A novel inexpensive modified calcium oxide (MCA) was prepared using commercial calcium oxide (CCO) and n-butanol and applied to curing phosphogypsum (PG) waste. With the MCA, the soluble phosphorus (SP), soluble fluoride (SF) and pH of PG changed from 230.6 mg/L, 180.7 mg/L and 2.89 drastically to 0.46 mg/L, 8.3 mg/L and 8.58 after 3 days, meeting the GB8978-1996 (SP <= 0.5 mg/L, SF <= 10 mg/L, pH = 6 similar to 9), remaining stable even after 90 days. Our mechanism study revealed that the MCA reacted with SP to form amorphous CaHPO4 which further tranformed to Ca-5(PO4)(3)OH. Ca-5(PO4)(3)OH combined with SF to produce Ca-5(PO4)(3)F (K-sp=2.1 x 10(-59)). Compared to CaF2 and CaHPO4, the K-sp of Ca-5(PO4)(3)F decreased markedly by approximately 10(48) and 10(52) times. More importantly, in this work MCA has been industrially applied to cure 790,000-tons PG. Moritoring for >500 days showed that the SP, SF and pH values met the GB8976-1996 standard continuously. This study demonstrated a promising new approach to sustainable management of industrial PG waste by treating PG with the novel inexpensive MCA to solidify SF and SP into precipitates of Ca-5(PO4)(3)F with greatly improved long-term stability.

With the growing aging population, fall detection has become a significant research focus in recent years. However, most existing studies have not effectively improved the accuracy of centroid state estimation in human instability, particularly in terms of multisource information fusion. To address this, we propose a centroid state estimation method based on the unscented Kalman filter (UKF), integrating data from the five-link model and trunk inertial measurement unit (IMU) sensors, significantly enhancing both the accuracy and robustness of centroid state estimation. Experimental results demonstrate that the proposed method achieves estimation errors of 0.0099 m and 0.0226 m/s for centroid displacement and velocity in the x-direction, and 0.0006 m and 0.0099 m/s in the y-direction, indicating high accuracy. To overcome the reliance of traditional supervised learning methods on large amounts of labeled data for instability state estimation, we propose an unsupervised learning-based instability state estimation method, featuring the adversarial training strategy for the sequence-to-sequence autoencoder-long short-term memory (AdvSAE-LSTM) model. Data from different sensors are preprocessed, and independent training datasets are constructed, after which the feature maps from each sensor are concatenated to form a global feature map. Compared to baseline methods, the proposed approach significantly improves instability state detection performance. Specifically, when the stability anomaly score (SAS) threshold is set to 0.02, both the false positive rate (FPR) and false negative rate (FNR) are 0%, with a detection delay (DD) of only 77.16 ms, achieving highly efficient and accurate estimation of human instability states.

Phosphogypsum stockpiles are significant sources of phosphorus pollution, especially during the flood season, generating phosphorus-rich acidic wastewater that exceeds the China GB 3838-2002, Class III standard (P <= 0.2 mg/L, pH = 6-9, applicable to China). This study developed a microscale zerovalent iron-calcium oxide synergy system (mZVI/Fe(II)/CaO) to treat on-site wastewater (P = 144.0 mg/L, pH = 2.43). Laboratory results showed that adding 0.39 g/L CaO and 10.0 g/L mZVI/Fe(II) reduced phosphorus to below 0.2 mg/L in 40 s, while maintaining a pH between 8 and 9. Mechanistically, the system removed phosphate by forming insoluble precipitates, including Fe3(PO4)28H2O, FePO4, and CaHPO42H2O. Additionally, CaO created an alkaline environment that altered the chemical form of the phosphate, thereby enhancing the adsorption capacity of mZVI/Fe(II) for phosphate. Leachate analysis after phosphorus removal by mZVI/Fe(II)/CaO showed compliance with the surface water Class III standard, indicating that its treatment would not cause secondary pollution to surface water bodies. The field experiment (wastewater flow rate = 150 m3/h) confirmed that the mZVI/Fe(II)/CaO ensured that both phosphorus concentration and pH reached the surface water Class III standard within 40 s and maintained stable phosphorus removal for 1-2 h. This study provides a feasible technical solution for the emergency treatment of phosphorus-containing wastewater.

Spherical robots, characterized by their unique fully enclosed spherical structures, offer enhanced mobility and robust operation in challenging environments and adverse weather conditions. This paper presents the design and control of a dual-degree-of-freedom pendulum-driven spherical robot, addressing key limitations of conventional designs such as large turning radii and inadequate slope-climbing capabilities. The influence of the pendulum’s eccentric mass on climbing performance and obstacle-crossing ability is systematically analyzed, leading to a novel structural design approach. A comprehensive dynamic model is established, decoupling the robot’s motion into linear and steering components to facilitate control system development. Building upon this model, an Adaptive High-Speed Sliding Mode Control (AHSMC) strategy is proposed to achieve rapid response and robust trajectory tracking under highly nonlinear dynamics. Experimental validations demonstrate a minimum turning radius of 0.2 meters and stable operation on slopes up to 12 ^∘ . Moreover, the AHSMC-based velocity control outperforms traditional PID and hierarchical sliding mode control (HSMC) methods in terms of speed regulation accuracy and robustness, enabling precise trajectory tracking across both smooth and asphalt surfaces. The results substantiate the effectiveness of the proposed design and control framework, underscoring its potential for practical deployment in dynamic and constrained environments typical of small-scale robotics applications.

OBJECTIVE:To investigate the clinical efficacy of exoskeleton-assisted training on lower limb functional recovery in the early rehabilitation phase following anterior cruciate ligament reconstruction (ACLR). METHODS:Between February 2024 and August 2024, 20 patients who were 2 to 3 weeks after anterior cruciate ligament reconstruction (ACLR) were selected, including 13 males and 7 females, with ages ranging from 18 to 45 years old (28.5±6.2) years old. These patients were divided into a conventional rehabilitation group and an exoskeleton-assisted group, with 10 patients in each group. The conventional rehabilitation group consisted of 6 males and 4 females, with a mean age of (24.60±1.78) years old, and received routine rehabilitation training. The exoskeleton-assisted group included 7 males and 3 females, with a mean age of (25.20±1.93) years old;on the basis of conventional rehabilitation, exoskeleton robot assistance was applied during gait training for this group. Both groups underwent a 4-week intervention program, five days per week. Lower limb function was assessed pre-intervention and at 4 weeks post-intervention using the Lysholm knee score, visual analogue scale (VAS) for pain, knee flexion range of motion (ROM), and center of pressure (COP) displacement area. RESULTS:Postoperative wound healing was satisfactory with no complications such as infection or re-injury. All 20 patients completed the 4-week intervention and assessments. After 4 weeks of intervention, the Lysholm scores of both groups were significantly higher than those before intervention;the score of the exoskeleton-assisted group (82.30±4.15) was higher than that of the conventional rehabilitation group (72.60±4.88), with a statistically significant difference(P<0.001). The VAS score of the exoskeleton-assisted group (2.38±0.52) was lower than that of the conventional rehabilitation group(3.45±0.69), with a statistically significant difference (P=0.001);the knee flexion ROM of the exoskeleton-assisted group(124.50±5.34)° was greater than that of the conventional rehabilitation group (115.80±5.76)°, with a statistically significant difference(P=0.002);the COP displacement area of the exoskeleton-assisted group (6.28±0.94) cm2 was smaller than that of the conventional rehabilitation group (8.45±1.12) cm2, with a statistically significant difference (P<0.001). CONCLUSION:Exoskeleton-assisted robotic training more effectively improves early knee function, reduces pain, increases joint range of motion, and enhances balance control in patients following ACLR. It may be applied as an optimized approach for early postoperative rehabilitation.

Wearable hand-assistive robotics play an important role in aiding elderly patients with hand dysfunction, where accurate gesture recognition and grip strength estimation are essential for natural human-robot interaction. However, few studies have tackled both tasks simultaneously. Inspired by the biological tendon-muscle system, this work introduces a soft robotic glove actuated by tendon-sheath artificial muscles. The system features an EMG-based controller that provides real-time assistance by jointly predicting hand gestures and grip strength using a GRU-based domain-adversarial neural network with a composite loss function, enabling combined classification and regression from shared EMG features. The model achieved 92.12% gesture classification accuracy and an R2 of 0.935 within subjects, and 79.43% accuracy with an R2 of 0.80 across subjects. Realtime testing with an unseen user further confirmed the model's robustness, achieving 80.94% accuracy and an R2 of 0.86. The soft robotic glove also significantly reduced forearm flexor muscle activity by up to 46.9% during grasping tasks, demonstrating effective assistance. Overall, this EMG-driven soft robotic glove offers personalized, adaptive, and precise hand support, showing strong potential to enhance autonomy and quality of life for elderly users.

Robotic hip exoskeletons hold enormous potential to enhance human locomotion. However, the rigid structures and predefined control laws limit their compliance and adaptability during dynamic human-robot interactions. Here, a novel parallel elastic hip exoskeleton is developed for human locomotion assistance. The exoskeleton utilizes a remote cable actuation system to improve compliance and incorporates a parallel elastic mechanism at the hip wearable components to enhance actuator energy efficiency by generating a compensatory torque. For exoskeleton control, a speed-adaptive torque control strategy is implemented to modulate the assistance torque in real time, based on the user's gait phase and hip movement frequency estimated by adaptive oscillators. The system was tested on seven healthy subjects, and preliminary results indicate that the parallel elastic element achieves a 40.2 % reduction in peak motor torque through energy conversion. The controller exhibits excellent torque tracking performance and effectively extracts human gait features across walking speeds with hip frequency correlation (R2 = 0.89). Furthermore, the hip exoskeleton significantly reduced users' peak hip moments and muscle activity while preserving natural kinematics. The parallel elastic hip exoskeleton demonstrates strong adaptive assistive capabilities and is expected to enhance locomotion in real-world applications.

Effective decoding of time-frequency-spatial features of electroencephalography is important for improving the performance of brain-computer interfaces (BCIs) based on motor imagery (MI). Existing attention-based and CNN-based approaches inadequately capture joint spatial-frequency dependencies and often fail to align learned features with established motor imagery priors, resulting in comparatively lower classification accuracy and limited interpretability. We propose a dual frequency-spatial-time attention network named FSTAM-net. In stage 1, a wavelet kernel network extracts interpretable time-frequency features to ensure clear interpretability, while a channel attention mechanism identifies spatial dependence features of the frequency response. In Stage 2, a self-attention mechanism is used to capture global temporal dependencies. Results show that the FSTAM-net outperformed other representative methods with 86.07%, 89.61%, and 80.79% classification accuracy on BCI Competition IV datasets 2a, 2b, and Giga DB dataset (cross-subject), respectively. Experiments demonstrate that FSTAM-net can automatically focus on relevant frequency and spatial regions for MI tasks while adaptively filtering out irrelevant time and frequency noise. Moreover, it displays desynchronization/synchronization (ERD/ERS) over any desired time frame, frequency range, or channels, even including the distinctive 'focal ERD/ surround ERS' pattern, and improves feature extraction by enhancing ERD/ERS. Overall, the results demonstrate that FSTAM-net is robust to noise interference, adaptability to different datasets, and cross-domain generalization, showing strong performance in MI intent decoding and feature analysis. The study provides a novel tool for analyzing neural responses related to MI tasks, potentially revealing significant insights into brain function in future research.

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.

Kaolinite-loaded amorphous zero-valent iron composite (K@AZVI) is a promising material for cadmiumcontaminated soil remediation. Still, the impact of iron (Fe) oxides on the surface of K@AZVI on the immobilization of cadmium (Cd) in soils caused by varying water management remains unclear. Hence, this study investigated the transformation of Fe oxides in K@AZVI and their effects on Cd morphology under varying soil water management (drying, wetting, and flooding). The results indicated that under K@AZVI treatment, the pe + pH (pe = Eh (oxidation-reduction potential, mV) / 59.2) values decreased from 11.59 to 9.83 and 5.61 with increased soil water management. Meanwhile, the content of crystalline Fe oxides (Fec) decreased significantly by 34.02 %-72.76 %, while the content of amorphous Fe oxides (Feo) increased by 29.01 %-55.20 %. This transformation from crystalline to amorphous Fe oxides significantly enhanced the immobilization of Cd, resulting in a 30.30 %-64.59 % reduction in the available Cd content compared to the control. In addition, the proportion of low active Cd was enhanced from 32.94 % to 56.10 %, further indicating that K@AZVI exhibited superior immobilization in flooded soil. Characterization and calculations showed that water management significantly improved the immobilization of Cd by K@AZVI by regulating the crystallinity of Fe oxides and enhancing their electron transfer rate and adsorption capacity. Therefore, investigating the morphological transformation of Fe under varying water management will help us to understand the mechanism of Fe redoxmediated Cd transport and transformation in the soil system.

This study aims to quantify the contributions of external, muscle, and ligament forces to the tibiofemoral contact loads during gait. Additionally, the relative contributions in patients with knee osteoarthritis (KOA) and healthy individuals were also compared. For this aim, twenty medial Kellgren-Lawrence (KL) 3-4 KOA patients and twenty healthy controls were recruited to perform the gait data collection experiment using a motion capture and force plate system. The relative contributions were calculated based on an improved musculoskeletal model with knee ligaments. The results showed that the contribution of muscle forces to the total compartment contact loads was greater than that of external forces for both the healthy individuals and the KOA patients. The medial compartment contact loads were contributed predominantly by external forces, and the lateral compartment contact loads were contributed negatively by external forces for both the healthy individuals and the KOA patients. For the healthy individuals, the total/lateral compartment contact loads were predominantly contributed by muscle forces. The ligament forces provide a contribution similar to muscle forces to the medial compartment contact loads. For the KOA patients, the total/lateral compartment contact loads were contributed predominantly by ligament forces. The ligament forces provide a negative contribution to the medial compartment contact loads. In conclusion, the knee ligaments provided important contributions to the tibiofemoral contact loads. Significant differences were found in the relative contributions between the KOA patients and the healthy individuals. The results of this study have significant clinical implications for further improving the current biomechanical treatments of KOA.

Knee osteoarthritis remains challenging to cure fundamentally, necessitating strategies to delay disease progression and enhance knee joint function through rehabilitation. Integrating sensors into lower limb rehabilitation exoskeletons can impart sensory capabilities, allowing for the collection and feedback of patient data during rehabilitation. This facilitates motor intention perception and quantitative rehabilitation evaluation. The human-robot interaction force is a reliable signal that provides patient motion state information using minimal sensors without significant performance variation. This study introduces a fiber grating human-robot interaction force sensor for lower limb rehabilitation exoskeleton, designed for easy installation due to its compact size. The sensor measures interaction force on the human sagittal plane, with three fiber gratings positioned at 120 degrees intervals around the sensor's circumference and an additional grating for temperature compensation. Experimental results indicate the sensor's axial force detection range is 0 to 100 N, with a resolution of 0.1 N.

Accurate gait phase estimation and recognition of lower limb movements are crucial for wearable assistive devices in medical rehabilitation. Due to the complexity of movement scenarios, obtaining precise motion modes and gait phases in real-time is particularly challenging. This is especially true for tasks that require high responsiveness, where algorithms need to recognize gait phase and movement mode simultaneously with or even ahead of the motion to inform further decisions by wearable devices. To address this issue, we proposed a novel cascaded multimodal information fusion approach for stable real-time lower limbmotion recognition based on a lightweight lower limb sensor system integrating shank-mounted inertial measurement unit (IMUs) and foot pressure sensors. Furthermore, an adaptive extended Kalman filter (AKEF) was employed to enhance the accuracy of movement mode recognition. In experimental settings, the continuous gait phase estimation error in mixed movement scenarios was as low as 2.12%, with errors of 1.43% in the stable gait phase and 2.51% in the transition phase. The movement mode recognition accuracy in mixed movement scenarios improved from 98.42% to 99.21%. This study will be applied to subsequent research in human motion analysis and real-time control of lower limb exoskeletons.

This paper presents a lightweight bidirectional cable-driven ankle exoskeleton system (total mass: 2.6 kg) based on a series elastic actuation architecture (actuator module mass: 1.05 kg). The system utilizes a waist-mounted drive unit and Bowden cables to deliver bidirectional assistance to the ankle joint (nominal force: 460 N, peak force: 680 N). By integrating a dynamically coupled adaptive oscillator (AO), the system achieves robust gait synchronization across a range of walking speeds (0.6 - 1.8 m/ s, phase estimation RMSE <2.48%, stride frequency estimation RMSE <0.1 Hz). This is complemented by a Gaussian Process (GP)-based torque planner and a cascaded torque control framework, ensuring seamless coordination with natural gait. Experimental characterization of the actuator demonstrates its high dynamic performance (torque bandwidth: 12.5 Hz) and low-impedance characteristics (peak passive backdrive torque: 0.97 N . m). Human trials involving five participants show that the system significantly expands the ankle joint range of motion (up to [-15.64 degrees, 20.67 degrees] at high speeds) while reducing peak muscle activation levels in the tibialis anterior (18.54%-30.21%) and gastrocnemius (19.34%-25.45%). This design, combining lightweight construction with adaptive control strategies, provides a highly effective solution for daily mobility assistance and rehabilitation applications.

The objective of this study was to develop a musculoskeletal model incorporated with a subject-specific knee joint to predict the tibiofemoral contact force (TFCF) during daily motions. For this purpose, 18 healthy participants were recruited to perform the motion data acquisition using synchronized motion capture and force platform systems, and motion simulation based on an improved musculoskeletal model for five daily activities, including normal walking, stair ascent, stair descent, sit-to-stand, and stand-to-sit. The proposed musculoskeletal model included subject-specific models of bones, cartilages, and meniscus, detailed knee ligaments and muscles, deformable elastic contacts, and multiple degrees of freedom (DOFs) of the knee joint. The prediction accuracy was demonstrated by the good agreements of TFCF curves between the model predictions and in vivo measurements for the five activities (RMSE: 0.216~0.311 BW, R2: 0.928~0.992, and CE: 0.048~0.141). Based on the validated model, the TFCF on total, medial, and lateral compartments (TFCFTotal, TFCFMedial, and TFCFLateral) during the five daily activities were predicted. For TFCFTotal, the peak force for stair descent or sit-to-stand was the largest, followed by stair ascent or stand-to-sit, and finally normal walking. For TFCFMedial, stair descent had the largest peak, followed by stair ascent. There were no significant differences between the peak TFCFMedial values of normal walking, sit-to-stand, and stand-to-sit. For TFCFLateral, the peak of sit-to-stand was the largest, followed by stand-to-sit or stair descent, and finally normal walking or stair ascent. This study is valuable for further understanding the biomechanics of a healthy knee joint and providing theoretical guidance for the treatment of knee osteoarthritis (KOA).

Knee and ankle single-joint exoskeletons enhance human locomotion but can impact the hip joint. This article introduces a multijoint coupling assistance mechanism and a modular exoskeleton prototype that avoids negative effects on the hip joint. During the stance phase, it sequentially assists the knee and ankle with one quasi-direct drive motor without affecting other joints, and reduces hip joint load in the swing phase. A controller trained on inertial measurement unit data from both legs determines the gait phase and generates corresponding biological torque. Experiments showed a 9.08% decrease in peak fascia lata muscle activity and an 18.32% reduction in peak hip joint torque when using the exoskeleton. This study reports that reducing the impact on the hip joint when assisting a single joint such as the knee or ankle can enhance the overall assistance performance of the exoskeleton.

Aiming at the problems of complex and harsh working environments and low reliability of the working arm of the anchor drilling robot, the dynamic characteristics of the working arm are studied based on the dynamic simulation analysis and vibration test analysis. In view of the influence of working load and deformation on the rigidity of the working arm, the static finite element analysis is implemented by simulating the actual working conditions. The stress and strain distribution of the working arm under the roof anchor drilling condition is derived, and on this basis, the modal analysis of the working arm with pre-stress is carried out. The six orders of vibration mode and the corresponding inherent frequencies are obtained. A vibration test system of the working arm is set up to achieve the three-dimensional vibration accelerations of the working arm under the roof anchor drilling condition. The fast Fourier transform of the vibration acceleration signals is conducted to obtain the vibration spectrum. The dynamic characteristics of the working arm are analyzed through the vibration frequency and the inherent frequency, which is of great reference value for improving the reliability of the working arm.

Background and Objective: Decoding pre-movement intention is crucial in developing a brain-computer interface (BCI) for neuro-rehabilitation robotic systems. However, the weak amplitude and non-smooth characteristics of EEG signals lead to the inability of existing methods to achieve the accuracy for proper applications. This study proposed a novel pre-movement intention decoding network framework to improve accuracy by extracting and optimizing the deep spatio-temporal features of EEG signals. Methods: A deep spatio-temporal neural network structure was constructed based on the brain intention generation mechanism and its movement expression. The collected multi-channel EEG data were reorganized into brain topographic distributions, after the initial extraction of the features and optimization using the coordinate attention mechanism, a 3-layer dense block with two bi-directional gated recirculation units was designed to effectively extract the deep spatial and temporal features, further decoding the pre-movement intention efficiently. Results: The experimental results showed an average accuracy of 95.51 f 1.79 % for healthy subjects and 90.48 f 2.90 % for stroke survivors in decoding pre-movement intention. All evaluation indexes are excellent. Pseudo-online testing showed the average TPR was 95.45 f 3.80 % and 90.71 f 7.77 % for healthy subjects and stroke survivors, respectively, and the latency was-1965 f 48 ms and-1974 f 36 ms. The results of the ablation and comparative analysis showed that the proposed framework is justified and its decoding capability outperforms other state-of-the-art algorithms. Conclusion: The method proposed in this study has high decoding accuracy and good online performance in pre-movement intention decoding based on EEG signals, which lays the foundation for further neuro-rehabilitation robotic systems.

Pomegranate harvesting remains a challenging task due to the fruit's tough stem, dense canopy, and sensitivity to mechanical damage. Traditional harvesting robots rely on vision-based stem localization, which increases computational complexity and reduces robustness in unstructured orchard environments. This paper presents a dual-shear ring end-effector designed to eliminate the need for precise stem detection, utilizing a self-locking shear mechanism that allows the stem to naturally align between the cutting blades. The system integrates a vision-assisted robotic manipulator for fruit detection and a torque regulation mechanism for optimized cutting force application. Experimental validation demonstrates a success rate of over 90% for stems up to 8 mm in diameter and robust performance even under partial and full occlusion conditions. The results confirm that the proposed system achieves efficient, adaptable, and damage-free harvesting, providing a viable solution for autonomous pomegranate harvesting.

Currently, few unpowered exoskeletons are used in the actual rehabilitation of patients. For individuals after anterior cruciate ligament (ACL) reconstruction, they usually exhibit abnormal gait characteristics, and this abnormal gait increases the likelihood of osteoarthritis (OA) in the long term. For these individuals, considering that the knee joint may be unstable due to insufficient muscle strength especially during the weight acceptance phase, this paper proposes a knee-extension-assisted unpowered exoskeleton for knee protection as well as gait rehabilitation training. This lightweight exoskeleton, weighing only 450g, holds promise for enhancing gait stability and lowering the risk of joint osteoarthritis. Custom-designed miniature gas springs contribute to energy storage and provide specified stiffness support to the human knee joint. Gait experiments conducted on six subjects (after ACL reconstruction nearly 1 month) indicated that walking with this exoskeleton significantly reduced peak rectus femoris (RF) muscle activity by 36.4% in weight acceptance phase. The results demonstrated the potential of this exoskeleton to relieve stress on the knee extensor muscles, safeguard patients during recovery, and assist in early gait training.