Intuitive Human-Robot-Environment Interaction With EMG Signals:A Review

A long history has passed since electromyography(EMG)signals have been explored in human-centered robots for intuitive interaction.However,it still has a gap between scientific research and real-life applications.Previous studies mainly focused on EMG decoding algorithms,leaving a dynamic relationship between the human,robot,and uncertain environment in real-life scenarios seldomly concerned.To fill this gap,this paper presents a comprehensive review of EMG-based techniques in human-robot-environment interaction(HREI)systems.The general processing framework is summarized,and three interaction paradigms,including direct control,sensory feedback,and partial autonomous control,are introduced.EMG-based intention decoding is treated as a module of the proposed paradigms.Five key issues involving precision,stability,user attention,compliance,and environmental awareness in this field are discussed.Several important directions,including EMG decomposition,robust algorithms,HREI dataset,proprioception feedback,reinforcement learning,and embodied intelligence,are proposed to pave the way for future research.To the best of what we know,this is the first time that a review of EMG-based methods in the HREI system is summarized.It provides a novel and broader perspective to improve the practicability of current myoelectric interaction systems,in which factors in human-robot interaction,robot-environment interaction,and state perception by human sensations are considered,which has never been done by previous studies.

Deep Learning for EMG-based Human-Machine Interaction:A Review

Electromyography(EMG)has already been broadly used in human-machine interaction(HMI)applications.Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a solution.Recently,many EMG pattern recognition tasks have been addressed using deep learning methods.In this paper,we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI.An overview of typical network structures and processing schemes will be provided.Recent progress in typical tasks such as movement classification,joint angle prediction,and force/torque estimation will be introduced.New issues,including multimodal sensing,inter-subject/inter-session,and robustness toward disturbances will be discussed.We attempt to provide a comprehensive analysis of current research by discussing the advantages,challenges,and opportunities brought by deep learning.We hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI systems.Furthermore,possible future directions will be presented to pave the way for future research.

Simultaneous occurrence of four magnetospheric wave modes and the resultant combined scattering effect on radiation belt electrons

We report a representative concurrent event of four wave modes at L≈5.0,including electrostatic electron cyclotron harmonic(ECH)waves,exohiss,magnetosonic(MS)waves,and electromagnetic ion cyclotron(EMIC)waves,based on the observations from Van Allen Probe A on October 15,2015.The diffusion coefficients induced by these waves are calculated by using both the Full Diffusion Code and test particle simulations.Moreover,the scattering effects of these waves on energetic electrons are simulated by using a two-dimensional Fokker-Planck diffusion model.The results show that ECH waves mainly scatter low-pitch-angle(<20°)electrons at 0.1-10 keV;exohiss can significantly scatter hundreds of kiloelectron volt electrons to form a reversed energy spectrum;MS waves mainly affect high-pitch-angle electrons(>60°);and EMIC waves scatter only>5 MeV electrons.The combined scattering effects of exohiss and MS waves are stronger than those of exohiss alone.The top-hat pitch angle distributions produced by exohiss are relaxed after adding the effect of MS waves.Because the energies of electrons scattered by ECH waves and EMIC waves are much lower and higher than those scattered by exohiss and MS waves,respectively,the combined scattering effects with the addition of ECH and EMIC waves show little difference from the results for the combination of MS waves and exohiss.These results suggest that distinct wave modes can occur simultaneously and scatter electrons in combination or individually,which requires careful consideration in future global simulations of the complex dynamics of radiation belt energetic electrons.

Xylocarpin H, a Limonoid of <i>Xylocarpus granatum</i>, Produces Antidepressant-Like Activities in Mice

Major depression is a common psychiatric disorder worldwide that imposes a substantial health burden on society. Currently available antidepressants do not meet the clinical needs. Here, we report that Xylocarpin H, a limonoid of Xylocarpus granatum, has antidepressant-like effects in mouse forced swimming and tail suspension tests, two validated models of depression. 7-day oral administration of Xylocarpin H resulted in dose-dependent decreases immobility duration within the dose range of 15 - 50 mg/kg. Xylocarpin H dose-dependently increases the time spent in the central zone at doses of 5 - 50 mg/kg in locomotion activity test. In addition, 7-day treatment Xylocarpus H at 15 and 50 mg/kg doses significantly decreases levels of serum corticosterone and adrenocorticotropic hormone (ACTH) following the acute stress of forced swimming test. Furthermore, these effective doses of Xylocarpin H do not affect locomotor activity and levels of serum corticosterone and ACTH in the absence of stress. In summary, the present study, for the first time, demonstrates that Xylocarpin H exerts antidepressant-like effects in mouse behavioral models of depression, likely by inhibiting HPA axis systems. These data provide primarily basis for developing Xylocarpin H as a novel antidepressant candidate for the treatment of depression and stress related disorders.

Ultrasound-assisted extraction of bound phenolic compounds from the residue of Apocynum venetum tea and their antioxidant activities

Bound phenolic compounds from the residue of Apocynum venetum(A.venetum)tea were released by alkaline digestion with sonication,followed by extraction using ethyl acetate(EtOAc).The fragment ions of compounds in free and bound forms were identified by liquid chromatography electrospray ionization tandem mass spectrometry(LC-ESI-MS/MS)in both positive and negative modes.Further investigation of chemical constituents in the EtOAc extract led to the isolation of eleven individual compounds,and their chemical structures were identified as(−)-epicatechin(1),(−)-loliolide(2),(−)-syringaresinol-4-O-β-D-glucopyranoside(3),alloside of benzyl alcohol(4),3-[(6-O-hexopyranosylhexopyranosyl)oxy]-2-(palmitoyloxy)propyl(9Z,12Z,15Z)-9,12,15-octadecatrienoate(5),apocynoside I(6),quercetin-3-O-β-D-glucopyranoside(7),kaempferol-3-O-β-D-glucopyranoside(8),[1-acetyloxy-3-[3,4,5-trihydroxy-6-[[3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxymethyl]oxan-2-yl]oxypropan-2-yl]hexadecanoate(9),1-stearoyl-2-palmitoyl-sn-glycerol(10),and 3-hexene-l-O-β-D-glucoside(11)using a series of spectroscopic methods.Compounds 2–5 and 9–11 were isolated for the first time from A.venetum,while antioxidant experiments showed that compounds 1,7,and 8 displayed strong DPPH and ABTS+radical scavenging activities.This study represents the first investigation of bound phenolic compounds with potential health benefits in the residue of A.venetum tea.