Graphene nanosheets were produced by electrical explosion of high-purity graphite sticks in distilled water at room temperature. The as-prepared samples were characterized by various techniques to find different forms of carbon phases, including graphite nanosheets, few-layer graphene, and especially, mono-layer graphene with good crystallinity. Delicate control of energy injection is critical for graphene nanosheet formation, whereas mono-layer graphene was produced under the charging voltage of 22.5-23.5 kV. On the basis of electrical wire explosion and our experimental results, the underlying mechanism that governs the graphene generation was carefully illustrated. This work provides a simple but innovative route for producing graphene nanosheets.
This paper presents a novel methodology for detecting the gait phase of human walking on level ground. The previous threshold method (TM) sets a threshold to divide the ground contact forces (GCFs) into on-ground and off-ground states. However, the previous methods for gait phase detection demonstrate no adaptability to different people and different walking speeds. Therefore, this paper presents a self-tuning triple threshold algorithm (STTTA) that calculates adjustable thresholds to adapt to human walking. Two force sensitive resistors (FSRs) were placed on the ball and heel to measure GCFs. Three thresholds (i.e., high-threshold, middle-threshold andlow-threshold) were used to search out the maximum and minimum GCFs for the self-adjustments of thresholds. The high-threshold was the main threshold used to divide the GCFs into on-ground and off-ground statuses. Then, the gait phases were obtained through the gait phase detection algorithm (GPDA), which provides the rules that determine calculations for STTTA. Finally, the STTTA reliability is determined by comparing the results between STTTA and Mariani method referenced as the timing analysis module (TAM) and Lopez–Meyer methods. Experimental results show that the proposed method can be used to detect gait phases in real time and obtain high reliability when compared with the previous methods in the literature. In addition, the proposed method exhibits strong adaptability to different wearers walking at different walking speeds.
Featured Application: The proposed non-anthropomorphic 3-DOF upper-limb exoskeleton is appropriate for the purpose of material hanging in an industrial setting, especially for handling heavy loads by the front side of the human body. Abstract:The contradiction between self-weight and load capacity of a power-assisted upper-limb exoskeleton for material hanging is unresolved. In this paper, a non-anthropomorphic 3-degree of freedom (DOF) upper-limb exoskeleton with an internally rotated elbow joint is proposed based on an anthropomorphic 5-DOF upper-limb exoskeleton for power-assisted activity. The proposed 3-DOF upper-limb exoskeleton contains a 2-DOF shoulder joint and a 1-DOF internally rotated elbow joint. The structural parameters of the 3-DOF upper-limb exoskeleton were determined, and the differences and singularities of the two exoskeletons were analyzed. The workspace, the joint torques and the power consumption of two exoskeletons were analyzed by kinematics and dynamics, and an exoskeleton prototype experiment was performed. The results showed that, compared with a typical anthropomorphic upper-limb exoskeleton, the non-anthropomorphic 3-DOF upper-limb exoskeleton had the same actual workspace; eliminated singularities within the workspace; improved the elbow joint force situation; and the maximum elbow joint torque, elbow external-flexion/internal-extension and shoulder flexion/extension power consumption were significantly reduced. The proposed non-anthropomorphic 3-DOF upper-limb exoskeleton can be applied to a power-assisted upper-limb exoskeleton in industrial settings.
Continuous joint angle estimation based on a surface electromyography (sEMG) signal can be used to improve the man-machine coordination performance of the exoskeleton. In this study, we proposed a time-advanced feature and utilized long short-term memory (LSTM) with a root mean square (RMS) feature and its time-advanced feature (RMSTAF; collectively referred to as RRTAF) of sEMG to estimate the knee joint angle. To evaluate the effect of joint angle estimation, we used root mean square error (RMSE) and cross-correlation coefficient ρ between the estimated angle and actual angle. We also compared three methods (i.e., LSTM using RMS, BPNN (back propagation neural network) using RRTAF, and BPNN using RMS) with LSTM using RRTAF to highlight its good performance. Five healthy subjects participated in the experiment and their eight muscle (i.e., rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gracilis (GC), semimembranosus (SM), sartorius (SR), medial gastrocnemius (MG), and tibialis anterior (TA)) sEMG signals were taken as algorithm inputs. Moreover, the knee joint angles were used as target values. The experimental results showed that, compared with LSTM using RMS, BPNN using RRTAF, and BPNN using RMS, the average RMSE values of LSTM using RRTAF were respectively reduced by 8.57%, 46.62%, and 68.69%, whereas the average ρ values were respectively increased by 0.31%, 4.15%, and 18.35%. The results demonstrated that LSTM using RRTAF, which contained the time-advanced feature, had better performance for estimating the knee joint motion.
Hikers and soldiers usually walk up and down slopes with a load carriage, causing injuries of the musculoskeletal system, especially during a prolonged load journey. The slope walking has been reported to lead to higher leg extensor muscle activities and joint moments. However, most of the studies investigated muscle activities or joint moments during slope walking without load carriage or only investigated the joint moment changes and muscle activities with load carriages during level walking. Whether the muscle activation such as the signal amplitude is influenced by the mixed factor of loads and grades and whether the influence of the degrees of loads and grades on different muscles are equal have not yet been investigated. To explore the effects of backpack loads on leg muscle activation during slope walking, ten young male participants walked at 1.11 m/s on a treadmill with different backpack loads (load masses: 0, 10, 20, and 30 kg) during slope walking (grade: 0, 3, 5, and 10°). Leg muscles, including the gluteus maximus (GM), rectus femoris (RF), hamstrings (HA), anterior tibialis (AT), and medial gastrocnemius (GA), were recorded during walking. The hip, knee, and ankle extensor muscle activations increased during the slope walking, and the hip muscles increased most among hip, knee, and ankle muscles (GM and HA increased by 46% to 207% and 110% to 226%, respectively, during walking steeper than 10° across all load masses (GM: p = 1.32 × 10−8 and HA: p = 2.33 × 10−16)). Muscle activation increased pronouncedly with loads, and the knee extensor muscles increased greater than the hip and ankle muscles (RF increased by 104% to 172% with a load mass greater than 30 kg across all grades (RF: p = 8.86 × 10−7)). The results in our study imply that the hip and knee muscles play an important role during slope walking with loads. The hip and knee extension movements during slope walking should be considerably assisted to lower the muscle activations, which will be useful for designing assistant devices, such as exoskeleton robots, to enhance hikers’ and soldiers’ walking abilities.
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