202 research outputs found

    Characteristics of muscle activation patterns at the ankle in stroke patients during walking.

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    Stroke causes impairment of the sensory and motor systems; this can lead to difficulties in walking and participation in society. For effective rehabilitation it is important to measure the essential characteristics of impairment and associate these with the nature of disability. Efficient gait requires a complex interplay of muscles. Surface electromyography(sEMG) can be used to measure muscle activity and to observe disruption to this interplay after stroke. Yet, classification of this disruption in stroke patients has not been achieved. It is hypothesised that features identified from the sEMG signal can be used to classify underlying impairments. A clinically viable gait analysis system has been developed, integrating an in-house wireless sEMG system synchronised with bilateral video and inertial orientation sensors. Signal processing techniques have been extended and implemented, appropriate for use with sEMG. These techniques have focussed on frequency domain features using wavelet analysis and muscle activation patterns using principal component analysis. The system has been used to measure gait from stroke patients and un-impaired subjects. Characteristic patterns of activity from the ankle musculature were defined using principal component analysis of the linear envelope. Patients with common patterns of tibialis anterior activity did not necessarily share common patterns of gastrocnemius or soleus activity. Patients with similar linear envelope patterns did not always present with the same kinematic profiles. The relationship between observable impairments, kinematics and sEMG is seen to be complex and there is therefore a need for a multidimensional view of gait data in relation to stroke impairment. The analysis of instantaneous mean frequency and time-frequency has revealed additional periods of activity not obvious in the linear or raw signal representation. Furthermore, characteristic calf activity was identified that may relate to abnormal reflex activity. This has provided additional information with which to group characteristic muscle activity. An evaluation of the co-activation of gastrocnemius and tibialis anterior muscles using a sub-band filtering technique revealed three groups; those with distinct co-activation, those with little co-activation and those with continuous activity in the antagonistic pair across the stride. Signal features have been identified in sEMG recordings from stroke patients whilst walking extending current signal processing techniques. Common features of the sEMG and movement have been grouped creating a decision matrix. These results have contributed to the field of clinical measurement and diagnosis because interpretation of this decision matrix is related to underlying impairment. This has provided a framework from which subsequent studies can classify characteristic patterns of impairment within the stroke population; and thus assist in the provision of rehabilitative interventions

    Human skill capturing and modelling using wearable devices

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    Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to fulfil all the requirements because even a relatively simple task such as a peg-in-hole insertion contains many uncertainties, e.g. clearance, initial grasping position and insertion path. Humans, on the other hand, can deal with these variations using their vision and haptic feedback. Although humans can adapt to uncertainties easily, most of the time, the skilled based performances that relate to their tacit knowledge cannot be easily articulated. Even though the automation solution may not fully imitate human motion since some of them are not necessary, it would be useful if the skill based performance from a human could be firstly interpreted and modelled, which will then allow it to be transferred to the robot. This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Recently, Learning from Demonstration (LfD) is gaining interest as a framework to transfer skills from human teacher to robot using probability encoding approaches to model observations and state transition uncertainties. In close or actual contact manipulation tasks, it is difficult to reliabley record the state-action examples without interfering with the human senses and activities. Therefore, wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. Firstly to track human motions accurately and reliably in a defined 3-dimensional workspace, a hybrid system of Vicon and IMUs is proposed to compensate for the known limitations of the individual system. The data fusion method was able to overcome occlusion and frame flipping problems in the two camera Vicon setup and the drifting problem associated with the IMUs. The results indicated that occlusion and frame flipping problems associated with Vicon can be mitigated by using the IMU measurements. Furthermore, the proposed method improves the Mean Square Error (MSE) tracking accuracy range from 0.8˚ to 6.4˚ compared with the IMU only method. Secondly, to record haptic feedback from a teacher without physically obstructing their interactions with the workpiece, wearable surface electromyography (sEMG) armbands were used as an indirect method to indicate contact feedback during manual manipulations. A muscle-force model using a Time Delayed Neural Network (TDNN) was built to map the sEMG signals to the known contact force. The results indicated that the model was capable of estimating the force from the sEMG armbands in the applications of interest, namely in peg-in-hole and beater winding tasks, with MSE of 2.75N and 0.18N respectively. Finally, given the force estimation and the motion trajectories, a Hidden Markov Model (HMM) based approach was utilised as a state recognition method to encode and generalise the spatial and temporal information of the skilled executions. This method would allow a more representative control policy to be derived. A modified Gaussian Mixture Regression (GMR) method was then applied to enable motions reproduction by using the learned state-action policy. To simplify the validation procedure, instead of using the robot, additional demonstrations from the teacher were used to verify the reproduction performance of the policy, by assuming human teacher and robot learner are physical identical systems. The results confirmed the generalisation capability of the HMM model across a number of demonstrations from different subjects; and the reproduced motions from GMR were acceptable in these additional tests. The proposed methodology provides a framework for producing a state-action model from skilled demonstrations that can be translated into robot kinematics and joint states for the robot to execute. The implication to industry is reduced efforts and time in programming the robots for applications where human skilled performances are required to cope robustly with various uncertainties during tasks execution

    Deep Learning Based Abnormal Gait Classification System Study with Heterogeneous Sensor Network

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    Gait is one of the important biological characteristics of the human body. Abnormal gait is mostly related to the lesion site and has been demonstrated to play a guiding role in clinical research such as medical diagnosis and disease prevention. In order to promote the research of automatic gait pattern recognition, this paper introduces the research status of abnormal gait recognition and systems analysis of the common gait recognition technologies. Based on this, two gait information extraction methods, sensor-based and vision-based, are studied, including wearable system design and deep neural network-based algorithm design. In the sensor-based study, we proposed a lower limb data acquisition system. The experiment was designed to collect acceleration signals and sEMG signals under normal and pathological gaits. Specifically, wearable hardware-based on MSP430 and upper computer software based on Labview is designed. The hardware system consists of EMG foot ring, high-precision IMU and pressure-sensitive intelligent insole. Data of 15 healthy persons and 15 hemiplegic patients during walking were collected. The classification of gait was carried out based on sEMG and the average accuracy rate can reach 92.8% for CNN. For IMU signals five kinds of abnormal gait are trained based on three models: BPNN, LSTM, and CNN. The experimental results show that the system combined with the neural network can classify different pathological gaits well, and the average accuracy rate of the six-classifications task can reach 93%. In vision-based research, by using human keypoint detection technology, we obtain the precise location of the key points through the fusion of thermal mapping and offset, thus extracts the space-time information of the key points. However, the results show that even the state-of-the-art is not good enough for replacing IMU in gait analysis and classification. The good news is the rhythm wave can be observed within 2 m, which proves that the temporal and spatial information of the key points extracted is highly correlated with the acceleration information collected by IMU, which paved the way for the visual-based abnormal gait classification algorithm.步态指人走路时表现出来的姿态,是人体重要生物特征之一。异常步态多与病变部位有关,作为反映人体健康状况和行为能力的重要特征,其被论证在医疗诊断、疾病预防等临床研究中具有指导作用。为了促进步态模式自动识别的研究,本文介绍了异常步态识别的研究现状,系统地分析了常见步态识别技术以及算法,以此为基础研究了基于传感器与基于视觉两种步态信息提取方法,内容包括可穿戴系统设计与基于深度神经网络的算法设计。 在基于传感器的研究中,本工作开发了下肢步态信息采集系统,并利用该信息采集系统设计实验,采集正常与不同病理步态下的加速度信号与肌电信号,搭建深度神经网络完成分类任务。具体的,在系统搭建部分设计了基于MSP430的可穿戴硬件设备以及基于Labview的上位机软件,该硬件系统由肌电脚环,高精度IMU以及压感智能鞋垫组成,该上位机软件接收、解包蓝牙数据并计算出步频步长等常用步态参数。 在基于运动信号与基于表面肌电的研究中,采集了15名健康人与15名偏瘫病人的步态数据,并针对表面肌电信号训练卷积神经网络进行帕金森步态的识别与分类,平均准确率可达92.8%。针对运动信号训练了反向传播神经网络,LSTM以及卷积神经网络三种模型进行五种异常步态的分类任务。实验结果表明,本工作中步态信息采集系统结合神经网络模型,可以很好地对不同病理步态进行分类,六分类平均正确率可达93%。 在基于视觉的研究中,本文利用人体关键点检测技术,首先检测出图片中的一个或多个人,接着对边界框做图像分割,接着采用全卷积resnet对每一个边界框中的人物的主要关节点做热力图并分析偏移量,最后通过热力图与偏移的融合得到关键点的精确定位。通过该算法提取了不同步态下姿态关键点时空信息,为基于视觉的步态分析系统提供了基础条件。但实验结果表明目前最高准确率的人体关键点检测算法不足以替代IMU实现步态分析与分类。但在2m之内可以观察到节律信息,证明了所提取的关键点时空信息与IMU采集的加速度信息呈现较高相关度,为基于视觉的异常步态分类算法铺平了道路

    Longitudinal tracking of physiological state with electromyographic signals.

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    Electrophysiological measurements have been used in recent history to classify instantaneous physiological configurations, e.g., hand gestures. This work investigates the feasibility of working with changes in physiological configurations over time (i.e., longitudinally) using a variety of algorithms from the machine learning domain. We demonstrate a high degree of classification accuracy for a binary classification problem derived from electromyography measurements before and after a 35-day bedrest. The problem difficulty is increased with a more dynamic experiment testing for changes in astronaut sensorimotor performance by taking electromyography and force plate measurements before, during, and after a jump from a small platform. A LASSO regularization is performed to observe changes in relationship between electromyography features and force plate outcomes. SVM classifiers are employed to correctly identify the times at which these experiments are performed, which is important as these indicate a trajectory of adaptation

    A survey on bio-signal analysis for human-robot interaction

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    The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems

    Computational Intelligence in Electromyography Analysis

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    Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

    Computational Intelligence in Healthcare

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    This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

    Intelligent Biosignal Processing in Wearable and Implantable Sensors

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    This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

    Applications of EMG in Clinical and Sports Medicine

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    This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields
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