A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements

Objective. Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle synergy for simultaneously estimating hand and wrist movements. Approach. Muscle excitations were firstly extracted using a two-step muscle synergy-derived method. Specifically, we calculated subject-specific muscle weighting matrix and corresponding profiles according to contributions of different muscles for movements derived from synergistic motion relation. Then, the improved excitations were used to simultaneously estimate hand and wrist movements through musculoskeletal modeling. Moreover, the offline comparison among the proposed method, traditional MM and regression methods, and an online test of the proposed method were conducted. Main results. The offline experiments demonstrated that the proposed approach outperformed the EMG envelope-driven MM and three regression models with higher R and lower NRMSE. Furthermore, the comparison of excitations of two MMs validated the effectiveness of the proposed approach in extracting muscle excitations in the presence of crosstalk. The online test further indicated the superior performance of the proposed method than the MM driven by EMG envelopes. Significance. The proposed excitation-extracting method identified more accurate neural commands for MMs, providing a promising approach in rehabilitation and robot control to model the transformation from surface EMG to joint kinematics.

Controllable assembly of continuous hollow graphene fibers with robust mechanical performance and multifunctionalities

Macroscopic conformation of individual graphene sheets serves as the backbone of translating their intrinsic merits towards multifunctional practical applications. However, controllable and continuous assemblies of graphene-based nanomaterials to create stable macroscopic structural components are always in face of great challenge. We have developed a scalable converging-flow assisted wet-spinning methodology for continuously fabricating hollow graphene fibers (HGFs, the newest variation of solid graphene fibers) with high quality. The degradable silk thread is selectively utilized as the continuous hollow structure former that holds the coaxially stacked graphene sheets aligned through the converging-flow modulating process. For the first time, we have created the longest freestanding HGF in length of 2.1 m. The continuous HGFs are in an average diameter of 180 μm and with 4-8 μm adjustable wall thicknesses. The optimal HGF demonstrates an average tensile strength of 300 MPa and modulus of 2.49 GPa (comparable to typical solid graphene fibers, but the highest among the reported HGFs in literature) and an exceptional failure elongation of 10.8%. Additionally, our continuous HGFs exhibit spontaneous resistive response to thermal and strain stimuli (in form of large deformations and human motions), offering great potential for developing multifunctional sensors. We envision that this work demonstrates an effective and well-controlled macroscopic assembly methodology for the scaled-up mass production of HGFs.

Short-term Inbetweening of 3D Human Motions

Creating computer generated human animations without the use of motion capture technology is a tedious and time consuming activity. Although there are several publications regarding animation synthesis using data driven methods, not many are dedicated towards the task of inbetweening, which consists of generating transition movements between frames. A modified version of LSTM, called Recurrent Transition Network (RTN), solves the inbetweening task for walking motion based on ten initial frames and two final frames. In this work, we are interested on the short-term inbetweening task, where we need to use the least amount of frames to generate the missing frames for short-term transitions. We are also interested on different kinds of movements, such as martial arts and Indian dance. Thus, we adapt the Recurrent Transition Network (RTN) to require only the two firts frames and the last one, called ARTN, and propose a simple post processing method combining ARTN with linear interpolation, called ARTN+. The results show that the average error of ARTN+ is less than the average error of each method (RTN and interpolation) separately in the martial arts and Indian dance dataset.

Ionic Conductive Organohydrogel With Ultrastretchability, Self-Healable and Freezing-Tolerant Properties for Wearable Strain Sensor

Currently, stretchable hydrogel has attracted great attention in the field of wearable flexible sensors. However, fabricating flexible hydrogel sensor simultaneously with superstretchability, high mechanical strength, remarkable self-healing ability, excellent anti-freezing and sensing features via a facile method remains a huge challenge. Herein, a fully physically linked poly(hydroxyethyl acrylamide)-gelatin-glycerol-lithium chloride (PHEAA-GE-Gl-LiCl) double network organohydrogel is prepared via a simple one-pot heating-cooling-photopolymerization method. The prepared PHEAA-GE-Gl-LiCl organohydrogel exhibits favorable stretchability (970%) and remarkable self-healing property. Meanwhile, due to the presence of glycerol and LiCl, the PHEAA-GE-Gl-LiCl organohydrogel possesses outstanding anti-freezing capability, it can maintain excellent stretchability (608%) and conductivity (0.102 S/m) even at −40°C. In addition, the PHEAA-GE-Gl-LiCl organohydrogel-based strain sensor is capable of repeatedly and stably detecting and monitoring both large-scale human motions and subtle physiological signals in a wide temperature range (from −40°C to 25°C). More importantly, the PHEAA-GE-Gl-LiCl organohydrogel-based sensor displays excellent strain sensitivity (GF = 13.16 at 500% strain), fast response time (300 ms), and outstanding repeatability. Based on these super characteristics, it is envisioned that PHEAA-GE-Gl-LiCl organohydrogel holds promising potentials as wearable strain sensor.