Hybrid pedestrian positioning system using wearable inertial sensors and ultrasonic ranging

Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.

A Novel Fall Detection Framework Using Skip-DSCGAN Based on Inertial Sensor Data

With the widespread use of Internet of Things(IoT)technology in daily life and the considerable safety risks of falls for elderly individuals,research on IoT-based fall detection systems has gainedmuch attention.This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection(Skip-DSCGAN)for fall detection.The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data.A semisupervised learning approach is adopted to train the model using only activities of daily living(ADL)data,which can avoid data imbalance problems.Furthermore,a quantile-based approach is employed to determine the fall threshold,which makes the fall detection frameworkmore robust.This proposed fall detection framework is evaluated against four other generative adversarial network(GAN)models with superior anomaly detection performance using two fall public datasets(SisFall&MobiAct).The test results show that the proposed method achieves better results,reaching 96.93% and 92.75% accuracy on the above two test datasets,respectively.At the same time,the proposed method also achieves satisfactory results in terms ofmodel size and inference delay time,making it suitable for deployment on wearable devices with limited resources.In addition,this paper also compares GAN-based semisupervised learning methods with supervised learning methods commonly used in fall detection.It clarifies the advantages of GAN-based semisupervised learning methods in fall detection.

Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors

The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingfirm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new worker.Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques.Despite widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is problematic.Through the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction workers.The suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker tasks.The proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work activities.In addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed model.With an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalfindings revealed that the suggested model could accurately recognize the actions of construction workers.Furthermore,we examined the impact of window size and sensor position on the identification efficiency of the proposed method.

Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors

Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to improveHGRaccuracy.We have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.

Seismic Noise Suppression for Ground-Based Investigation of an Inertial Sensor by Suspending the Electrode Cage

Performance test of a high precise accelerometer or an inertial sensor on the ground is inevitably limited by the seismic noise. A torsion pendulum has been used to investigate the performances of an electrostatic accelerometer, where the test mass is suspended by a fiber to compensate for its weight, and this scheme demonstrates an advantage, compared with the high-voltage levitation scheme, in which the effect of the seismic noise can be suppressed for a few orders of magnitude in low frequencies. In this work, the capacitive electrode cage is proposed to be suspended by another pendulum, and theoretical analysis shows that the effects of the seismic noise can be further suppressed for more than one order by suspending the electrode cage.

Preliminary Network Centric Therapy for Machine Learning Classification of Deep Brain Stimulation Status for the Treatment of Parkinson’s Disease with a Conformal Wearable and Wireless Inertial Sensor

The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Therapy is highly relevant to the treatment of Parkinson’s disease through deep brain stimulation. Originally wearable and wireless systems for quantifying Parkinson’s disease involved the use a smartphone to quantify hand tremor. Although originally novel, the smartphone has notable issues as a wearable application for quantifying movement disorder tremor. The smartphone has evolved in a pathway that has made the smartphone progressively more cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone utilizes an inertial sensor package that is not certified for medical analysis, and the trial data access a provisional Cloud computing environment through an email account. These concerns are resolved with the recent development of a conformal wearable and wireless inertial sensor system. This conformal wearable and wireless system mounts to the hand with the profile of a bandage by adhesive and accesses a secure Cloud computing environment through a segmented wireless connectivity strategy involving a smartphone and tablet. Additionally, the conformal wearable and wireless system is certified by the FDA of the United States of America for ascertaining medical grade inertial sensor data. These characteristics make the conformal wearable and wireless system uniquely suited for the quantification of Parkinson’s disease treatment through deep brain stimulation. Preliminary evaluation of the conformal wearable and wireless system is demonstrated through the differentiation of deep brain stimulation set to “On” and “Off” status. Based on the robustness of the acceleration signal, this signal was selected to quantify hand tremor for the prescribed deep brain stimulation settings. Machine learning classification using the Waikato Environment for Knowledge Analysis (WEKA) was applied using the multilayer perceptron neural network. The multilayer perceptron neural network achieved considerable classification accuracy for distinguishing between the deep brain stimulation system set to “On” and “Off” status through the quantified acceleration signal data obtained by this recently developed conformal wearable and wireless system. The research achievement establishes a progressive pathway to the future objective of achieving deep brain stimulation capabilities that promote closed-loop acquisition of configuration parameters that are uniquely optimized to the individual through extrinsic means of a highly conformal wearable and wireless inertial sensor system and machine learning with access to Cloud computing resources.

Healthcare Algorithms by Wearable Inertial Sensors: A Survey

Wearable smart devices, such as smart watch, wristband are becoming increasingly popular recently. They generally integrate the MEMS-designed inertial sensors, including accelerometer, gyroscope and compass, which provide a convenient and inexpensive way to collect motion data of users. Such rich, continuous motion data provide great potential for remote healthcare and decease diagnosis. Information processing algorithms play the critical role in these approaches, which is to extract the motion signatures and to access different kinds of judgements. This paper reviews key algorithms in these areas. In particular, we focus on three kinds of applications: 1) gait analysis; 2) fall detection and 3) sleep monitoring. They are the most popular healthcare applications based on the inertial data. By categorizing and introducing the key algorithms, this paper tries to build a clear map of how the inertial data are processed; how the inertial signatures are defined, extracted, and utilized in different kinds of applications. This will provide a valuable guidance for users to understand the methodologies and to select proper algorithm for specifi c application purpose.

Distinction of an Assortment of Deep Brain Stimulation Parameter Configurations for Treating Parkinson’s Disease Using Machine Learning with Quantification of Tremor Response through a Conformal Wearable and Wireless Inertial Sensor

Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Imperative for the deep brain stimulation parameter optimization process is the quantification of response feedback. As a significant improvement to traditional ordinal scale techniques is the advent of wearable and wireless systems. Recently conformal wearable and wireless systems with a profile on the order of a bandage have been developed. Previous research endeavors have successfully differentiated between deep brain stimulation “On” and “Off” status through quantification using wearable and wireless inertial sensor systems. However, the opportunity exists to further evolve to an objectively quantified response to an assortment of parameter configurations, such as the variation of amplitude, for the deep brain stimulation system. Multiple deep brain stimulation amplitude settings are considered inclusive of “Off” status as a baseline, 1.0 mA, 2.5 mA, and 4.0 mA. The quantified response of this assortment of amplitude settings is acquired through a conformal wearable and wireless inertial sensor system and consolidated using Python software automation to a feature set amenable for machine learning. Five machine learning algorithms are evaluated: J48 decision tree, K-nearest neighbors, support vector machine, logistic regression, and random forest. The performance of these machine learning algorithms is established based on the classification accuracy to distinguish between the deep brain stimulation amplitude settings and the time to develop the machine learning model. The support vector machine achieves the greatest classification accuracy, which is the primary performance parameter, and K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.

Implementation of Machine Learning Classification Regarding Hemiplegic Gait Using an Assortment of Machine Learning Algorithms with Quantification from Conformal Wearable and Wireless Inertial Sensor System

The quantification of gait is uniquely facilitated through the conformal wearable and wireless inertial sensor system, which consists of a profile comparable to a bandage. These attributes advance the ability to quantify hemiplegic gait in consideration of the hemiplegic affected leg and unaffected leg. The recorded inertial sensor data, which is inclusive of the gyroscope signal, can be readily transmitted by wireless means to a secure Cloud. Incorporating Python to automate the post-processing of the gyroscope signal data can enable the development of a feature set suitable for a machine learning platform, such as the Waikato Environment for Knowledge Analysis (WEKA). An assortment of machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and na&#239ve Bayes, were evaluated in terms of classification accuracy and time to develop the machine learning model. The K-nearest neighbors achieved optimal performance based on classification accuracy achieved for differentiating between the hemiplegic affected leg and unaffected leg for gait and the time to establish the machine learning model. The achievements of this research endeavor demonstrate the utility of amalgamating the conformal wearable and wireless inertial sensor with machine learning algorithms for distinguishing the hemiplegic affected leg and unaffected leg during gait.

Automatic modeling algorithm of stochastic error for inertial sensors

This paper proposes an automatic algorithm to determine the properties of stochastic processes and their parameters for inertial error. The proposed approach is based on a recently developed method called the generalized method of wavelet moments (GMWM), whose estimator was proven to be consistent and asymptotically normally distributed. This algorithm is suitable mainly (but not only) for the combination of several stochastic processes, where the model identification and parameter estimation are quite difficult for the traditional methods, such as the Allan variance and the power spectral density analysis. This algorithm further explores the complete stochastic error models and the candidate model ranking criterion to realize automatic model identification and determination. The best model is selected by making the trade-off between the model accuracy and the model complexity. The validation of this approach is verified by practical examples of model selection for MEMS-IMUs (micro-electro-mechanical system inertial measurement units) in varying dynamic conditions.

Error Model of Rotary Ring Laser Gyro Inertial Navigation System

To improve the precision of inertial navigation system（INS） during long time operation,the rotation modulated technique（RMT） was employed to modulate the errorr of the inertial sensors into periodically varied signals,and,as a result,to suppress the divergence of INS errors.The principle of the RMT was introduced and the error propagating functions were derived from the rotary navigation equation.Effects of the measurement error for the rotation angle of the platform on the system precision were analyzed.The simulation and experimental results show that the precision of INS was ① dramatically improved with the use of the RMT,and ② hardly reduced when the measurement error for the rotation angle was in arc-second level.The study results offer a theoretical basis for engineering design of rotary INS.

A Quaternion Scaled Unscented Kalman Estimator for Inertial Navigation States Determination Using INS/GPS/Magnetometer Fusion

This Inertial Navigation System (INS), Global Positioning System (GPS) and fluxgate magnetometer technologies have been widely used in a variety of positioning and navigation applications. In this paper, a low cost solid state INS/GPS/Magnetometer integrated navigation system has been developed that incorporates measurements from an Inertial Navigation System (INS), Global Positioning System (GPS) and fluxgate magnetometer (Mag.) to provide a reliable complete navigation solution at a high output rate. The body attitude estimates, especially the heading angle, are fundamental challenges in a navigation system. Therefore targeting accurate attitude estimation is considered a significant contribution to the overall navigation error. A better estimation of the body attitude estimates leads to more accurate position and velocity estimation. For that end, the aim of this research is to exploit the magnetometer and accelerometer data in the attitude estimation technique. In this paper, a Scaled Unscented Kalman Filter (SUKF) based on the quaternion concept is designed for the INS/GPS/Mag integrated navigation system under large attitude error conditions. Simulation and experimental results indicate a satisfactory performance of the newly developed model.

基于图优化的GNSS/双目视觉/惯性SLAM系统开发及应用

为提高机器人室外长航时定位精度,提出一种基于图优化的全球导航卫星系统(GNSS)/双目视觉/惯性同时定位与建图(SLAM)系统开发及应用。将空间中的线特征作为几何约束的补充,集成至前端的特征提取及后端的位姿优化线程,提升位姿解算精度。同时,以因子图构建联合优化的图结构,并推导出全局观测误差模型。近200 m的BullDog-CX机器人巡检结果表明,所提算法相比于VINSFusion和PL-VINS分别取得约12.6%及3.4%的定位精度提升,为室外机器人长航时导航提供了一种可行方案。

基于改进储备池计算的高精度扭秤动力学状态预测方法

在空间引力波探测任务中,超高精度惯性传感器在入轨之前必须经过地面测试与评价,悬丝扭秤是地面测试的首选装置。为了获得悬丝扭秤的精确动力学模型,以减小其固有系统误差,提出了一种基于改进储备池计算(RC)的扭秤动力学预测模型。所提模型利用注意力机制强化时间序列数据的长期依赖特征,并通过贝叶斯优化算法定位模型最优超参数空间,提高了动力学模型的预测精度。在实验室收集的扭秤转角时序数据上验证了所提方法的有效性,相较于传统RC的模型预测误差平均降低40%以上,可以为惯性传感器的地面测试提供可靠的动力学参考。

激光雷达SLAM算法综述

即时定位与地图构建(simultaneous localization and mapping,SLAM)是自主移动机器人和自动驾驶的关键技术之一,而激光雷达则是支撑SLAM算法运行的重要传感器。基于激光雷达的SLAM算法,对激光雷达SLAM总体框架进行介绍,详细阐述前端里程计、后端优化、回环检测、地图构建模块的作用并总结所使用的算法;按由2D到3D,单传感器到多传感器融合的顺序,对经典的具有代表性的开源算法进行描述和梳理归纳;介绍常用的开源数据集,以及精度评价指标和测评工具;从深度学习、多传感器融合、多机协同和鲁棒性研究四个维度对激光雷达SLAM技术的发展趋势进行展望。

基于IMU与激光雷达融合的无人弹药补给车SLAM系统研究

针对单一传感器建图精度低、实时性不足的问题,将IMU融合到激光雷达SLAM算法中。首先,采用手眼标定方法对2种传感器坐标系外参进行标定,实现传感器在时间与空间上的对齐。然后,结合因子图优化模型,解决在建图过程中产生的漂移现象,并将IMU融合到激光雷达LeGO-LOAM算法中。最后,在室外场景下搭建了无人弹药补给车SLAM实验平台,分别进行了LeGO-LOAM算法融合IMU前后的建图和定位试验。结果表明,融合IMU后的SLAM算法建图和定位精度都明显提高,满足了在未知环境下无人弹药补给车建图和定位的性能要求。

惯性传感器测试质量质心测量装置及方法研究

惯性传感器是空间引力波探测的核心载荷,惯性传感器内的测试质量是惯性和测量基准。测试质量质心、形心偏移会产生杂散力噪声,空间引力波探测对测试质量质心、形心偏移的技术指标要求小于3.75μm。由于需要获得测试质量精确的质心位置,并且商用质心测量装置无法满足测试质量质心测量精度需求,因此设计了一种基于五线摆的新型质心测量装置,根据测得的五线摆自由振动频率得到测试质量质心位置。实验结果表明,质心测量精度优于±1μm,满足空间引力波探测惯性传感器测试质量质心测量的精度需求。

引力波探测中惯性传感器的残余气体动力学特性机理研究

作为空间引力波探测的核心系统,惯性传感器在轨运行时的噪声干扰诱发机制解析是迫切需要解决的关键科学问题。惯性传感器中检验质量(TM)的残余加速度噪声是限制探测器灵敏度的最大噪声源。作为残余气体噪声的直接表征手段,惯性传感器复杂拓扑构型下气体分子逃逸时间动态特性解析亟待解决。文章首先考虑了狭小空间下检验质量块与周围壁面间的材料属性、放气特性等差异性,建立了异质空间复杂拓扑结构下的惯性传感器数理模型;其次解析出残余气体噪声与气体分子逃逸时间之间的定量约束关系,提取了逃逸时间核心影响因素;随后以蒙特卡洛模拟技术为切入点,得到约束条件下气体分子从板间扩散出去所需的逃逸时间及碰撞次数的模拟解。文章研究发现:不同气体成分、壁面性质对残余气体噪声有较大影响;考虑气体分子在壁面的滞留时间会大幅增加逃逸时间、降低碰撞频率。

惯性传感器在老年环卫工人跌倒风险评估中的应用

我国乡镇的环卫工人大多年龄较大,其工作常常需要清扫崎岖不平和雨后湿滑的路面,存在较高跌倒风险。为提升环卫工人群体的安全作业水平,开展了基于惯性传感器的跌倒风险评估研究。招募了18名被试者开展试验研究。首先开展动态步态指数(Dynamic Gait Index,DGI)评估,确认每位被试者的跌倒风险程度,作为样本标签。5枚惯性传感器用于采集被试者作业相关动作的加速度数据。采用机器学习分类器开发分类模型。经过训练和优化后,右脚踝处加速度数据训练的支持向量机分类器整体性能最好(准确率为88.62%,F1值为90.00%,AUC为89.12%)。研究表明,开发的跌倒风险评估模型能够较好地实现对高跌倒风险老年环卫工人样本的识别与评估。基于较低成本的惯性传感技术的跌倒风险评估模型有利于在老年环卫工群体中推广应用,提高该群体的作业安全水平。

基于平面特征的LIO增强GNSS RTK定位性能研究

针对应用单一传感器的智能导航定位技术在城市复杂环境下难以满足准确、无缝和稳定性能要求的问题,提出一种基于平面特征的雷达惯性里程计(LIO)增强全球卫星导航系统(GNSS)实时动态定位技术(RTK)定位性能的方法:通过滑动窗口构建多帧激光雷达(LiDAR)平面特征的关联;并将RTK与LIO在位置域层面直接融合;最后在开阔环境和城市环境中进行实验。结果表明,RTK/惯性导航系统(INS)/LiDAR组合系统能够在GNSS挑战环境中达到分米级精度;同时,在LiDAR平面特征的约束下,该方法速度和姿态的估计性能也可得到改善。