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

3D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon

Gait disorder diagnosis and rehabilitation is one area where human perception and observation are highly integrated. Predominantly, gait evaluation, comprises technological devices for gait analysis such as, dedicated force sensors, cameras, and wearable sensor based solutions, however they are limited by insufficient gait parameter recognition, post processing, installation costs, mobility, and skin irritation issues. Thus, the proposed study concentrates on the creation of a widely deployable, noncontact and non-intrusive gait recognition method from impulse radio ultra wideband (IR-UWB) sensing phenomenon, where a standalone IR-UWB system can detect gait problems with less human intervention. A 3D human motion model for gait identification from IR-UWB has been proposed with embracing spherical trigonometry and vector algebra to determine knee angles. Subsequently, normal and abnormal walking subjects were involved in this study. Abnormal gait subjects belong to the spastic gait category only. The prototype has been tested in both the anechoic and multipath environments. The outcomes have been corroborated with a simultaneously deployed Kinect Xbox sensor and supported by statistical graphical approach Bland and Altman (B&A) analysis

Non-Intrusive Gait Recognition Employing Ultra Wideband Signal Detection

A self-regulating and non-contact impulse radio ultra wideband (IR-UWB) based 3D human gait analysis prototype has been modeled and developed with the help of supervised machine learning (SML) for this application for the first time. The work intends to provide a rewarding assistive biomedical application which would help doctors and clinicians monitor human gait trait and abnormalities with less human intervention in the fields of physiological examinations, physiotherapy, home assistance, rehabilitation success determination and health diagnostics, etc. The research comprises IR-UWB data gathered from a number of male and female participants in both anechoic chamber and multi-path environments. In total twenty four individuals have been recruited, where twenty individuals were said to have normal gait and four persons complained of knee pain that resulted in compensated spastic walking patterns. A 3D postural model of human movements has been created from the backscattering property of the radar pulses employing understanding of spherical trigonometry and vector fields. This subjective data (height of the body areas from the ground) of an individual have been recorded and implemented to extract the gait trait from associated biomechanical activity and differentiates the lower limb movement patterns from other body areas. Initially, a 2D postural model of human gait is presented from IR-UWB sensing phenomena employing spherical co-ordinate and trigonometry where only two dimensions such as, distance from radar and height of reflection have been determined. There are five pivotal gait parameters; step frequency, cadence, step length, walking speed, total covered distance, and body orientation which have all been measured employing radar principles and short term Fourier transformation (STFT). Subsequently, the proposed gait identification and parameter characterization has been analysed, tested and validated against popularly accepted smartphone applications with resulting variations of less than 5%. Subsequently, the spherical trigonometric model has been elevated to a 3D postural model where the prototype can determine width of motion, distance from radar, and height of reflection. Vector algebra has been incorporated with this 3D model to measure knee angles and hip angles from the extension and flexion of lower limbs to understand the gait behavior throughout the entire range of bipedal locomotion. Simultaneously, the Microsoft Kinect Xbox One has been employed during the experiment to assist in the validation process. The same vector mathematics have been implemented to the skeleton data obtained from Kinect to determine both the hip and knee angles. The outcomes have been compared by statistical graphical approach Bland and Altman (B&A) analysis. Further, the changes of knee angles obtained from the normal gaits have been used to train popular SMLs such as, k-nearest neighbour (kNN) and support vector machines (SVM). The trained model has subsequently been tested with the new data (knee angles extracted from both normal and abnormal gait) to assess the prediction ability of gait abnormality recognition. The outcomes have been validated through standard and wellknown statistical performance metrics with promising results found. The outcomes prove the acceptability of the proposed non-contact IR-UWB gait recognition to detect gait

Gait rehabilitation monitor

This work presents a simple wearable, non-intrusive affordable mobile framework that allows remote patient monitoring during gait rehabilitation, by doctors and physiotherapists. The system includes a set of 2 Shimmer3 9DoF Inertial Measurement Units (IMUs), Bluetooth compatible from Shimmer, an Android smartphone for collecting and primary processing of data and persistence in a local database. Low computational load algorithms based on Euler angles and accelerometer, gyroscope and magnetometer signals were developed and used for the classification and identification of several gait disturbances. These algorithms include the alignment of IMUs sensors data by means of a common temporal reference as well as heel strike and stride detection algorithms to help segmentation of the remotely collected signals by the System app to identify gait strides and extract relevant features to feed, train and test a classifier to predict gait abnormalities in gait sessions. A set of drivers from Shimmer manufacturer is used to make the connection between the app and the set of IMUs using Bluetooth. The developed app allows users to collect data and train a classification model for identifying abnormal and normal gait types. The system provides a REST API available in a backend server along with Java and Python libraries and a PostgreSQL database. The machine-learning type is Supervised using Extremely Randomized Trees method. Frequency, time and time-frequency domain features were extracted from the collected and processed signals to train the classifier. To test the framework a set of gait abnormalities and normal gait were used to train a model and test the classifier.Este trabalho apresenta uma estrutura móvel acessível, simples e não intrusiva, que permite a monitorização e a assistência remota de pacientes durante a reabilitação da marcha, por médicos e fisioterapeutas que monitorizam a reabilitação da marcha do paciente. O sistema inclui um conjunto de 2 IMUs (Inertial Mesaurement Units) Shimmer3 da marca Shimmer, compatíveís com Bluetooth, um smartphone Android para recolha, e pré-processamento de dados e armazenamento numa base de dados local. Algoritmos de baixa carga computacional baseados em ângulos Euler e sinais de acelerómetros, giroscópios e magnetómetros foram desenvolvidos e utilizados para a classificação e identificação de diversas perturbações da marcha. Estes algoritmos incluem o alinhamento e sincronização dos dados dos sensores IMUs usando uma referência temporal comum, além de algoritmos de detecção de passos e strides para auxiliar a segmentação dos sinais recolhidos remotamente pelaappdestaframeworke identificar os passos da marcha extraindo as características relevantes para treinar e testar um classificador que faça a predição de deficiências na marcha durante as sessões de monitorização. Um conjunto de drivers do fabricante Shimmer é usado para fazer a conexão entre a app e o conjunto de IMUs através de Bluetooth. A app desenvolvida permite aos utilizadores recolher dados e treinar um modelo de classificação para identificar os tipos de marcha normais e patológicos. O sistema fornece uma REST API disponível num servidor backend recorrendo a bibliotecas Java e Python e a uma base de dados PostgreSQL. O tipo de machine-learning é Supervisionado usando Extremely Randomized Trees. Features no domínio do tempo, da frequência e do tempo-frequência foram extraídas dos sinais recolhidos e processados para treinar o classificador. Para testar a estrutura, um conjunto de marchas patológicas e normais foram utilizadas para treinar um modelo e testar o classificador

Automatic recognition of gait patterns in human motor disorders using machine learning: A review

Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.This work was supported by the FCT - Fundação para a Ciência e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, and the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941. Also, this work was partially supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness