Comparison of knee biomechanical characteristics during gait between patients with knee osteoarthritis and healthy individuals.

Objective: This study aim to quantify the differences in knee biomechanics during gait between knee osteoarthritis (KOA) patients and healthy individuals. Methods: Twenty KOA patients (4 males and 16 females, 66.2 ± 7.7 years) and twenty controls (16 males and 4 females, 64.8 ± 5.4 years) were recruited for gait test using the motion capture system and force-platform system. The spatiotemporal parameters, knee kinematics and kinetics, and tibiofemoral contact force (TFCF) were calculated using an improved musculoskeletal model. Results: KOA patients walked with reduced speed (48.6 %), stride length (32.9 %), stride height (33.0 %), time proportions of single-support phases (19.2 %), increased gait cycle time (31.0 %), time proportions of stance (8.5 %) and double-support phases (57.7-75.9 %). KOA patients had significant smaller peak flexion angle (29.1 %), flexion ROM (50.6 %) and peak flexion moment (90.2 %), greater peak adduction moment (KAM) (40.7 %), peak rotation moments (KRM) (50.0 %), KAM impulse (106.2 %) and KRM impulse (126.0 %). In proximodistal direction, greater medial TFCF impulse (238 %), total and medial first-peak TFCF (9.6 % and 15.2 %), and smaller lateral peak TFCF (33.3 %) and TFCF impulse (38.4 %) were found in KOA patients. Besides, significant differences were found in the total, medial and lateral peak TFCFs and TFCF impulses in mediolateral direction, and the medial and lateral TFCFs and TFCF impulses in anteroposterior direction. Conclusions: Significant differences were found in the spatiotemporal parameters, knee kinematics and kinetics, and TFCF between the two groups. The results of this study have important implication for clinicians and rehabilitation physicians. These quantified biomechanical differences can provide data support for the personalized and quantified rehabilitation strategies, give suggestions for the exercises of KOA patients, help monitor disease, evaluate surgical treatment, and develop more effective preoperative planning and postoperative rehabilitation strategies.

Parallel Elastic Self-Alignment Mechanism Enhances Energy Efficiency and Reduces Misalignment in a Powered Knee Exoskeleton.

OBJECTIVE: This paper aims to enhance exoskeleton compliance during locomotion assistance by reducing misalignment and to improve energy efficiency by overcoming the limitations posed by the bulky structure of powered rigid exoskeletons. METHODS: A novel compliant knee exoskeleton, featuring a parallel elastic self-alignment mechanism, has been developed and structurally optimized. The exoskeleton uses adaptive oscillators to determine the wearer's gait phase and provides real-time assistance to the knee joint. RESULTS: Bench tests demonstrate that the parallel elastic mechanism significantly reduces the driving torque of the knee exoskeleton. Performance evaluations reveal that, compared to a commercial orthosis, the root-mean-square of knee angle error, joint misalignment, and unexpected interaction forces are reduced by 16.5 ± 11.3%, 23.3 ± 4.9%, and 17.7 ± 1.3%, respectively. Gait intervention experiments show reductions in average and maximum muscle activity of the knee joint by 7.6 ± 4.9% and 23.2 ± 5.7%, respectively. Additionally, the exoskeleton decreases negative work performed by the knee joint and the total lower limb by 22.7% and 8.6%, respectively. CONCLUSION: The parallel elastic self-alignment mechanism effectively mitigates joint misalignment, while the parallel springs offer partial gravity compensation, thereby enhancing both the energy efficiency and locomotion assistance of the exoskeleton. SIGNIFICANCE: The parallel elastic self-alignment mechanism effectively addresses both misalignment and energy efficiency challenges in powered exoskeletons, providing valuable insights for future design improvements.

Cross-domain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals.

Background and Objective: Exoskeleton robot control should ideally be based on human voluntary movement intention. The readiness potential (RP) component of the motion-related cortical potential is observed before movement in the electroencephalogram and can be used for intention prediction. However, its single-trial features are weak and highly variable, and existing methods cannot achieve high cross-temporal and cross-subject accuracies in practical online applications. Therefore, this work aimed to combine a deep convolutional neural network (CNN) framework with a transfer learning (TL) strategy to predict the lower limb voluntary movement intention, thereby improving the accuracy while enhancing the model generalization capability; this would also provide sufficient processing time for the response of the exoskeleton robotic system and help realize robot control based on the intention of the human body. Methods: The signal characteristics of the RP for lower limb movement were analyzed, and a parameter TL strategy based on CNN was proposed to predict the intention of voluntary lower limb movements. We recruited 10 subjects for offline and online experiments. Multivariate empirical-mode decomposition was used to remove the artifacts, and the moment of onset of voluntary movement was labeled using lower limb electromyography signals during network training. Results: The RP features can be observed from multiple data overlays before the onset of voluntary lower limb movements, and these features have long latency periods. The offline experimental results showed that the average movement intention prediction accuracy was 95.23% ± 1.25% for the right leg and 91.21% ± 1.48% for the left leg, which showed good cross-temporal and cross-subject generalization while greatly reducing the training time. Online movement intention prediction can predict results about 483.9 ± 11.9 ms before movement onset with an average accuracy of 82.75%. Conclusion: The proposed method has a higher prediction accuracy with a lower training time, has good generalization performance for cross-temporal and cross-subject aspects, and is well-prioritized in terms of the temporal responses; these features are expected to lay the foundation for further investigations on exoskeleton robot control.

Magnetorheological Damper With Variable Displacement Permanent Magnet for Assisting the Transfer of Load in Lower Limb Exoskeleton.

Magnetorheological (MR) fluid exhibits the ability to modulate its shear state through variations in magnetic field intensity, and is widely used for applications requiring damping. Traditional MR dampers use the current in the coil to adjust the magnetic field strength, but the accumulated heat can cause the magnetic field strength to decay if it works for a long time. In order to deal with this shortcoming, a novel MR damper is proposed in this paper, which is based on a variable displacement permanent magnet to adjust the output resistance torque and applied to an exoskeleton joint for human load transfer assistance. A finite element model is used to determine the size parameters of the magnet and separator, so that the maximum output torque is optimal and the torque is uniformly distributed with the magnet displacement. The MR damper was characterized and calibrated on the experimental bench to make it controllable. The novel design enables the torque mass density of the MR damper to reach 8.83Nmm/g, the torque volume density to reach 48.7N/mm2, and has stability for long-term operation. Based on the torque control method proposed, a preliminary human experiment is conducted. The ground reaction force (GRF) data of the subjects is analyzed here, which represents the effect of load transfer to the exoskeleton. Compared with no exoskeleton, the GRF with exoskeleton is significantly reduced: the peak GRF in early stance phase is reduced by 24.14%, and in late stance phase is reduced by 19.77%. Based on our net load benefit (NLB) and net force benefit (NFB) evaluation indicators, the effectiveness of the proposed MR damper exoskeleton for human weight bearing assistance is established.

Efficient Removal of Antimony(V) from Antimony Mine Wastewater by Micrometer Zero-Valent Iron.

This paper investigates the effectiveness of two commercial micron zero-valent irons (mZVIs) in removing Sb(V) from antimony mine wastewater. The wastewater contains a range of complex components and heavy metal ions, including As(V), which can impact the removal efficiency of mZVI. The study aims to provide insights into actual working conditions and focuses on influencing factors and standard conditions. The results demonstrate that mZVI can reduce Sb(V) concentration in the mine wastewater from 3875.7 µg/L to below the drinking water standard of 5 µg/L within 2 h. Adding a small amount of mZVI every 30 min helps to maintain a high removal rate. The study confirms the existence of a reduction reaction by changing the atmospheric conditions of the reaction, and the addition of 1,10-phenanthroline highlights the important role of active Fe(II) in the adsorption and removal of Sb(V) by mZVI. Additionally, the paper presents an innovative experimental method of acid treatment followed by alkali treatment, which proves the interfacial reaction between mZVI and Sb(V). Overall, the study demonstrates that the removal of Sb(V) by mZVI entails a dual function of reduction and adsorption, highlighting the potential of mZVI in repairing Sb(V) in antimony mine wastewater.

Effect and mechanism of kaolinite loading amorphous zero-valent iron to stabilize cadmium in soil.

Amorphousness effectively improves the electron transfer rate of zero-valent iron. In this study, a novel kaolinite loading amorphous zero-valent iron composite (K-AZVI) was prepared and applied to the remediation of soils with cadmium (Cd) pollution concentrations of 20, 50, and 100 mg/kg respectively. The results showed that the application of K-AZVI increased the pH and cation exchange capacity (CEC) of soil, and decreased the dissolved organic carbon (DOC) and organic matter (OM) of soil, thus indirectly promoting the adsorption of Cd in the soil. After 28 days of stabilization, the stabilizing efficiency of K-AZVI on the water-soluble Cd content in soil reached 98.72 %. Under the amendment of 0.25 %-1.0 % (w/w), the available Cd content in 20-100 mg/kg contaminated soil decreased by 46.47 %-62.23 %, 24.10 %-41.52 %, and 16.09 %-30.51 % respectively compared with CK. More importantly, the addition of K-AZVI promoted the transformation of 33.18 %-48.42 % exchangeable fraction (EXC) to 10.09 %-20.14 % residual fraction (RES), which increased the abundance and diversity of soil bacterial communities. Comprehensive risk assessment showed that adding 1.0 % K-AZVI provided the best remediation on contaminated soil. In addition, the results of scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) of K-AZVI before and after the reaction showed that the stabilization mechanism of K-AZVI to Cd in soil is mainly the stable metal species (Cd(OH)2, CdO and CdFe2O4) formed by the direct complexation and coprecipitation of a large number of iron oxides formed by the rapid corrosion of amorphous zero-valent iron (AZVI). Overall, this work provides a promising approach to the remediation of Cd-contaminated soil using K-AZVI composites.

Physicochemical Effects of Sulfur Precursors on Sulfidated Amorphous Zero-Valent Iron and Its Enhanced Mechanisms for Cr(VI) Removal.

Amorphous zerovalent iron (AZVI) has gained considerable attention due to its remarkable reactivity, but there is limited research on sulfidated amorphous zerovalent iron (SAZVI) and the influence of different sulfur precursors on its reactivity remains unclear. In this study, SAZVI materials with an amorphous structure were synthesized using various sulfur precursors, resulting in significantly increased specific surface area and hydrophobicity compared to AZVI. The Cr(VI) removal efficiency of SAZVI-Na2S, which exhibited the most negative free corrosion potential (-0.82 V) and strongest electron transfer ability, was up to 8.5 times higher than that of AZVI. Correlation analysis revealed that the water contact angle (r = 0.87), free corrosion potential (r = -0.92), and surface Fe(II) proportion (r = 0.98) of the SAZVI samples played crucial roles in Cr(VI) removal. Furthermore, the enhanced elimination ability of SAZVI-Na2S was analyzed, primarily attributed to the adsorption of Cr(VI) by the FeSx shell, followed by the rapid release of internal electrons to reduce Cr(VI) to Cr(III). This process ultimately led to the precipitation of FeCr2O4 and Cr2S3 on the surface of SAZVI-Na2S, resulting in their removal from the water. This study provides insights into the influence of sulfur precursors on the reactivity of SAZVI and offers a new strategy for designing highly active AZVI for efficient Cr(VI) removal.

Fabricating Amorphous Zero-Valent Iron for Cr(VI) Efficient Reduction: Unveiling the Effect of Ethylenediamine on Physicochemical Properties.

Amorphous zero-valent iron (AZVI) has attracted wide attention due to its high-efficiency reduction ability. However, the effect of different EDA/Fe(II) molar ratios on the physicochemical properties of the synthesized AZVI requires further investigation. Herein, series of AZVI samples were prepared by changing the molar ratio of EDA/Fe(II) to 1/1 (AZVI@1), 2/1 (AZVI@2), 3/1 (AZVI@3), and 4/1 (AZVI@4). When the EDA/Fe(II) ratio increased from 0/1 to 3/1, the Fe0 proportion on the AZVI surface increased from 26.0 to 35.2% and the reducing ability was enhanced. As for AZVI@4, the surface was severely oxidized to form a large amount of Fe3O4, and the Fe0 content was only 74.0%. Moreover, the removal ability of Cr(VI) was in the order AZVI@3 > AZVI@2 > AZVI@1 > AZVI@4. The isothermal titration calorimetry results revealed that the increase of the molar ratio of EDA/Fe(II) would lead to the stronger complexation of EDA with Fe(II), which resulted in the gradual decrease of the yield of AZVI@1 to AZVI@4 and the gradual deterioration of water pollution after the synthesis. Therefore, based on the evaluation of all indicators, AZVI@2 was the optimal material, not only because its yield was as high as 88.7% and the secondary water pollution level was low, but most importantly, the removal efficiency of Cr(VI) by AZVI@2 was excellent. Furthermore, the actual Cr(VI) wastewater with the concentration of 14.80 mg/L was treated with AZVI@2, and the removal rate of 97.0% was achieved after only a 30 min reaction. This work clarified the effect of different ratios of EDA/Fe(II) on the physicochemical properties of AZVI, which provided insights for guiding the reasonable synthesis of AZVI and is also conducive to investigating the reaction mechanism of AZVI in Cr(VI) remediation.

EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study.

Introduction: Active rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinical exercise therapy. However, its effect on electroencephalogram (EEG) signals is not yet known. Therefore, the objective of this paper is to construct a model of the EEG signal generation mechanism of lower limb active movement intention and then investigate whether VR induction could improve movement intention detection based on EEG. Methods: Firstly, a neural dynamic model of lower limb active movement intention generation was established from the perspective of signal transmission and information processing. Secondly, the movement-related EEG signal was calculated based on the model, and the effect of VR induction was simulated. Movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted to analyze the enhancement of movement intention. Finally, we recorded EEG signals of 12 subjects in normal and VR environments to verify the effectiveness and feasibility of the above model and VR induction enhancement of lower limb active movement intention for individuals with dyskinesia. Results: Simulation and experimental results show that VR induction can effectively enhance the EEG features of subjects and improve the detectability of movement intention. Discussion: The proposed model can simulate the EEG signal of lower limb active movement intention, and VR induction can enhance the early and accurate detectability of lower limb active movement intention. It lays the foundation for further robot control based on the actual needs of users.

Design and Experimental Evaluation of a Lower-Limb Exoskeleton for Assisting Workers With Motorized Tuning of Squat Heights.

This paper presents the E-LEG, a novel semi-passive lower-limb exoskeleton for worker squatting assistance, with motorized tuning of the assistive squatting height. Compared with other passive industrial exoskeletons for the lower-limbs, the E-LEG presents novel design features namely inertial sensor for measuring the tilt angle of thigh and the novel electromagnetic switch for adjusting squat height. These features could enhance the effectiveness of the system. In addition to the introduction to exoskeleton design, this paper also reports the systematic experimental evaluation of human subjects. With the assistance of different conditions, the variability of muscular activity was evaluated in long-term static squatting task. The set of metrics to evaluate the effect of the device included leg muscle activity, plantar pressure fluctuation, plantar pressure center fluctuation and gait angles. Results show that the exoskeleton can reduce the muscular activity of the user during squatting, and it will have little affect the normal gait of the user during walking. In this study, we found that the E-LEG exoskeleton has potential effectiveness in reducing the muscular strain on long-term continuous squatting activities.

A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton.

Gait phase classification is important for rehabilitation training in patients with lower extremity motor dysfunction. Classification accuracy of the gait phase also directly affects the effect and rehabilitation training cycle. In this article, a multiple information (multi-information) fusion method for gait phase classification in lower limb rehabilitation exoskeleton is proposed to improve the classification accuracy. The advantage of this method is that a multi-information acquisition system is constructed, and a variety of information directly related to gait movement is synchronously collected. Multi-information includes the surface electromyography (sEMG) signals of the human lower limb during the gait movement, the angle information of the knee joints, and the plantar pressure information. The acquired multi-information is processed and input into a modified convolutional neural network (CNN) model to classify the gait phase. The experiment of gait phase classification with multi-information is carried out under different speed conditions, and the experiment is analyzed to obtain higher accuracy. At the same time, the gait phase classification results of multi-information and single information are compared. The experimental results verify the effectiveness of the multi-information fusion method. In addition, the delay time of each sensor and model classification time is measured, which shows that the system has tremendous real-time performance.

The Effects of a Passive Exoskeleton on Human Thermal Responses in Temperate and Cold Environments.

The exoskeleton as functional wearable equipment has been increasingly used in working environments. However, the effects of wearing an exoskeleton on human thermal responses are still unknown. In this study, 10 male package handlers were exposed to 10 °C (COLD) and 25 °C (TEMP) ambient temperatures while performing a 10 kg lifting task (LIFTING) and sedentary (REST) both with (EXO) and without the exoskeleton (WEXO). Thermal responses, including the metabolic rate and mean skin temperature (MST), were continuously measured. Thermal comfort, thermal sensation and sweat feeling were also recorded. For LIFTING, metabolic heat production is significant decrease with the exoskeleton support. The MST and thermal sensation significantly increase when wearing the exoskeleton, but thermal discomfort and sweating are only aggravated in TEMP. For REST, MST and thermal sensation are also increased by the exoskeleton, and there is no significant difference in the metabolic rate between EXO and WEXO. The thermal comfort is significantly improved by wearing the exoskeleton only in COLD. The results suggest that the passive exoskeleton increases the local clothing insulation, and the way of wearing reduces the "pumping effect", which makes a difference in the thermal response between COLD and TEMP. Designers need to develop appropriate usage strategies according to the operative temperature.

Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion.

Aiming at the requirement of rapid recognition of the wearer's gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multi-source feature parameters. We obtain information on human lower extremity acceleration and plantar analyze the relationship between these parameters and gait cycle studying the motion state recognition method based on feature evaluation and neural network. Based on the actual requirements of exoskeleton per use, 15 common gait patterns were determined. Using this, the studies were carried out on the time domain, frequency domain, and energy feature extraction of multi-source lower extremity motion information. The distance-based feature screening method was used to extract the optimal features. Finally, based on the multi-layer BP (back propagation) neural network, a nonlinear mapping model between feature quantity and motion state was established. The experimental results showed that the recognition accuracy in single motion mode can reach up to 98.28%, while the recognition accuracy of the two groups of experiments in mixed motion mode was found to be 92.7% and 97.4%, respectively. The feasibility and effectiveness of the model were verified.