A tele-monitoring system for gait rehabilitation with an inertial measurement unit and a shoe-type ground reaction force sensor

In this paper, a tele-monitoring system is proposed, using an inertial measurement unit (IMU) and a shoe-type ground reaction force (GRF) sensor called a Smart Shoe to measure a patient’s walking data, and transmitting the measured data via the Internet. In our previous work, a mobile gait-monitoring system was developed, which provided visual feedback based on GRFs measured by a Smart Shoe (used as a mobile platform). However, the limited information provided by the Smart Shoe alone may not be adequate for a tele-monitoring system using the Internet. In the present tele-monitoring system for gait rehabilitation, a Smart Shoe is combined with an IMU for detailed monitoring of walking motions. By analyzing the signals from the IMU and the Smart Shoe, foot trajectories, walking distance, length of stride, etc., can be estimated. A user-friendly graphic interface displays the measured or estimated data on separate computers at the patient’s location and the physical therapist’s office. Thus, using the proposed system, it is possible to monitor a patient’s walking motion via the Internet, without restrictions on time or place.     

Preliminary Evaluations of a Self-Contained Anthropomorphic Transfemoral Prosthesis

This paper presents a self-contained powered knee and ankle prosthesis, intended to enhance the mobility of transfemoral amputees. A finite-state based impedance control approach, previously developed by the authors, is used for the control of the prosthesis during walking and standing. Experiments on an amputee subject for level treadmill and overground walking are described. Knee and ankle joint angle, torque, and power data taken during walking experiments at various speeds demonstrate the ability of the prosthesis to provide a functional gait that is representative of normal gait biomechanics. Measurements from the battery during level overground walking indicate that the self-contained device can provide more than 4500 strides, or 9 km, of walking at a speed of 5.1 km/h between battery charges.      

Energy expenditure rate in level and uphill treadmill walking determined from empirical models and foot inertial sensing data

An empirical model is used for predicting the energy expenditure rate of treadmill walking from walking speed and incline, which are measured by a foot-mounted inertial sensor. The difference between values of the energy expenditure rate obtained by entering measured and true values of these variables in the model equation is less than the errors that are reported to affect model based assessments of the metabolic response to locomotion in humans.     

Gait event detection algorithm based on smart insoles

Gait analysis is an effective clinical tool across a wide range of applications. Recently, inertial measurement units have been extensively utilized for gait analysis. Effective gait analyses require good estimates of heel‐strike and toe‐off events. Previous studies have focused on the effective device position and type of triaxis direction to detect gait events. This study proposes an effective heel‐strike and toe‐off detection algorithm using a smart insole with inertial measurement units. This method detects heel‐strike and toe‐off events through a time‐frequency analysis by limiting the range. To assess its performance, gait data for seven healthy male subjects during walking and running were acquired. The proposed heel‐strike and toe‐off detection algorithm yielded the largest error of 0.03 seconds for running toe‐off events, and an average of 0–0.01 seconds for other gait tests. Novel gait analyses could be conducted without suffering from space limitations because gait parameters such as the cadence, stance phase time, swing phase time, single‐support time, and double‐support time can all be estimated using the proposed heel‐strike and toe‐off detection algorithm.     

Quaternion based attitude estimation algorithm applied to signals from body-mounted gyroscopes

An application of inertial sensing to gait analysis is described: the attitude estimation algorithm computes the attitude quaternion associated with a body part, i.e. the foot instep moving in the inertial space, by employing a spherical linear interpolation procedure to compensate for the errors due to the influence of the gyroscope bias. The effectiveness of the approach is demonstrated by simulations.     

Gait probability image: An information-theoretic model of gait representation

In this paper, we propose a new probabilistic gait representation to characterize human walking for recognition by gait. The approach obtains the binomial distribution of every pixel in a gait cycle. Organizing the binomial distribution of all pixels in the gait image, we obtain the gait signature, which we denote as the Gait Probability Image (GPI). In the recognition stage, symmetric Kullback–Leibler divergence is used to measure the information theoretical distance between gait signatures. The experimental results reveal that GPI achieves promising recognition rates. Besides that, experiments on different walking speeds demonstrate that GPI is robust to slight variation in walking speed.     

Symbolic Time Series Analysis of Temporal Gait Dynamics

Signals obtained from biological systems exhibit pronounced complexity. The patterns of change contain valuable information about the dynamics of underlying control mechanism of the complex biological systems. Human gait is a complex process with multiple inputs and numerous outputs. Various complexity analysis tools have been proposed to extract information from human gait time series. In this study, we used recently developed threshold based symbolic entropy to compare the spontaneous output of the human locomotors system during constrained and metronomically paced walking protocols. For that purpose, stride interval time series of healthy subjects who walked for 1 h at normal, slow and fast rates under different conditions was transformed into symbol sequences. Normalized corrected Shannon entropy (NCSE) was computed from the symbol sequences of the stride interval time series. The findings indicated that the unprompted output of human locomotors system is more complex during unconstrained normal walking as compared with slow, fast or metronomically paced walking.     

基于惯性传感器的动态步长算法研究

行人步态参数的精确估计是行人自主导航系统和行人健康监测的关键技术之一。针对当前行人自主导航系统中步长估算算法精度低和弱适应性的问题,提出了一种计算行人动态步长算法。首先对行人的步态特征进行分解,利用改进的零速检测确定行人运动状态,采用卡尔曼滤波技术降低惯性传感器中累积误差的影响,再对进行滤波和坐标转换后的加速度进行双重积分,最终得到行人脚尖的运动轨迹。通过采用MTI-700惯性模块设计实验并进行实验验证。结果表明,该文提出的步长算法计算的步长与行人实际步长的误差低于3.0%。与现有的行人动态步长算法相比,该算法首次计算出行人脚尖的运动轨迹,精度较高且适应强,在行人自主导航及行人健康监测领域具有较大的应用价值。     

Waalbot: An Agile Small-Scale Wall-Climbing Robot Utilizing Dry Elastomer Adhesives

This paper proposes a small-scale agile wall-climbing robot, which is able to climb on smooth vertical surfaces using flat adhesive elastomer materials for attachment. Using two actuated legs with rotary motion and two passive revolute joints at each foot, this robot can climb and steer in any orientation. Due to its compact design, a high degree of miniaturization is possible. It has onboard power, computing, and wireless communication, which allow for semiautonomous operation. Various aspects of a functioning prototype design and performance are discussed in detail, including leg and foot design and gait dynamics. A model for the adhesion requirements and performance is developed and verified through experiments. Using an adhesive elastomer (Vytaflex 10), the current prototype can climb 90deg slopes at a speed of up to 6 cm/s and steer to any angle reliably on a smooth acrylic surface as well as transition from floor walking to wall climbing. This robot is intended for inspection and surveillance applications, and ultimately, for space missions.     

Support vector machines for automated gait classification

Ageing influences gait patterns causing constant threats to control of locomotor balance. Automated recognition of gait changes has many advantages including, early identification of at-risk gait and monitoring the progress of treatment outcomes. In this paper, we apply an artificial intelligence technique [support vector machines (SVM)] for the automatic recognition of young-old gait types from their respective gait-patterns. Minimum foot clearance (MFC) data of 30 young and 28 elderly participants were analyzed using a PEAK-2D motion analysis system during a 20-min continuous walk on a treadmill at self-selected walking speed. Gait features extracted from individual MFC histogram-plot and Poincaré-plot images were used to train the SVM. Cross-validation test results indicate that the generalization performance of the SVM was on average 83.3% (+/-2.9) to recognize young and elderly gait patterns, compared to a neural network's accuracy of 75.0+/-5.0%. A "hill-climbing" feature selection algorithm demonstrated that a small subset (3-5) of gait features extracted from MFC plots could differentiate the gait patterns with 90% accuracy. Performance of the gait classifier was evaluated using areas under the receiver operating characteristic plots. Improved performance of the classifier was evident when trained with reduced number of selected good features and with radial basis function kernel. These results suggest that SVMs can function as an efficient gait classifier for recognition of young and elderly gait patterns, and has the potential for wider applications in gait identification for falls-risk minimization in the elderly.     

智能仿生人工腿硬件研究与设计

CIP-Ⅰ Leg是国内首个智能仿生人工腿原型机.首先介绍了CIP-Ⅰ Leg的基本结构,给出了实物照片和三维CAD视图,然后重点介绍了该系统的步速调整原理、控制系统结构、针阀开度值的设置方法以及硬件和软件设计等.提出了一种新的步速测量方法,即通过测量人工腿一个完整的步行周期,用步行周期的长短来反映步行速度的快慢.在硬件电路设计中,采用MSP430F149微处理器处理霍尔传感器的信号并计算步行周期值.实验结果表明,该步速测量系统精度高,实时性好,功耗低,能准确地检测CIP-Ⅰ Leg的步行速度.     

Sensors and control concept of a biped robot

The biped robot "Johnnie" is designed to achieve a dynamically stable gait pattern, allowing for high walking velocities. Very accurate and fast sensors were developed for the machine. In particular, the design of the three-dimensional-orientation sensor and the six-axes force-torque sensor are presented. The control scheme is based on the information from these sensors to deal with unstructured terrain and disturbances. Two different implementations are investigated: a computed torque approach and a trajectory control with adaptive trajectories. Walking speeds of 2.4 km/h have been achieved in experiments.     

Long-term unrestrained measurement of stride length and walkingvelocity utilizing a piezoelectric gyroscope

Long-term monitoring of stride length and walking velocity is considered to provide useful information for making decisions on treatment of patients with gait disabilities. The purpose of this study was to develop a device with the following design criteria: lightweight, easy attachment, little hindrance to the natural gait pattern, sufficient memory to record for one day, and practicality in clinical use. The prototype consists of a piezoelectric gyroscope, which detects angular velocity of the thigh of one leg in the sagittal plane, and a microprocessor-based maximum/minimum detector/data logger of a cyclic analog signal associated with the gait cycle. The accuracy of the device was evaluated in 20 normal subjects, seven above-the-knee (A/K) amputees, and ten hemiplegic patients, and relative accuracy within ±15% was obtained, except for two special cases     

Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring

An ambulatory gait analysis method using body-attached gyroscopes to estimate spatio-temporal parameters of gait has been proposed and validated against a reference system for normal and pathologic gait. Later, ten Parkinson's disease (PD) patients with subthalamic nucleus deep brain stimulation (STN-DBS) implantation participated in gait measurements using our device. They walked one to three times on a 20-m walkway. Patients did the test twice: once STN-DBS was ON and once 180 min after turning it OFF. A group of ten age-matched normal subjects were also measured as controls. For each gait cycle, spatio-temporal parameters such as stride length (SL), stride velocity (SV), stance (ST), double support (DS), and gait cycle time (GC) were calculated. We found that PD patients had significantly different gait parameters comparing to controls. They had 52% less SV, 60% less SL, and 40% longer GC. Also they had significantly longer ST and DS (11% and 59% more, respectively) than controls. STN-DBS significantly improved gait parameters. During the stim ON period, PD patients had 31% faster SV, 26% longer SL, 6% shorter ST, and 26% shorter DS. GC, however, was not significantly different. Some of the gait parameters had high correlation with Unified Parkinson's Disease Rating Scale (UPDRS) subscores including SL with a significant correlation (r = -0.90) with UPDRS gait subscore. We concluded that our method provides a simple yet effective way of ambulatory gait analysis in PD patients with results confirming those obtained from much more complex and expensive methods used in gait labs.     

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.     

Prototype development and gait planning of biologically inspired multi-legged crablike robot

In order to investigate the walking gait of the legged robot with multiple redundant walking legs, the motion features of the biologic crab are studied. To study the motion property of multi-legged animals in depth, an event sequence analysis method is proposed, and employed to design the motion pattern of multi-legged robot. A low-consumption environmental self-adaptive bionic gait with its phase factor of 0.25 and duty factor of 0.454 is analyzed based on the analysis of pace order, gait parameters and single leg’s terminal trajectory on uneven terrain. According to the structures and motion patterns of biologic crab, a multi-legged crablike prototype with its experimental platform is developed. The contrast tests of environmental self-adaptive bionic gait and double tetrapod gait are experimented at the same velocity, and slope climbing tests are performed as well. The experimental results show that, although the double tetrapod gait enables four legs to support the robot’s body at any time, there exists halt or backward phenomena periodically. However, the robot using the new gait has lower gravity fluctuation in displacement and velocity without halt or backward problem, and the decreasing of motion speed leads to the increasing of the gravity fluctuation and the toe-force.     

Design and locomotion control of a hydraulic lower extremity exoskeleton for mobility augmentation

In this paper, a system design and three locomotion control algorithms are proposed for a hydraulic lower extremity exoskeleton to enhance mobility and reduce muscle fatigue caused by backpack loads. The range of motion of the exoskeleton and the capacity of the hydraulic power unit, which generates the hydraulic flow and pressure, are determined by analysing human walking data obtained using a motion capture device and force plates. For movement comfort, the mechanical structure and the joints of the exoskeleton are designed such that the motion of the wearer coincides with that of the exoskeleton. In addition, locomotion control algorithms for stable normal walking are described; these algorithms enable dual-mode control and transition control. Dual-mode control is comprised of an active mode in the stance phase and a passive mode in the swing phase. In the active mode, the exoskeleton is controlled to track the motion of the wearer, and in the passive mode, its active joints work as passive joints by blocking the hydraulic power supply from the hydraulic power unit. Transition control, which consists of a torque-shaping method and a pre-transition algorithm, is adopted to improve locomotion responses during gait phase transition. Finally, to verify the effectiveness of the locomotion control algorithms and the developed hydraulic lower limb exoskeleton, walking experiments are performed on a treadmill, at a speed of 4 km/h, while carrying 45 kg backpack loads. The assistance effect of the exoskeleton is also validated by comparing the electromyography (EMG) signals of four selected muscles, with and without the exoskeleton, for single stance and level walking while carrying the same 45 kg backpack loads.     

Classification of periodic activities using the Wasserstein distance

In this paper, we introduce a novel nonparametric classification technique based on the use of the Wasserstein distance. The proposed scheme is applied in a biomedical context for the analysis of recorded accelerometer data: the aim is to retrieve three types of periodic activities (walking, biking, and running) from a time-frequency representation of the data. The main interest of the use of the Wasserstein distance lies in the fact that it is less sensitive to the location of the frequency peaks than to the global structure of the frequency pattern, allowing us to detect activities almost independently of their speed or incline. Our system is tested on a 24-subject corpus: results show that the use of Wasserstein distance combined with some supervised learning techniques allows us to compare with some more complex classification systems.     

基于自适应阈值的行人惯性导航零速检测算法

在基于微机电系统(MEMS)的行人惯性导航中零速区间检测算法是制约导航位置解算误差增长的重要因素。针对利用固定阈值实现零速检测算法在不同运动状态下存在检测适应性差的问题,根据行人步态特征以及周期性零速规律,以合加速度和合角速度为检测数据,提出了一种基于自适应阈值的零速检测算法。利用检测数据在运动状态下的动态特点及统计特征,根据获得的运动状态信息更新零速区间检测阈值,以适应在不同运动状态下实现准确的零速区间检测。实验表明,自适应阈值算法对零速区间可以进行精确检测,零速检测准确率达到98%。检测结果用于导航定位解算的误差率小于1.5%。