Ambulatory assessment of ankle and foot dynamics

Ground reaction force (GRF) measurement is important in the analysis of human body movements. The main drawback of the existing measurement systems is the restriction to a laboratory environment. This paper proposes an ambulatory system for assessing the dynamics of ankle and foot, which integrates the measurement of the GRF with the measurement of human body movement. The GRF and the center of pressure (CoP) are measured using two six-degrees-of-freedom force sensors mounted beneath the shoe. The movement of foot and lower leg is measured using three miniature inertial sensors, two rigidly attached to the shoe and one on the lower leg. The proposed system is validated using a force plate and an optical position measurement system as a reference. The results show good correspondence between both measurement systems, except for the ankle power estimation. The root mean square (RMS) difference of the magnitude of the GRF over 10 evaluated trials was (0.012 +/- 0.001) N/N (mean +/- standard deviation), being (1.1 +/- 0.1)% of the maximal GRF magnitude. It should be noted that the forces, moments, and powers are normalized with respect to body weight. The CoP estimation using both methods shows good correspondence, as indicated by the RMS difference of (5.1 +/- 0.7) mm, corresponding to (1.7 +/- 0.3)% of the length of the shoe. The RMS difference between the magnitudes of the heel position estimates was calculated as (18 +/- 6) mm, being (1.4 +/- 0.5)% of the maximal magnitude. The ankle moment RMS difference was (0.004 +/- 0.001) Nm/N, being (2.3 +/- 0.5)% of the maximal magnitude. Finally, the RMS difference of the estimated power at the ankle was (0.02 +/- 0.005) W/N, being (14 +/- 5)% of the maximal power. This power difference is caused by an inaccurate estimation of the angular velocities using the optical reference measurement system, which is due to considering the foot as a single segment. The ambulatory system considers separate heel and forefoot segments, thus allowing an additional foot moment and power to be estimated. Based on the results of this research, it is concluded that the combination of the instrumented shoe and inertial sensing is a promising tool for the assessment of the dynamics of foot and ankle in an ambulatory setting.     

Ambulatory Hip Angle Estimation using Gaussian Particle Filter

Hip angle is a major parameter in gait analysis while gait analysis plays an important role in healthcare, animation and other applications. Accurate estimation of hip angle using wearable inertial sensors in ambulatory environment remains a challenge because 1) the non-linear nature of thigh movement has not been well addressed, and 2) the variation of micro-inertial sensor measurement noise has not been studied yet. We propose to use Hybrid Dynamic Bayesian Network (HDBN) to model the non-linear hip angle dynamics and variation of measurement noise levels, and Gaussian Particle Filter (GPF) to estimate the hip angle during gait cycles from the measurements of the wearable accelerometers that are attached to the thighs. The experiments have been conducted and the results have shown that the proposed method can achieve significant accuracy improvement over the previous work on the ambulatory hip angle estimation.     

基于惯性传感器和改进Criminis算法的距离估计

针对距离估计问题,提出了一种新颖的利用惯性传感器和单目摄像机的距离估计系统,该系统通过结合惯性导航算法和图像处理算法来实现距离估计。首先通过移动单目摄像机,得到一个目标的两幅图像,并利用改进后的Criminis算法提取两个图像间的特征值,再利用惯性导航算法对摄像机的运动进行估计运算,计算出摄像机移动的基线估计,最后通过最小二乘法得到估计距离。同时为了减少误差,在摄像机移动的过程中利用了零速度更新。通过实际环境实验,我们对该算法的准确度进行了评估,在几米范围内,它的平均距离误差是3.9%。     

Shape estimation of a large workspace continuum manipulator with fusion of inertial sensors

This paper focuses on shape estimation of a novel multi-section continuum and compliant robotic manipulator with large workspace. Real-time shape estimation is important for closed-loop control of continuum robots. However, due to the continuum deformation of such manipulators, it is not feasible to use conventional angular sensors. On the other hand, in order to increase the motion range in the proposed manipulator structure, each flexible link includes several serially connected sections. Because of the effect of gravitational forces, each section in these long size links could have different curvatures which makes the shape estimation more challenging. In this study, inertial sensors are utilized to estimate the overall shape of the large scale continuum manipulator. MEMS based inertial measurement units (IMUs) including gyroscopes, accelerometers, and magnetometers are installed on each section and the shapes of the sections are determined with Extended Kalman Filter (EKF) fusion algorithm. But the effect of unwanted external disturbances including translational accelerations and magnetic interferences on the observation model of the EKF, severely affects the estimation performance. So, a disturbance rejection algorithm (DRA) is proposed which can detect the presence of disturbances in each measurement axis and excludes that sensor axis from estimation procedure. Performance of the proposed shape estimation method is validated experimentally on the implemented manipulator.     

基于DOA矩阵法的矢量传感器阵列二维波达方向估计

DOA矩阵法是一种二维波达方向估计方法,该方法具有分辩率较高、计算量较少的特点。该文将DOA矩阵法扩展至矢量传感器阵列的波达方向估计,推导出相应的计算公式,分析了算法计算复杂度。算法分析表明,相对于ESPRIT算法,DOA矩阵法计算复杂度大大降低。Monte Carlo仿真结果表明,与ESPRIT算法相比,DOA矩阵法估计的均方根偏差和均方根标准方差相对大一些,所需的最低快拍数也增加。     

Relative location estimation in wireless sensor networks

Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS.     

一种非等间距线阵的DOA估计方法

针对非等间距线阵测向中的测向精度和相位模糊之间的矛盾,该文提出了一种基于四阶累积量的波达角(DOA)估计算法。该算法利用基线间的参差关系解相位模糊,以突破VESPA算法中参考阵元间距不大于半波长的限制,从而有效地提高了测向精度。依靠导向矢量、累积量矩阵特征值以及其对应的特征向量三者之间的关系,该算法实现了各基线的相位差与信号源的配对。仿真实验验证了该算法的有效性。     

Neural network-based estimation of power electronic waveforms

Artificial neural network techniques are indicating a lot of promise for application in power electronic systems. So far, these applications are mainly confined to control, identification, and diagnostic problems, but the application in estimation is fairly new. The paper explores the application of neural networks for estimation of power electronic waveforms. The distorted line current waveforms in a single-phase thyristor AC controller and a three-phase diode rectifier that feeds an inverter-machine load have been taken into consideration, and neural networks have been trained to estimate the total RMS current, fundamental RMS current, displacement factor, and power factor. The performance of the neural network-based estimators has been compared with the actual values, and excellent performance is indicated. Neural network-based estimation has the usual advantages of very fast and simultaneous response of all the outputs, noise, and fault-tolerant performance and can be easily implemented in dedicated analog or digital hardware chips, which can coexist with digital signal processor (DSP) and/or application-specific integrated circuit (ASIC) chips. The estimation techniques can be extended to more complex waveforms in power electronics     

基于分簇结构无线传感器网络的高效无源定位方法

针对基于节点通信能力和能量受限的无线传感器网络实现高精度无源定位的问题,首先,在分簇结构网络中,通过折中单个簇的TDOA定位精度和运算复杂度,确定了簇规模;其次,基于直达波环境中TDOA定位误差是按布站GDOP对测量误差放大的原理,提出第一轮定位先使用网内所有节点以RSSI定位方法粗估计目标辐射源位置,并根据各簇在该粗估计位置处的布站GDOP和测量误差估算TDOA定位标准差,第二轮定位选择具有较低TDOA定位估计标准差的部分簇参与TDOA定位,最后将这些簇的TDOA定位结果按估算的定位估计标准差加权平均,求得最终定位解。仿真结果证明该方法有效的去除了冗余节点,实现以半数节点接近使用全部节点的定位精度。      

A Calibration Technique for a Redundant IMU Containing Low‐Grade Inertial Sensors

A calibration technique for a redundant inertial measurement unit (IMU) containing low‐grade inertial sensors is proposed. In order to calibrate a redundant IMU that can detect and isolate faulty sensors, the fundamental coordinate frames in the IMU are defined and the IMU error is modeled based on the frames. Equations to estimate the error coefficients of the redundant IMU are formulated, and a test sequence using a 2‐axis turntable is also presented. Finally, a redundant IMU with cone configuration is implemented using low‐grade inertial sensors, and the performance of the proposed technique is verified experimentally.     

无源雷达目标信号时延与多普勒频率联合估计

提出一种基于STFRFT的无源雷达目标时延与多普勒联合估计新方法。首先,给出了一种STFRFT时频分辨率量化方法及其数学表达式;其次,利用STFRFT的投影包络对回波信号时延与Doppler频移进行了联合估计;最后,讨论分析了脉内信噪比、高斯窗参数等对所提方法估计性能的影响。仿真结果表明,与CAF方法相比,所提方法更容易获得高精度的时延与Doppler频移的联合估计,具有更高的可靠性。     

双模态信息融合的飞行目标位姿估计方法

针对在飞行目标位姿估计中背景复杂、目标提取及位姿解算精度低、速度实时性差等问题,提出了一种激光与图像信息融合的飞行目标位姿估计方法。首先,建立彩色相机和激光雷达间的坐标变换模型实现两传感器像素级匹配,对同一时刻的图像和点云进行融合处理;其次,采用Vi Be算法与深度信息融合对图像中运动目标进行提取,并根据图像目标的位置框选出对应的点云;最后,利用PnP算法进行特征点粗配准,获取点云间初始旋转平移矩阵,并采用迭代最近点算法进行精配准,利用IK-D Tree加速临近点搜索,提高配准速度。使用仿真试验和半实物仿真试验对方法准确性和稳定性进行了验证,结果显示：二维图像目标检测算法正确率为97%,错误分类比为0.011 2%;位姿估计算法与传统迭代最近点算法相比精度提高了53.2%,单次耗时从261 ms降低至132 ms,效率提升约49.4%,与其他算法相比也具有一定优势。为飞行目标落点精准预测和制导控制提供解决思路。     

空间分集MIMO雷达的DOA估计新方法

多输入多输出(MIMO)雷达分布式布阵获得空间分集增益,但DOA估计是一个难题。该文分析了DOA估计的旁瓣一致现象,从空间分集MIMO雷达信号模型出发,推导了发射正交PCM信号,在接收端匹配滤波信号分选构造虚拟子阵的过程,提出了一种谱值优选算法进行空间分集MIMO雷达DOA估计。该算法得到目标在各子阵的最优估计,在发射端获得空间分集增益抑制目标闪烁的同时,在接收端进行稳健的多目标波达方向超分辨估计。最后仿真实验与相控阵进行了比较,表明了该算法的有效性,以及抑制目标闪烁改善DOA估计的能力。      

Dead-reckoning sensor system and tracking algorithm for 3-D pipeline mapping

A dead-reckoning sensor system and a tracking algorithm for 3-D pipeline mapping are proposed for a tap water pipeline for which the diameter is small and the inner surface is rough due to pipe scales. The goals of this study are to overcome the performance limitations of small and low-grade sensors by combining various sensors with complementary functions and achieve robustness against a severe environment. A dead-reckoning sensor system consists of a small, low-cost micro electromechanical system inertial measurement unit (MEMS IMU) and an optical navigation sensor (used in laser mice). A tracking algorithm consists of a multi-rate extended Kalman filter (EKF) to fuse redundant and complementary data from the MEMS IMU and the optical navigation sensor and a geometry compensation method to reduce position estimation error using the end point of the pipeline. Two sets of experimental data have been obtained by driving a radio-controlled car equipped with the sensor system in a 3-D pipeline and on asphalt pavement. Our study can be used to estimate the path of a 3-D pipeline or mobile robots.     

Ambulatory position and orientation tracking fusing magnetic and inertial sensing

This paper presents the design and testing of a portable magnetic system combined with miniature inertial sensors for ambulatory 6 degrees of freedom (DOF) human motion tracking. The magnetic system consists of three orthogonal coils, the source, fixed to the body and 3-D magnetic sensors, fixed to remote body segments, which measure the fields generated by the source. Based on the measured signals, a processor calculates the relative positions and orientations between source and sensor. Magnetic actuation requires a substantial amount of energy which limits the update rate with a set of batteries. Moreover, the magnetic field can easily be disturbed by ferromagnetic materials or other sources. Inertial sensors can be sampled at high rates, require only little energy and do not suffer from magnetic interferences. However, accelerometers and gyroscopes can only measure changes in position and orientation and suffer from integration drift. By combing measurements from both systems in a complementary Kalman filter structure, an optimal solution for position and orientation estimates is obtained. The magnetic system provides 6 DOF measurements at a relatively low update rate while the inertial sensors track the changes position and orientation in between the magnetic updates. The implemented system is tested against a lab-bound camera tracking system for several functional body movements. The accuracy was about 5 mm for position and 3 degrees for orientation measurements. Errors were higher during movements with high velocities due to relative movement between source and sensor within one cycle of magnetic actuation.     

融合压缩采样与深度神经网络的直接序列扩频参数估计

直接序列扩频(Direct Sequence Spread Spectrum,DSSS）信号的宽频带特性所带来的高采样率增加了参数估计的实现难度。针对现有技术所面临的问题与挑战,融合压缩采样与深度神经网络(Deep Neural Network,DNN),提出了用于估计DSSS信号参数的方法。一方面,压缩采样能够利用信号的冗余性,以较低的采样率获取信号中的有效参数信息;另一方面,DNN在提取数据特征方面具有高效准确的特点。通过对压缩采样与参数估计网络的整体训练,实现两者的有效配合,进而实现以较低采样率对DSSS信号参数的准确估计。仿真实验证明了该参数估计方法在低信噪比下的估计能力相对于传统方法具有一定的提升。     

MEMS IMU Based Pedestrian Indoor Navigation for Smart Glass

In open air environment, Global Positioning System (GPS) receiver can determine its position with very high accuracy. Inside a building the GPS signal is degraded, the position estimation from the GPS receiver is very erroneous and of no practical use. In this paper, we present an indoor navigation system to track the position of a pedestrian by using built-in inertial measurement unit (IMU) sensors of a smart eyeglass. The device used for this project was an intelligent eye-wear “JINS MEME”. Here algorithm for step detection, heading and stride estimation are used to estimate the position based on the known locations of the walker using Pedestrian Dead Reckoning method (PDR). We have used extended Kalman filters as sensor fusion algorithm, where measurements of acceleration and orientation from IMU are used to track user’s movement, pace, and heading. The results showed that the level of accuracy was entirely acceptable. Average deviancy between the estimated and real position was less than 1.5 m for short range of walk was accomplished. There are some ideas for further development. Increasing the accuracy of the position estimation by palliation of stride length estimation error was identified as the most essential.     

Ambulatory monitoring of physical activities in patients with Parkinson's disease

A new ambulatory method of monitoring physical activities in Parkinson's disease (PD) patients is proposed based on a portable data-logger with three body-fixed inertial sensors. A group of ten PD patients treated with subthalamic nucleus deep brain stimulation (STN-DBS) and ten normal control subjects followed a protocol of typical daily activities and the whole period of the measurement was recorded by video. Walking periods were recognized using two sensors on shanks and lying periods were detected using a sensor on trunk. By calculating kinematics features of the trunk movements during the transitions between sitting and standing postures and using a statistical classifier, sit-to-stand (SiSt) and stand-to-sit (StSi) transitions were detected and separated from other body movements. Finally, a fuzzy classifier used this information to detect periods of sitting and standing. The proposed method showed a high sensitivity and specificity for the detection of basic body postures allocations: sitting, standing, lying, and walking periods, both in PD patients and healthy subjects. We found significant differences in parameters related to SiSt and StSi transitions between PD patients and controls and also between PD patients with and without STN-DBS turned on. We concluded that our method provides a simple, accurate, and effective means to objectively quantify physical activities in both normal and PD patients and may prove useful to assess the level of motor functions in the latter.     

倾斜梳齿的MEMS电容式传感器惯性脉冲响应特性研究

 DRIE(Deep Reactive Ion Etching)工艺加工的高深宽比梳齿电容不能保证绝对平行.本文在考虑低真空空气阻尼力的同时,研究了梳齿电容倾斜的MEMS传感器对脉冲惯性信号的响应,并分析了DRIE工艺因素对器件性能的影响.研究结果表明,当传感器为没有静电力反馈的双边电容结构时,梳齿电容的不平行对传感器的响应位移、惯性脉冲响应线性度范围影响明显,且随着封装真空度增加而加重.若传感器有静电力反馈,惯性脉冲响应的灵敏度降低,但DRIE工艺因素的影响程度降低.为了抑制DRIE工艺导致的梳齿电容不平行因素的影响,文中还设计了一个新型的变电容面积的MEMS惯性传感器,并用ANSYS初步分析了其性能,设计了其详细的制作工艺流程.