of sliding time window method and other preprocessing methods, comparison of time-domain and frequency-domain signal processing effects, cross-subject motion recognition accuracy, and the possibility of using thigh and calf muscles in amputees. Our experiments demonstrate that the adjusted ResNet can achieve significant classification accuracy, with an average accuracy rate of 95.34% for human gait phases. Our research provides a valuable resource for future studies in this area and demonstrates the potential for ResNet as a robust and effective method for lower limb human motion intention pattern recognition.

Current Technical Study on Functional Electrical Simulation for Stroke Patients

Stroke is a disease caused by sudden rupture or blockage of cerebral blood vessels, resulting in damage to brain tissue. And it has become a high fatality rate disease. Safe and effective treatments are urgently needed. FES can improve upper and lower limb movement and increase mobility in stroke patients, which is an effective physical therapy to stimulate the recovery of muscle activity. FES releases low-frequency pulses through surface or subcutaneous electrodes to stimulate the contraction of the target muscle to generate movement and reestablish the connection of motor pathways. FES treatment alone is no longer sufficient. Based on 8 studies in the last 5 years, the therapeutic effects of transcranial direct current stimulation and VR combined with functional electrical simulation were reviewed. Both combinations have achieved quite satisfactory therapeutic effects, but the therapeutic effect of VR still needs further research, and more combinations of treatment programs are more worthy of expectation.

Design, control and evaluation of a treadmill-based Pelvic Exoskeleton (PeXo) with self-paced walking mode

Most gait rehabilitation exoskeletons focus only on assisting lower limb motions. However, the pelvis plays an essential role in overground ambulation. This calls for the need of gait training devices that allow full control and assistance of user’s pelvis to improve gait rehabilitation therapies of stroke survivors. This paper presents a new pelvic assistance treadmill-based exoskeleton (PeXo) for gait rehabilitation. The system includes a total of 5 actively controlled Degrees of Freedom (DOFs), plus a passive one, in order to provide all DOFs of the human pelvis. The paper describes the mechatronic design and haptic control of PeXo, and introduces a newly developed self-paced walking mode where the user can control walking speed at his/her will. The system was evaluated by means of a pilot study with a stroke survivor. Results showed PeXo can be used safely and reliably during walking activities, allowing for the natural motion of the pelvis with reduced interaction forces. However, the use of the platform reduced user’s Range of Motion at the pelvis and lower limb joints, whereas lower limb’s muscle activity increased to compensate the disturbances introduced by the platform. Nevertheless, the user reported a positive feedback when using the system, suggesting potential and promising advantages of PeXo that should be explored in a larger study in the future.

Interpretable Dual-branch EMGNet: A transfer learning-based network for inter-subject lower limb motion intention recognition

Currently, the fusion of surface Electromyography (EMG) and deep learning is gradually showing immense potential in the research of Lower Limb Motion Intention Recognition (LLMIR). Nevertheless, most deep learning algorithms have poor interpretability without special design or the help of other post-hoc analysis tools, as well as unsatisfactory performance in cross-subject prediction. Hence, this paper presents a novel Interpretable Dual-Branch EMG Network (IDB-EMGNet), in which one branch is dedicated to lower limb motion recognition, and the other is able to predict knee joint angles in advance. The shallow feature extraction module of IDB-EMGNet is constructed using an ante-hoc interpretable SincNet technique, which enables the detection of the spectral range of EMG used for the LLMIR task. An improved bottleneck block with shuffle attention is designed for deep feature extraction, which enhances model performance with only a little increase in complexity. The performance of IDB-EMGNet in both intra-subject and inter-subject scenarios is investigated, where the latter integrates the transfer learning technique. Specifically, by conducting model pre-training on source-domain subjects and transferring the learned knowledge to target-domain subjects, satisfactory performance can be achieved even with less computing resource. Experimental results on two publicly available datasets indicate that the proposed approach exhibits superior applicability to both normal subjects and knee-pathology patients, showing a promising prospect in the controller design of human-robot collaborative exoskeletons.

Relationship between ankle–foot-complex mobility during static loading and frontal moment impulses of knee and hip joints during the stance phase

BackgroundAnkle–foot-complex mobility impairments, which can be assessed by the difference between the sitting and standing positions, are related to an increase in the load on the knee and hip joints during the stance phase of the gait. Research questionWhat is the relationship between the ankle–foot-complex mobility during static weight bearing and the mechanical stresses on the knee and hip joints throughout the stance phase? MethodsAnkle–foot-complex mobility and gait data were collected from 26 healthy adults. The complex mobility was established by comparing the foot indices, measured using a three-dimensional foot scanner, in sitting and standing positions. The gait data were acquired using eight cameras (recording at 100 Hz) and three force plates (recording at 1000 Hz). Stance phase data were collected via ground reaction forces. The stance phase was dissected into shock absorption and propulsion phases, during which the external knee and hip adduction moment impulses (KAMi, HAMi) were recorded. The correlation between the ankle–foot-complex mobility during static weight bearing and KAMi and HAMi during the stance phase was examined using Pearson's product–moment correlation coefficients. ResultsThis study revealed that KAMi correlated with medial malleolus mobility (r = −0.44) throughout the stance phase. Furthermore, in the propulsive phase, KAMi correlated with calcaneus (r = 0.51) and navicular (r = −0.50) mobilities, whereas HAMi correlated with calcaneus mobility (r = −0.40). SignificanceThe study provides insights into the relationship between the static mobility of the ankle–foot complex in healthy individuals and mechanical stress during the stance phase. Calcaneus and navicular mobilities were related to efficient push-off in the propulsive phase. Medial malleolus mobility was related to the control of the lateral tilt of the lower limb and ankle dorsiflexion motion throughout the stance phase.

A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities

Tasks of daily living are often sporadic, highly variable, and asymmetric. Analyzing these real-world non-cyclic activities is integral for expanding the applicability of exoskeletons, protheses, wearable sensing, and activity classification to real life, and could provide new insights into human biomechanics. Yet, currently available biomechanics datasets focus on either highly consistent, continuous, and symmetric activities, such as walking and running, or only a single specific non-cyclic task. To capture a more holistic picture of lower limb movements in everyday life, we collected data from 12 participants performing 20 non-cyclic activities (e.g. sit-to-stand, jumping, squatting, lunging, cutting) as well as 11 cyclic activities (e.g. walking, running) while kinematics (motion capture and IMUs), kinetics (force plates), and electromyography (EMG) were collected. This dataset provides normative biomechanics for a highly diverse range of activities and common tasks from a consistent set of participants and sensors.

Human Lower Limb Motion Intention Recognition for Exoskeletons: A Review

Human Lower Limb Motion Intention Recognition for Exoskeletons: A Review

Attempted Suicide with a Self-Inflicted Penetrating Stab Wound to the Medulla Oblongata Caused by a Pair of Scissors: Case Illustration

A 78-year-old male patient with a history of psychotic depression was found in the bathroom of his apartment with a pair of scissors driven deeply into the back of his neck. He presented with tetraparesis with residual control over the left lower limb movement. Immediate skull radiograph and brain computed tomography scans revealed the tip of the scissors passing into the foramen magnum. Emergent surgery with midline suboccipital craniectomy and resection of the posterior arch of C1 was performed. The scissors were spontaneously dislocated in the course of surgery. Thereafter, debridement, placement of external ventricular drain and primary closure of the dura and skin were achieved. Post-operatively, the patient was not able to follow any instructions. On the 22nd post-operative day, the patient passed away in palliation. To our knowledge, this is the first reported case of a suicide attempt involving the use of scissors reaching the medulla oblongata.

High-level control architecture of lower limb exoskeleton: A review

As a rehabilitation robot for aiding in the movement of lower limbs, the lower limb exoskeleton is a beneficial device. In order to make the most effective use of the exoskeleton, the control strategy plays a crucial role. This review paper provides a background and classification of lower limb exoskeleton control strategies, such as model-based and hierarchy-based control. Further, we presented mainly the high-level control architecture of lower limb exoskeletons, which is aimed at detecting the intention of human movement. An in-depth discussion is provided in this paper regarding manual user input (MUI), surface electromyography (sEMG), and brain-computer interface (BCI). Many people need exoskeletons, which is why this review was written. Exoskeletons, however, are expensive and cannot be mass-produced, and their control methods are immature, making them ineffective. Thus, the objective of this review is to identify research gaps and common limitations in previous research to obtain future directions for improving the usability of the control mechanism. In an alternative approach, MUI and BCI are combined to reduce the time spent switching movement modes and the amount of concentration required to do so.

Feasibility of using fNIRS to explore motor-related regional haemodynamic signal changes in patients with sensorimotor impairment and healthy controls: A pilot study.

While functional near-infrared spectroscopy (fNIRS) can provide insight into cortical brain activity during motor tasks in healthy and diseased populations, the feasibility of using fNIRS to assess haemoglobin-evoked responses to reanimated upper limb motor function in patients with tetraplegia remains unknown. The primary objective of this pilot study is to determine the feasibility of using fNIRS to assess cortical signal intensity changes during upper limb motor tasks in individuals with surgically restored grip functions. The secondary objectives are: 1) to collect pilot data on individuals with tetraplegia to determine any trends in the cortical signal intensity changes as measured by fNIRS and 2) to compare cortical signal intensity changes in affected individuals versus age-appropriate healthy volunteers. Specifically, patients presented with tetraplegia, a type of paralysis resulting from a cervical spinal cord injury causing loss of movement and sensation in both lower and upper limbs. All patients have their grip functions restored by surgical tendon transfer, a procedure which constitutes a unique, focused stimulus for brain plasticity. fNIRS is used to assess changes in cortical signal intensity during the performance of two motor tasks (isometric elbow and thumb flexion). Six individuals with tetraplegia and six healthy controls participate in the study. A block paradigm is utilized to assess contralateral and ipsilateral haemodynamic responses in the premotor cortex (PMC) and primary motor cortex (M1). We assess the amplitude of the optical signal and spatial features during the paradigms. The accuracy of channel locations is maximized through 3D digitizations of channel locations and co-registering these locations to template atlas brains. A general linear model approach, with short-separation regression, is used to extract haemodynamic response functions at the individual and group levels. Peak oxyhaemoglobin (oxy-Hb) changes in PMC appear to be particularly bilateral in nature in the tetraplegia group during both pinch and elbow trials whereas for controls, a bilateral PMC response is not especially evident. In M1 / primary sensory cortex (S1), the oxy-Hb responses to the pinch task are mainly contralateral in both groups, while for the elbow flexion task, lateralization is not particularly clear. This pilot study shows that the experimental setup is feasible for assessing brain activation using fNIRS during volitional upper limb motor tasks in individuals with surgically restored grip functions. Cortical signal changes in brain regions associated with upper extremity sensorimotor processing appear to be larger and more bilateral in nature in the tetraplegia group than in the control group. The bilateral hemispheric response in the tetraplegia group may reflect a signature of adaptive brain plasticity mechanisms. Larger studies than this one are needed to confirm these findings and draw reliable conclusions.

STGNN-LMR: A Spatial–Temporal Graph Neural Network Approach Based on sEMG Lower Limb Motion Recognition

STGNN-LMR: A Spatial–Temporal Graph Neural Network Approach Based on sEMG Lower Limb Motion Recognition

Electrospinography for non-invasively recording spinal sensorimotor networks in humans

Objective. Currently, few non-invasive measures exist for directly measuring spinal sensorimotor networks. Electrospinography (ESG) is one non-invasive method but is primarily used to measure evoked responses or for monitoring the spinal cord during surgery. Our objectives were to evaluate the feasibility of ESG to measure spinal sensorimotor networks by determining spatiotemporal and functional connectivity changes during single-joint movements at the spinal and cortical levels. Approach. We synchronously recorded electroencephalography (EEG), electromyography, and ESG in ten neurologically intact adults while performing one of three lower-limb tasks (no movement, plantar-flexion and knee flexion) in the prone position. A multi-pronged approach was applied for removing artifacts using filtering, artifact subspace reconstruction and independent component (IC) analysis. Next, data were segmented by task and ICs of EEG were clustered across participants. Within-participant analysis of ICs and ESG data was conducted, and ESG was characterized in the time and frequency domains. Generalized partial directed coherence analysis was performed within ICs and between ICs and ESG data by participant and task. Results. K-means clustering resulted in five clusters of ICs at Brodmann areas (BAs) 9, BA 8, BA 39, BA 4, and BA 22. Areas associated with motor planning, working memory, visual processing, movement, and attention, respectively. Time-frequency analysis of ESG data found localized changes during movement execution when compared to no movement. Lastly, we found bi-directional changes in functional connectivity (p < 0.05, adjusted for multiple comparisons) within IC’s and between IC’s and ESG sensors during movement when compared to the no movement condition. Significance. To our knowledge this is the first report using high density ESG for characterizing single joint lower limb movements. Our findings provide support that ESG contains information about efferent and afferent signaling in neurologically intact adults and suggests that we can utilize ESG to directly study the spinal cord.

Development of a three-dimensional muscle-driven lower limb model developed using an improved CFD-FE method

Analysis of the musculoskeletal movements (gait analysis) is needed in many scenarios. The in vivo method has some difficulties. For example, recruiting human subjects for the gait analysis is challenging due to many issues. In addition, when plenty of subjects are required, the follow-up experiments take a long period and the dropout of subjects always occurs. An efficient and reliable in silico simulation platform for gait analysis has been desired for a long time. Therefore, a technique using three-dimensional (3D) muscle modeling to drive the 3D musculoskeletal model was developed and the application of the technique in the simulation of lower limb movements was demonstrated. A finite element model of the lower limb with anatomically high fidelity was developed from the MRI data, where the main muscles, the bones, the subcutaneous tissues, and the skin were reconstructed. To simulate the active behavior of 3D muscles, an active, fiber-reinforced hyperelastic muscle model was developed using the user-defined material (VUMAT) model. Two typical movements, that is, hip abduction and knee lifting, were simulated by activating the responsible muscles. The results show that it is reasonable to use the improved CFD-FE method proposed in the present study to simulate the active contraction of the muscle, and it is feasible to simulate the movements by activating the relevant muscles. The results from the present technique closely match the physiological scenario and thus the technique developed has a great potential to be used in the in silico human simulation platform for many purposes.