Kinematic calibration under the expectation maximization framework for exoskeletal inertial motion capture system

This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.

Movement Function Assessment Based on Human Pose Estimation from Multi-View

Human pose estimation is a basic and critical task in the field of computer vision that involves determining the position(or spatial coordinates)of the joints of the human body in a given image or video.It is widely used in motion analysis,medical evaluation,and behavior monitoring.In this paper,the authors propose a method for multi-view human pose estimation.Two image sensors were placed orthogonally with respect to each other to capture the pose of the subject as they moved,and this yielded accurate and comprehensive results of three-dimensional(3D)motion reconstruction that helped capture their multi-directional poses.Following this,we propose a method based on 3D pose estimation to assess the similarity of the features of motion of patients with motor dysfunction by comparing differences between their range of motion and that of normal subjects.We converted these differences into Fugl–Meyer assessment(FMA)scores in order to quantify them.Finally,we implemented the proposed method in the Unity framework,and built a Virtual Reality platform that provides users with human–computer interaction to make the task more enjoyable for them and ensure their active participation in the assessment process.The goal is to provide a suitable means of assessing movement disorders without requiring the immediate supervision of a physician.

基于Leap motion 的大学物理实验虚拟课堂设计

通过利用手势控制器(Leap Motion)技术,设计并实施一套创新的大学物理虚拟实验课程.通过在Unity3D软件中整合Leap Motion,创建一个大学物理实验测量杨氏模量实验场景,主要是利用C#语言进行开发,通过设计出多个模块,包括主界面设计和基本实验组件来完成物理实验的设计.在Leap Motion官网上下载关于Unity3D的SDK资源包并且导入Unity3D中,用其建立手部模型,实现Leap Motion控制器与Unity3D的手部交互实验系统的设计.人机交互技术的引入不仅可以丰富大学物理实验的教学手段,而且还能提高学生的学科理解和实际操作能力,同时培养其创新思维和科技素养.

Robotic Etiquette:Socially Acceptable Navigation of Service Robots with Human Motion Pattern Learning and Prediction

Nonverbal and noncontact behaviors play a significant role in allowing service robots to structure their interactions withhumans.In this paper, a novel human-mimic mechanism of robot’s navigational skills was proposed for developing sociallyacceptable robotic etiquette.Based on the sociological and physiological concerns of interpersonal interactions in movement,several criteria in navigation were represented by constraints and incorporated into a unified probabilistic cost grid for safemotion planning and control, followed by an emphasis on the prediction of the human’s movement for adjusting the robot’spre-collision navigational strategy.The human motion prediction utilizes a clustering-based algorithm for modeling humans’indoor motion patterns as well as the combination of the long-term and short-term tendency prediction that takes into accountthe uncertainties of both velocity and heading direction.Both simulation and real-world experiments verified the effectivenessand reliability of the method to ensure human’s safety and comfort in navigation.A statistical user trials study was also given tovalidate the users’favorable views of the human-friendly navigational behavior.

Superflexible and Lead-Free Piezoelectric Nanogenerator as a Highly Sensitive Self-Powered Sensor for Human Motion Monitoring

For traditional piezoelectric sensors based on poled ceramics,a low curie tem-perature(T_(c))is a fatal flaw due to the depolarization phenomenon.However,in this study,we find the low T_(c) would be a benefit for flex-ible piezoelectric sensors because small alterations of force trig-ger large changes in polarization.BaTi_(0.88)Sn_(0.12)O_(3)(BTS)with high piezoelectric coefficient and low T_(c) close to human body temperature is taken as an example for materials of this kind.Continuous piezo-electric BTS films were deposited on the flexible glass fiber fabrics(GFF),self-powered sensors based on the ultra-thin,superflexible,and polarization-free BTS-GFF/PVDF composite piezoelectric films are used for human motion sensing.In the low force region(1-9 N),the sensors have the outstanding performance with voltage sensitivity of 1.23 V N^(−1) and current sensitivity of 41.0 nA N^(−1).The BTS-GFF/PVDF sensors can be used to detect the tiny forces of falling water drops,finger joint motion,tiny surface deformation,and fatigue driving with high sensitivity.This work provides a new paradigm for the preparation of superflexible,highly sensitive and wearable self-powered piezoelectric sensors,and this kind of sensors will have a broad application prospect in the fields of medical rehabilitation,human motion monitoring,and intelligent robot.

Dataset of human motion status using IR-UWB through-wall radar

Ultra-wideband(UWB)through-wall radar has a wide range of applications in non-contact human information detection and monitoring.With the integration of machine learning technology,its potential prospects include the physiological monitoring of patients in the hospital environment and the daily monitoring at home.Although many target detection methods of UWB through-wall radar based on machine learning have been proposed,there is a lack of an opensource dataset to evaluate the performance of the algorithm.This published dataset is measured by impulse radio UWB(IR-UWB)through-wall radar system.Three test subjects are measured in different environments and several defined motion status.Using the presented dataset,we propose a human-motion-status recognition method using a convolutional neural network(CNN),and the detailed dataset partition method and the recognition process flow are given.On the well-trained network,the recognition accuracy of testing data for three kinds of motion status is higher than 99.7%.The dataset presented in this paper considers a simple environment.Therefore,we call on all organizations in the UWB radar field to cooperate to build opensource datasets to further promote the development of UWB through-wall radar.

Design and Analysis of a Mechanical Device to Harvest Energy from Human Footstep Motion

Portable electronics is usually powered by battery,which is not sustainable not only to the longtime outdoor use but also to our living environment.There is rich kinetic energy in footstep motion during walking,so it is ideal to harvest the kinetic energy from human footstep motion as power source for portable electronic devices.In this paper,a novel mechanism based on dual-oscillating mode is designed to harvest the kinetic energy from footstep motion.The harvester contains two oscillating sub-mechanisms：one is spring-mass oscillator to absorb the vibration from external excitation,i.e.,the footstep motion,and the other is cantilever beam with tip mass for amplifying the vibration.Theoretic analysis shows that the dual-oscillating mechanism can be more effectively harness the foot step motion.The energy conversion sub-mechanism is based on the electromagnetic induction,where the wire coils fixed at the tip end of the cantilever beam serves as the slider and permanent magnets and yoke form the changing magnetic field.Simulation shows that the harvester,with total mass 70 g,can produce about 100 mW of electricity at the walking speed of 2 steps per second.

Textile-Based Strain Sensor for Human Motion Detection

Human motion analysis consists of real-time monitoring and recording of human body’s kinematics. It is very essential to track ambulatory and dailylife human motion, which is crucial for many applications and disciplines.Electronic textiles(e-textiles) afford a valid alternative to traditional solidstate sensors due to their merits of low cost, lightweight, flexibility, and feasibility to fit various human bodies. In this mini-review, textile-based sensor platforms and human motion analysis are well discussed in Section 1.Second, theoretical principles of textile-based strain sensors are introduced including resistive, capacitive, and piezoelectrical sensors. Section 3 focuses on various types of textile materials that are functionalized as sensing systems by intrinsic or extrinsic modifications. Section 4 summaries various types of e-textile-based strain sensors for human motion analysis. The final two sections mainly present perspectives and challenges, and conclusions,respectively.

Investigation of human motion effects on 60GHz indoor office propagation

A modified random walk model for human motion is proposed to investigate characteristics of 60GHz indoor office propagation.Compared with the classic random walk model,the movement tendency in the walking process is taken into account in the modified model.Based on the proposed model,path gains of the propagation environment are simulated under a variety of settings by using a ray tracing method.Simulation results and analysis show that human motion is a major source of disturbance to the indoor office propagation and results in performance degradation in some areas.

Key Pose Frame Extraction Method of Human Motion Based on 3D Framework and X-Means

The key pose frames of a human motion pose sequence,play an important role in the compression,retrieval and semantic analysis of continuous human motion.The current available clustering methods in literatures are difficult to determine the number of key pose frames automatically,and may destroy the postures’ temporal relationships while extracting key frames.To deal with this problem,this paper proposes a new key pose frames extraction method on the basis of 3D space distances of joint points and the improved X-means clustering algorithm.According to the proposed extraction method,the final key pose frame sequence could be obtained by describing the posture of human body with space distance of particular joint points and then the time-constraint X-mean algorithm is applied to cluster and filtrate the posture sequence.The experimental results show that the proposed method can automatically determine the number of key frames and save the temporal characteristics of motion frames according to the motion pose sequence.

Human Motion Recognition Based on Incremental Learning and Smartphone Sensors

Batch processing mode is widely used in the training process of human motiun recognition. After training, the motion elassitier usually remains invariable. However, if the classifier is to be expanded, all historical data must be gathered for retraining. This consumes a huge amount of storage space, and the new training process will be more complicated. In this paper, we use an incremental learning method to model the motion classifier. A weighted decision tree is proposed to help illustrate the process, and the probability sampling method is also used. The resuhs show that with continuous learning, the motion classifier is more precise. The average classification precision for the weighted decision tree was 88.43% in a typical test. Incremental learning consumes much less time than the batch processing mode when the input training data comes continuously.

STTG-net:a Spatio-temporal network for human motion prediction based on transformer and graph convolution network

In recent years,human motion prediction has become an active research topic in computer vision.However,owing to the complexity and stochastic nature of human motion,it remains a challenging problem.In previous works,human motion prediction has always been treated as a typical inter-sequence problem,and most works have aimed to capture the temporal dependence between successive frames.However,although these approaches focused on the effects of the temporal dimension,they rarely considered the correlation between different joints in space.Thus,the spatio-temporal coupling of human joints is considered,to propose a novel spatio-temporal network based on a transformer and a gragh convolutional network(GCN)(STTG-Net).The temporal transformer is used to capture the global temporal dependencies,and the spatial GCN module is used to establish local spatial correlations between the joints for each frame.To overcome the problems of error accumulation and discontinuity in the motion prediction,a revision method based on fusion strategy is also proposed,in which the current prediction frame is fused with the previous frame.The experimental results show that the proposed prediction method has less prediction error and the prediction motion is smoother than previous prediction methods.The effectiveness of the proposed method is also demonstrated comparing it with the state-of-the-art method on the Human3.6 M dataset.

Human Motion Recognition Using Ultra-Wideband Radar and Cameras on Mobile Robot

Cameras can reliably detect human motions in a normal environment,but they are usually affected by sudden illumination changes and complex conditions,which are the major obstacles to the reliability and robustness of the system.To solve this problem,a novel integration method was proposed to combine bi-static ultra-wideband radar and cameras.In this recognition system,two cameras are used to localize the object's region,regions while a radar is used to obtain its 3D motion models on a mobile robot.The recognition results can be matched in the 3D motion library in order to recognize its motions.To confirm the effectiveness of the proposed method,the experimental results of recognition using vision sensors and those of recognition using the integration method were compared in different environments.Higher correct-recognition rate is achieved in the experiment.

Impact of human motion on TVOCs inhalation dose under side re-circulated ventilation

The main objectives were to (1) calculate the total volatile organic compounds (TVOCs) inhalation dose, (2) analyze the proportions of human’s inhaled contaminant dose from different sources, and (3) present a newly defined ratio of relative inhalation dose level (RIDL) to assess indoor air quality (IAQ). A user defined function based on CFD (computational fluid dynamics) was developed, which integrated human motion model with TVOCs emission model in a high sidewall air supply ventilation mode. Based on simulation results of 10 cases, it is shown that the spatial concentration distribution of TVOCs is affected by human motion. TVOCs diffusion characteristic of building material is the most effective way to impact the TVOCs inhalation dose. From the RIDL index, case A-2 has the most serious IAQ problem, while case D-1 is of the best IAQ.

Segmental Kinematic Coupling of the Human Spinal Column during Locomotion

As one of the most important daily motor activities, human locomotion has been investigated intensively in recent decades. The locomotor functions and mechanics of human lower limbs have become relatively well understood. However, so far our understanding of the motions and functional contributions of the human spine during locomotion is still very poor and simultaneous in-vivo limb and spinal column motion data are scarce. The objective of this study is to investigate the delicate in-vivo kinematic coupling between different functional regions of the human spinal column during locomotion as a stepping stone to explore the locomotor function of the human spine complex. A novel infrared reflective marker cluster system was constrncted using stereophotogrammetry techniques to record the 3D in-vivo geometric shape of the spinal column and the segmental position and orientation of each functional spinal region simultaneously. Gait measurements of normal walking were conducted. The preliminary results show that the spinal column shape changes periodically in the frontal plane during locomotion. The segmental motions of different spinal functional regions appear to be strongly coupled, indicating some synergistic strategy may be employed by the human spinal column to facilitate locomotion. In contrast to traditional medical imaging-based methods, the proposed technique can be used to investigate the dynamic characteristics of the spinal column, hence providing more insight into the functional biomechanics of the human spine.

A Multi-Task Motion Generation Model that Fuses a Discriminator and a Generator

The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spatial-temporal structures,and the deep learning model can fully describe the potential semantic structure of human motion.To improve the authenticity of the generated human motion sequences,we propose a multi-task motion generation model that consists of a discriminator and a generator.The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom degrees.And target motion sequences are created with these styles by the generator.Unlike traditional related works,our model can handle multiple tasks,such as identifying styles and generating data.In addition,by extracting 17 crucial joints from 29 human joints,our model avoids data redundancy and improves the accuracy of model recognition.The experimental results show that the discriminator of the model can effectively recognize diversified movements,and the generated data can correctly fit the actual data.The combination of discriminator and generator solves the problem of low reuse rate of motion data,and the generated motion sequences are more suitable for actual movement.

Combining Multi-scale Directed Depth Motion Maps and Log-Gabor Filters for Human Action Recognition

Recognition of the human actions by computer vision has become an active research area in recent years. Due to the speed and the high similarity of the actions, the current algorithms cannot get high recognition rate. A new recognition method of the human action is proposed with the multi-scale directed depth motion maps(MsdDMMs) and Log-Gabor filters. According to the difference between the speed and time order of an action, MsdDMMs is proposed under the energy framework. Meanwhile, Log-Gabor is utilized to describe the texture details of MsdDMMs for the motion characteristics. It can easily satisfy both the texture characterization and the visual features of human eye. Furthermore, the collaborative representation is employed as action recognition by the classification. Experimental results show that the proposed algorithm, which is applied in the MSRAction3 D dataset and MSRGesture3 D dataset, can achieve the accuracy of 95.79% and 96.43% respectively. It also has higher accuracy than the existing algorithms, such as super normal vector(SNV), hierarchical recurrent neural network(Hierarchical RNN).

面向人体运动检测的水凝胶基柔性应变传感器设计

在可穿戴电子设备迅猛发展的背景下,构建面向人体运动检测的可穿戴柔性应变传感器成为目前传感器研究的重点之一。基于聚丙烯酰胺(PAM)、纤维素纳米晶(CNCs)、单宁酸(TA)构建了一种导电水凝胶基柔性应变传感器。机械性能与电学性能测试结果表明,相较于其他柔性应变传感器,聚丙烯酰胺-纤维素纳米晶-单宁酸导电水凝胶基柔性应变传感器具有超长的拉伸范围(0%~2 900%)、较好的黏附性、快速的响应时间(280 ms)和良好的稳定性(超320次加载-卸载循环)。该传感器对人体关节弯曲的大应变与喉部活动的小应变等运动信号具备良好的检测功能,可用作人体运动检测的柔性可穿戴传感器。

基于肌电−惯性融合的人体运动估计:高斯滤波网络方法

本文研究了基于肌电(Electromyography,EMG)−惯性融合的人体运动估计问题,提出了一种序贯渐进高斯滤波网络(Sequential progressive Gaussian filtering network,SPGF-net)估计方法来形成肌电和惯性的互补性优势,以提高人体运动估计精度和稳定性.首先,利用卷积神经网络对观测数据进行特征提取,以及利用长短期记忆(Long short-term memory,LSTM)网络模型来学习噪声统计特性和量测模型.其次,采用序贯融合的方式融合异构传感器量测特征,以建立高斯滤波与深度学习相结合的网络模型来实现人体运动估计.特别地,引入渐进量测更新对网络量测特征的不确定性进行补偿.最后,通过实验结果表明,相比于现有的卡尔曼滤波网络,该融合方法在上肢关节角度估计中的均方根误差(Root mean square error,RMSE)下降了13.8%,相关系数(R^(2))提高了4.36%.

多矢量姿态测量算法研究综述

将矢量测量转化为姿态测量,该问题在姿态估计中被称为Wahba问题。介绍了最小化Wahba问题的损失函数的不同算法,包括最优四元数估计器(ESOQ)、四元数估计器(QUEST)、快速最优姿态矩阵(FOAM)、矩阵的奇异值分解(SVD)等广泛使用的算法,以及基于快速线性四元数姿态估计器(FLAE)、黎曼流形等近年来提出的算法。给出了简单的计算原理与推导过程,并通过计算机仿真对照,归纳各类算法在计算旋转矩阵时的计算精度、鲁棒性。针对姿态测量算法应对当前应用场景的性能需求,简要介绍其在惯性测量单元(IMU)动态对准、协同集群视觉定姿、图像拼接等方面的适用可能和工作原理,并在此基础上简述了姿态测量算法当前的缺陷与未来的发展趋势。