Research On Key Technologies Of Upper Limb Motor Analysis Based On Surface EMG

The production of human movement depends on the contraction of skeletal muscles under the stimulation of central control system,which drives the bone to move in a directional manner with the joint as the fulcrum.Electromyography(EMG)signal would be generated during the contraction of skeletal muscles,which carries motor neural drive information and can directly reflect the nature of movements.Among them,the surface electromyography(SEMG)signal can be collected non-invasively on the skin surface,and the operation is simple and convenient,which is very suitable for human motor analysis.Motor analysis based on SEMG includes not only analyzing the mechanisms of movement production,investigation of the pathological causes of the emergence of movement dysfunction,and the evaluation of motor health status,but also the capture of motor behaviors,understanding of movement intentions,and the construction of human-machine interface using motor information as instructions for control.All of these directions have important research significance.Accurate motor analysis depends on the breakthrough of SEMG signal processing technology.Specifically,in terms of analyzing motor health,although SEMG analysis is considered as a very promising way of diagnosing neuromuscular diseases and evaluating motor dysfunctions,methods for systematic and quantitative assessment or evaluation have not yet been established.Especially,some current SEMG-based evaluation indicators and methods need to be developed and improved,and their clinical significance requires further verification.In terms of understanding movement intentions,although the technique of myoelectric control of multiple degrees of freedom based on pattern recognition has made significant progress,it is limited to the analysis of a variety of predefined and isolated motion patterns.Namely,this technique usually lacks a meaningful description and interpretation of the entire process of continuous movements.Besides,muscle fatigue always made a negative impact on the performance of myoelectric control,but its mechanism is still unclear,and as a result the methods for overcoming its negative effect have not fully developed.Aiming at addressing both technical problems regarding the lack of effective clinical diagnosis methods and the difficulty in accurate estimation of continuous movements,this dissertation contributes to the development of the SEMG signal processing techniques.Movements of the human upper limbs are considered due to their dexterous and important motor functions in the activities of daily lives.In-depth researches on key techniques of the SEMG signal processing have been carried out in three different directions,including the diagnosis of complex neuromuscular diseases,the assessment of motor dysfunctions after peripheral nerve injury(PNI),the estimation of continuous joint movements.A series of effective methods and solutions are presented.The primary research achievements and contributions of this dissertation are listed as follows:(1)Considering the existence of complex changes in the poststroke hemiparetic muscles and the lack of effective SEMG diagnostic methods,a method based on clustering index analysis was proposed to examine complex neuromuscular changes.It was specifically used to explore the similarities and differences of neuromuscular changes between the distal and proximal muscles after stroke hemiparesis.The SEMG signals from the biceps brachii,thenar muscles,and the first dorsal interosseous muscles were recorded during the isometric muscle contractions at different contraction force levels on both sides of stroke subjects,while the SEMG data were also collected from the same muscles on the dominant side of age-matched healthy control subjects.Subsequently,the clustering index method was used to analyze how a poststroke cerebral lesion affects motor unit(MU)survival and functions,and compared the MU changes in type and degree within different muscles.It was found from this study that the MU changes in paretic muscles after stroke were much complex across subjects and muscles,in both neurogenic changes and myopathic changes.In particular,the abnormalities in both distal muscles of the same patient showed a high degree of consistency at the individual level,but no significant correlation was found between the distal and proximal muscles.These findings were well interpreted from the viewpoint of neuromuscular pathology,with sufficient discussions.This study further explored the pathological mechanism of poststroke hemiplegia,providing some basis for clinical diagnosis and treatment.It also demonstrated important clinical application potential of the SEMG technique for non-invasive examination of neuromuscular changes.(2)Considering the lack of non-invasive quantitative diagnostic techniques in the field of clinical assessment of the degree of PNI,a non-invasive quantitative evaluation method for traumatic upper limb PNI based on SEMG was proposed.This method worked by the means of analyzing the possible data property changes and functional abnormalities reflected from the SEMG signal after the occurrence of PNI.In the proposed method,SEMG signal acquisition sites and movement testing tasks were designed based on anatomical knowledge.A quantitative evaluation framework was presented with SEMG signal analyzed in different aspects.This framework included two modules:evaluation of upper limb PNI existence and quantitative evaluation of injury degree of three individual nerves.In order to evaluate the performance of the proposed method,7 subjects suffering from the upper limb PNI and 10 healthy subjects were recruited.The quantitative evaluation decisions from both the proposed method and the clinical routine evaluation approach were compared.The proposed method yielded high consistent results/outcomes with the clinical evaluation approach,which proves the effectiveness of the proposed method.In addition,compared with the clinical routine evaluation approach,the proposed method was able to pay more attention to upper-limb motor function after the PNI.(3)Considering the fact that many previous studies failed to support continuous elbow joint angle estimation during complex movement situations,a novel elbow joint angle estimation method was proposed.The proposed method was based on the equilibrium state assumption that the dynamic flexion,extension and hovering state of the elbow joint correspond to different equilibrium states formed by both active and antagonistic muscle contractions.The proposed method for joint angle estimation involved SEMG data segmentation according to equilibrium states by detecting different movement directions and the hovering state.Subsequently,the SEMG-force-angle relationship was constructed based on Hill muscle model and biomechanical model for different equilibrium states respectively.Five different elbow joint motion tasks included different hovering states and motion direction reversals were designed as testing tasks in the experiment,and 4 different comparison methods were used for verifying the effectiveness of the proposed method.The experimental results proved that the proposed method based on different equilibrium states can effectively improve the joint angle estimation accuracy under all of the 5 above mentioned testing tasks.Especially,the performance improvement was found to be significant under relatively complex testing tasks.(4)Considering the performance degradation of elbow joint angle estimation caused by muscle fatigue due to its impact on SEMG-movement relations,a method for estimating continuous elbow joint angle with correcting the fatiguing effect was proposed.It was found that muscle fatigue has a greater impact on a part of the tested subjects,which showed as an evident increase of the SEMG root mean square(RMS)amplitude,and a downward shift of the corresponding frequency spectrum.Especially,it was further found that the RMS amplitudes of the low-frequency bands of the SEMG signals appeared to increase more obviously during the muscle fatiguing approach by analyzing the RMS changes of SEMG signal at different frequency bands.Accordingly,a method with muscle fatigue correction is proposed to filtering out low-frequency bands of the raw SEMG data,and it was found that an optimal correction of the fatiguing effect was achieved when the cut-off frequency of a high-pass filter was set to be 100Hz.This finding further verified that the changing of SEMG during muscle fatiguing approach might be related to the increased recruitments of type I muscle fibers.