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

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

Gait as a predictor for cognitive decline in Parkinson's disease

PhD ThesisCognitive decline and dementia are core features of Parkinson’s disease (PD) with major personal and socioeconomic impact. Identifying individuals at risk of cognitive decline and dementia is vital in order to optimise clinical management and develop novel therapeutics. However, biomarkers for cognitive decline remain a major unmet need. A large structured review undertaken as part of this thesis revealed discrete gait characteristics predicted cognitive decline and dementia in older adults but to date no such study has been conducted in PD. Thus, the primary aim of this thesis was to investigate gait as a clinical biomarker for cognitive decline in PD. Newly diagnosed PD participants (n=118) and controls (n=184) completed a detailed quantitative gait assessment under single and dual task conditions at baseline. Additionally, a comprehensive battery of neuropsychological assessments were completed at baseline, 18 and 36 months later. Mixed-effects models identified significant gait predictors of cognitive decline over three years. Baseline cognition was also explored as a predictor for cognitive decline. Finally, gait was collected in the free-living environment using a body-worn monitor (BWM) and cross-sectional analysis explored free-living gait-cognition associations. Original contributions to knowledge were that gait characteristics under single and dual task in an incident cohort of PD predicted decline in discrete cognitive domains over three years. Critically, in comparison to gait, baseline neuropsychological assessment performance did not predict cognitive decline. Additionally, cross-sectional analysis in early PD revealed discrete gait-cognition associations in free-living signifying future clinical utility for gait as a clinical biomarker. This thesis provides the first evidence for gait as a clinical biomarker for cognitive decline in PD. Discrete gait characteristics may provide a low cost clinical biomarker and make an important contribution to prognostic models of dementia risk in PD

Human Movement Variability, Nonlinear Dynamics, and Pathology: Is There A Connection?

Fields studying movement generation, including robotics, psychology, cognitive science, and neuroscience utilize concepts and tools related to the pervasiveness of variability in biological systems. The concept of variability and the measures for nonlinear dynamics used to evaluate this concept open new vistas for research in movement dysfunction of many types. This review describes innovations in the exploration of variability and their potential importance in understanding human movement. Far from being a source of error, evidence supports the presence of an optimal state of variability for healthy and functional movement. This variability has a particular organization and is characterized by a chaotic structure. Deviations from this state can lead to biological systems that are either overly rigid and robotic or noisy and unstable. Both situations result in systems that are less adaptable to perturbations, such as those associated with unhealthy pathological states or absence of skillfulness

Machine Learning for Gait Classification

Machine learning is a powerful tool for making predictions and has been widely used for solving various classification problems in last decades. As one of important applications of machine learning, gait classification focuses on distinguishing different gait patterns by investigating the quality of gait of individuals and categorizing them as belonging to particular classes. The most studied gait pattern classes are the normal gait patterns of healthy people, i.e., gait of people who do not have any gait disability caused by an illness or an injury, and the pathological gait of patients suffering from illnesses which cause gait disorders such as neurodegenerative diseases (NDDs). There has been significant research work trying to solve the gait classification problems using advanced machine learning techniques, as the results may be beneficial for the early detection of underlined NDDs and for the monitoring of the gait rehabilitation progress. Despite the huge development in the field of gait analysis and classification, there are still a number of challenges open to further research. One challenge is the optimization of applied machine learning strategies to achieve better classification results. Another challenge is to solve gait classification problems even in the case when only limited amount of data are available. Further, a challenge is the development of machine learning-based methods that could provide more precise results to evaluate the level of gait quality or gait disorder, in contrast of just classifying gait pattern as belonging to healthy or pathological gait. The focus of this thesis is on the development, implementation and evaluation of a novel and reliable solution for the complex gait classification problems by addressing the current challenges. This solution is presented as a classification framework that can be applied to different types of gait signals, such as lower-limbs joint angle signals, trunk acceleration signals, and stride interval signals. Developed framework incorporates a hybrid solution which combines two models to enhance the classification performance. In order to provide a large number of samples for training the models, a sample generation method is developed which could segments the gait signals into smaller fragments. Classification is firstly performed on the data sample level, and then the results are utilized to generate the subject-level results using a majority voting scheme. Besides the class labels, a confidence score is computed to interpret the level of gait quality. In order to significantly improve the gait classification performances, in this thesis a novel feature extraction methods are also proposed using statistical methods, as well as machine learning approaches. Gaussian mixture model (GMM), least square regression, and k-nearest neighbors (kNN) are employed to provide additional significant features. Promising classification results are achieved using the proposed framework and the extracted features. The framework is ultimately applied to the management of patients and their rehabilitation, and is proved to be feasible in many clinical scenarios, such as the evaluation of medication effect on Parkinsona s disease (PD) patientsa gait, the long-term gait monitoring of the hereditary spastic paraplegia (HSP) patient under physical therapy

The therapeutic contributions of somatosensory feedback during exercise for those with Parkinson\u27s disease

Previous research has proposed that the somatosensory feedback generated during exercise is a key component in regards to the mechanism underlying the therapeutic effects of exercise on the motor symptoms of Parkinson’s disease (PD). This thesis aimed to further examine the contributions of different forms of somatosensory feedback during exercise in PD in order to understand the mechanism for symptom improvements that certain exercise studies report. This randomized, controlled exercise study consisted of three treadmill groups, with the RATE and MAGNITUDE groups serving as the experimental conditions, while the CONTROL condition was an active comparator treadmill walking group. The RATE group attempted to elicit a rapid sampling rate from somatosensory afferents by having participants walk at a high cadence. The MAGNITUDE group attempted to generate a signal from somatosensory receptors that was larger or richer in magnitude by having participants wear ankle weights with the premise that the additional weight would cause tension sensitive golgi tendon organs to increase signaling. The CONTROL treadmill group served as an active comparator control group where participants walked regularly. Each condition finished with 13 participants with idiopathic PD. All treadmill groups trained at the same aerobic intensity, duration, and frequency. however, only the RATE group improved in the primary outcome measure (motor section of the Unified Parkinson’s Disease Rating Scale (UPDRS-III)) after exercise. Furthermore, this same condition improved on the upper limb score of the UPDRS-III, possibly indicative of an overall improvement in basal ganglia (BG) functioning. Main effects of time were reported for step length in velocity across all treadmill training groups during both self-paced and maximal walking speeds. No changes in any measures of postural control were detected. This study demonstrates that exercise that generates a high rate of somatosensory feedback from appears to be the most capable of improving motor symptoms of PD. Furthermore, gait improvements from treadmill training were independent of improvements in UPDRS-III, and are likely an effect of motor learning

Human Gait Model Development for Objective Analysis of Pre/Post Gait Characteristics Following Lumbar Spine Surgery

Although multiple advanced tools and methods are available for gait analysis, the gait and its related disorders are usually assessed by visual inspection in the clinical environment. This thesis aims to introduce a gait analysis system that provides an objective method for gait evaluation in clinics and overcomes the limitations of the current gait analysis systems. Early identification of foot drop, a common gait disorder, would become possible using the proposed methodology

Diagnosis and Treatment of Parkinson's Disease

Parkinson's disease is diagnosed by history and physical examination and there are no laboratory investigations available to aid the diagnosis of Parkinson's disease. Confirmation of diagnosis of Parkinson's disease thus remains a difficulty. This book brings forth an update of most recent developments made in terms of biomarkers and various imaging techniques with potential use for diagnosing Parkinson's disease. A detailed discussion about the differential diagnosis of Parkinson's disease also follows as Parkinson's disease may be difficult to differentiate from other mimicking conditions at times. As Parkinson's disease affects many systems of human body, a multimodality treatment of this condition is necessary to improve the quality of life of patients. This book provides detailed information on the currently available variety of treatments for Parkinson's disease including pharmacotherapy, physical therapy and surgical treatments of Parkinson's disease. Postoperative care of patients of Parkinson's disease has also been discussed in an organized manner in this text. Clinicians dealing with day to day problems caused by Parkinson's disease as well as other healthcare workers can use beneficial treatment outlines provided in this book

A study of motor control in healthy subjects and in Parkinson's disease patients

Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2008.Includes bibliographical references.Parkinson's disease (PD) is a primarily motor disorder which affects at least half a million people in the US alone. Deep brain stimulation (DBS) is a neurosurgical intervention by which neural structures are stimulated electrically by an implanted pacemaker. It has become the treatment of choice for PD, when not adequately controlled by drug therapy. We introduced a novel robotic platform for the study of the effects of DBS on motor control in PD. Subjects performed discrete wrist movements with and without a force field. We found preliminary indication that motor learning may be taking place with stimulation, and demonstrated how robotic testing can augment existing clinical tools in evaluation of the disease. To study the effect of stimulation on movement frequency, we employed a rhythmic task that required movements of the elbow to remain within a closed shape on a phase plane. Three closed shapes required varying frequency/amplitude combinations of elbow movement. The task was performed with and without visual feedback. Analysis of data from the healthy control subjects revealed a non-monotonic relation between accuracy on the phase plane and movement speed. Further kinematic analyses, including movement intermittency and harmonicity, number and type of submovements (movement primitives) fit per movement cycle, and the effects of vision on intermittency were used to support the model we propose, whereby there exist two subtypes of rhythmic movement; small-amplitude, high-frequency movements are nearly maximally harmonic, and harness the elastic properties of the limb to achieve smoothness and accuracy, and large-amplitude, low-frequency movements share characteristics with a string of discrete movements, and make use of visual feedback to achieve smoothness and accuracy.(cont.) Bradykinesia (slowness of movement) is one of the hallmarks of PD. We examined the effects of visual feedback on bradykinesia. PD patients off dopaminergic medication and healthy age-matched controls performed significantly faster movements when visual feedback was withdrawn. For the bradykinetic subjects, this increase in movement speed meant either a mitigation or an elimination of bradykinesia. Our results support a role of the basal ganglia in sensorimotor integration, and argue for the integration of nonvision exercises into patients' physical therapy regime.by Shelly Levy-Tzedek.Ph.D