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Gait phase classification for in-home gait assessment

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... Related range sensor and homevideo based systems, which cost about £700, such as [9][10][11], that build on the work of [12], with Pro-Trainer motion analysis software (Sports Motion, Inc., Cardiff, CA), offer gait analysis outside the gait laboratory, e.g. in local clinics and at homes. Similar to other range sensor and home-video based gait analysis systems [2,[13][14][15][16][17][18][19][20][21] and inertial measurement unit (IMU) based gait analysis systems [22][23][24][25][26], the gait parameters obtained after data processing can be sent to physiatrists for clinical consultation, indicating the potential for tele-rehabilitation [27][28][29][30][31]. It is shown in [32] that a 2D video tracker software provides similar accuracy to VICON 3D system for knee angle measurement but not for measurement of the ankle angle over time. ...
... Our system addresses some of the drawbacks of related range sensor and home-video based systems [2,9,11,[13][14][15][16][17][18][19]41] and IMU systems [22][23][24][25][26] namely: (i) unlike [9], there are no colour restrictions on the background or the participant's clothing; (ii) in contrast to [9], which is validated on only one healthy volunteer with one walking trial with no gold standard benchmark, we validate our proposed system's knee angle against the gold standard VICON MX Giganet 6xT40 and 6xT160 (VICON Motion Systems Ltd., Oxford, UK, approximately £ 250,000) optical motion analysis system (the same gold standard as used by Ugbolue et al. [11]); (iii) unlike systems of [11] and Pro-Trainer and Siliconcoach (Siliconcoach Ltd., Dunedin, New Zealand) as used by the authors in [42,43] that require significant manual effort, our system autonomously tracks the markers attached to the joints and calculates the knee angle; the only operational effort required is for marker-template selection for tracking initialisation which is done via a user-friendly graphical user interface (GUI); (iv) unlike the passive marker system [41] that is only validated on one side of the body without any benchmarking systems, our system is validated on both sides of the body with a gold standard VICON optical motion analysis system; (v) 3D Kinect range sensor-based systems [13-18, 20, 21] cannot reliably capture relatively fast body motion, since Kinect operates at only 30 frames per second (fps), whereas our system operates at 210 fps; (vi) like other range sensor and home-video based systems, our system is non-intrusive to the participants, which is in contrast to state-of-the-art IMU gait analysis systems [22][23][24][25][26]. However, with only a 2D camera in our gait analysis system, its drawback lies in the following two aspects: (i) estimation of the human joint locations using our system is less accurate compared to 3D Kinect-based range sensor systems, and (ii) the gait parameters derived from the 2D images in our system are less reliable than those derived from the inertial data in IMU systems. ...
... Our future work will be focused on further improvement of performance and potential measurement capability of more gait parameters using a stereo 2D-camera system or a single depth sensing device [2,[13][14][15][16][17][18][19] with a high frame rate, without sacrificing portability, to remove the parallax error, and leverage the 3D information for quantifying a larger number of gait parameters such as hip, knee, and ankle angles in both the sagittal and frontal planes, and pelvis tilt, calculating temporal-spatial parameters (step length and width, stride length, step time, cadence and step length symmetry), gait speed and measuring sagittal/ frontal plane knee motion, but at an increased processing complexity. Furthermore, neural-network-based methods are what we could consider in future research that involves evaluations with public availability of large labelled datasets of walking trials. ...
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While optical motion analysis systems can provide high-fidelity gait parameters, they are usually impractical for local clinics and home use, due to high cost, requirement for large space, and lack of portability. In this study, we focus on a cost-effective and portable, single-camera gait analysis solution, based on video acquisition with calibration, autonomous detection of frames-ofinterest, Kalman-filter+Structural-Similarity-based marker tracking, and autonomous knee angle calculation. The proposed system is tested using 15 participants, including 10 stroke patients and 5 healthy volunteers. The evaluation of autonomous frames-ofinterest detection shows only 0.2% difference between the frame number of the detected frame compared to the frame number of the manually labelled ground truth frame, and thus can replace manual labelling. The system is validated against a gold standard optical motion analysis system, using knee angle accuracy as metric of assessment. The accuracy investigation between the RGBand the grayscale-video marker tracking schemes shows that the grayscale system suffers from negligible accuracy loss with a significant processing speed advantage. Experimental results demonstrate that the proposed system can automatically estimate the knee angle, with R-squared value larger than 0.95 and Bland-Altman plot results smaller than 3.0127 degrees mean error.
... It is to compare and find optimal alignment between two given time-sequences. Furthermore, Ye et al [60] utilizes DTW to compute a distance matrix for gait pattern extraction while Su et al [61] applying DTW measure the similarity of joint data between "at home exercises "and" in hospital exercises". ...
... Su et al then evaluate the performance by using Adaptive Neuro-Fuzzy Inference System (ANFIS) which integrates a neural network and a fuzzy logic [61]. Nomm et al practiced Neural Network based model, NN-based ANARX (Additive Nonlinear Auto Regressive exogenous), in their monitoring system as it can adjust the system according to the specific needs of each patient [62] whereas Ye et al used NN-based on nonlinear autoregressive with exogenous (NARX) for gait phase classification and Enhanced Random Decision Forest (ERF) for missing features cases [60]. Next, Nahavandi et al trained a Random Decision Forest (RDF) for generalising a learning model in order to discriminate between seven RULA-scored sets of postures [63]. ...
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Kinect-based physical rehabilitation grows significantly as a mechanism for clinical assessment and rehabilitation due to its flexibility, low-cost and markerless system for human action capture. It is also an approach to provide convenience for for patients’ exercises continuation at home. In this paper, we discuss a review of the present Kinect-based physiotherapy and assessment for rehabilitation patients to provide an outline of the state of art, limitation and issues of concern as well as suggestion for future work in this approach. The paper is constructed into three main parts. The introduction was discussed on physiotherapy exercises and the limitation of current Kinect-based applications. Next, we also discuss on Kinect Skeleton Joint and Kinect Depth Map features that being used widely nowadays. A concise summary with significant findings of each paper had been tabulate for each feature; Skeleton Joints and Depth Map. Afterwards, we assemble a quite number of classification method that being implemented for activity recognition in past few years. © 2018 Institute of Advanced Engineering and Science. All rights reserved.
... Consequently, running gait assessment has moved towards instrumented approaches such as wearable technology including inertial measurement units (IMU), force/pressure plate analysis [8][9][10], as well as three-dimensional (3D) motion tracking [11,12] in an effort to provide reliable, reproducible outcomes. Despite research-grade wearable technology's utility in providing a wide range of gait outcomes, they are currently limited in use due to the cost [13], tethering of peripheral technologies, and reliance upon bespoke environments with expert assistance [9]. For example, wearable sensors or 3D-motion reflective markers often rely upon precise anatomical placement for optimal use [14,15]. ...
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Running gait assessment is essential for development of technique optimization strategies as well as to inform injury prevention and rehabilitation. Currently, running gait assessment relies on (i) visual assessment, exhibiting subjectivity and limited reliability, or (ii) use of instrumented approaches, which often carry high costs and can be intrusive due to attachment of equipment to the body. Here use of an IoT-enabled markerless computer vision smartphone application based upon Googles pose estimation model BlazePose was evaluated for running gait assess-ment for use in low-resource settings. That human pose estimation architecture was used to ex-tract contact time, swing time, step time, knee flexion angle and foot strike location from a large cohort of runners. The gold-standard Vicon 3D motion capture system was used as a reference. The proposed approach performs robustly, demonstrating good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all running gait outcomes. Additionally, temporal outcomes exhibit low mean error (0.01-0.014s) in left foot outcomes. However, there are some discrepancies in right foot outcomes, due to occlusion. This study demonstrates that the proposed low-cost and markerless system provides accurate running gait assessment outcomes. The approach may help routine running gait assessment in low-resource environments.
... Publication [47] presents a comparison between different alghorithms to extract gait phases from inertial units worn by the subject and surface electromyography, while [48] presents inertial units and an adaptive Bayesian approach for recognition of walking activities. Reference [49] presents instead a classification method based on a single 3D depth camera and 12 key feature, to provide a reliable and low cost way to assess the gait phases. All the sensors used in these works, however, are either worn by the user, or monitor a limited volume of space where the user can walk. ...
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The paper presents a novel method for the classification of gait phases for power gait orthosis users based on machine learning. The classification uses depth images collected from a Time of Flight camera embedded in the crutches employed for the assisted gait. The machine learning algorithm foresees an initial phase of data collection and processing, identifying the 3D points belonging to the foot and those belonging to the floor. From these, a feature set is computed analyzing the values of percentiles of distances of the foot from the floor, and passed to a modified version of Random Forest classifier, called Sigma-z Random Forest. The classifier considers the uncertainties associated to each feature set and provides both the classification of the gait phase (stance or swing) and an associated confidence value. In this work, we propose the use of the confidence value to improve the reliability of the gait phase classification, by applying an optimized threshold to the confidence value obtained for each new frame. The algorithm has been tested on different subjects and environments. An average classification accuracy of 87.3% has been obtained (+6.3% with respect to the standard random forest classifier), with a minor loss of unclassifiable frames. Results highlight that unclassifiable samples are usually associated to transitions between stance and swing.
... Recent research on WSS for biomedical applications can be divided into two major areas. One area focuses on recognition of daily activities, such as walking feature recognition [2], [3], walking condition classification [1], [4]- [6] and gait phase detection [7]- [10], in which the kinematic data obtained from inertial sensors (accelerometers or gyroscopes) are directly used as inputs to inference techniques. The second area focuses on accurate measurement of human motion data such as joint angles, or 3D body segment positions and orientations. ...
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There are many important challenges in gait analysis, which has many applications in healthcare, rehabilitation, therapy, and exercise training. However, gait analysis is typically performed in a gait laboratory, which is inaccessible to the general population and is not available in natural gait environments (e.g., outdoors). In this paper, we discuss the development of a high-fidelity, cost-effective, wireless sensor network to address the challenge of efficient gait monitoring in real-world walking scenarios. The sensor network is designed in a modular way to capture plantar forces and knee angle, angular velocity, and angular acceleration. A force module called a smart insole is designed to measure the plantar forces. The module is comprised of force sensitive resistors (FSRs) and a signal conditioning circuit. Various signal conditioning techniques, including a novel technique called transfrequency, are investigated to provide a linear mapping for FSR measurements and to provide data acquisition fidelity. The motion module includes a low-cost inertial measurement unit (IMU) augmented with a Kalman filter to provide filtered knee kinematics. A qualitative evaluation of the sensor network communication module is achieved by considering the internal communication protocols between the modules and the external wireless transmission protocol used to deliver data to an end-point terminal PC. Experiments are conducted to validate the motion and force modules. Then, the overall, integrated system is compared to gold standard laboratory results, demonstrating a successful application for gait identification. The results show that the sensor network accurately captures important gait parameters and features.
... Inertial sensors are also extensively used for ADL analysis, balance assessment and gait analysis of older people . The use of a Kinect sensor in gait analysis has gained attention of researchers for gait pattern recognition and classification [24,25]. ...
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Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries. A quantitative measure of spasticity using body-worn sensors is important in order to assess rehabilitative motor training and to adjust the rehabilitative therapy accordingly. We present a new approach to spasticity detection using the Integrated Posture and Activity NEtwork by Medit Aachen (IPANEMA) body sensor network (BSN). For this, a new electromyography (EMG) sensor node was developed and employed in human locomotion. Following an analysis of the clinical gait data of patients with unilateral cerebral palsy, a novel algorithm was developed based on the idea to detect co-activation of antagonistic muscle groups as observed in the exaggerated stretch reflex with associated joint rigidity. The algorithm applies a cross-correlation function to the EMG signals of two antagonistically working muscles and subsequent weighting using a Blackman window. The result is a co-activation index which is also weighted by the signal equivalent energy to exclude positive detection of inactive muscles. Our experimental study indicates good performance in the detection of co-active muscles associated with spasticity from clinical data as well as measurements from a BSN in qualitative comparison with the Modified Ashworth Scale (MAS) as classified by clinical experts. Possible applications of the new algorithm include (but are not limited to) use in robotic sensorimotor therapy to reduce the effect of spasticity.
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BACKGROUND, gait event detection is important for diagnosis and evaluation. This is a challenging endeavor due to subjectivity, high amount of data, among other problems. ANFIS, ARX, OE, NARX and models based on NN were developed in order to detect gait events without the problems mentioned. OBJECTIVE, to compare developed models’ performance and determinate the most suitable model for gait events detection. METHODS, knee joint angle, heel foot switch and toe foot switch during normal walking in a treadmill were collected from a healthy volunteer. Gait events were classified by three experts in human motion. Experts’ mean classification was obtained and all models were trained and tested with the collected data and experts’ mean classification. Fit percentage was obtained to evaluate models performance. RESULTS, Fit percentage was: ANFIS: 79.49%, ARX: 68.8%, OE: 71.39%, NARX: 88.59%, NNARX: 67.66%, NNRARX: 68.25% and NNARMAX: 54.71%.DISCUSSION, NARX had the best performance for gait events classification. For ARX and OE, previous filtering is needed. NN’s models showed the best performance for high frequency components. ANFIS and NARX were able to integrate criteria from three experts for gait analysis. CONCLUSION, NARX and ANFIS are suitable for gait event identification. Test with additional subjects is needed.
Article
This study aims to validate a commercially available inertial sensor based motion capture system, Xsens MVN BIOMECH using its native protocols, against a camera-based motion capture system for the measurement of joint angular kinematics. Performance was evaluated by comparing waveform similarity using range of motion, mean error and a new formulation of the coefficient of multiple correlation (CMC). Three dimensional joint angles of the lower limbs were determined for ten healthy subjects while they performed three daily activities: level walking, stair ascent, and stair descent. Under all three walking conditions, the Xsens system most accurately determined the flexion/extension joint angle (CMC > 0.96) for all joints. The joint angle measurements associated with the other two joint axes had lower correlation including complex CMC values. The poor correlation in the other two joint axes is most likely due to differences in the anatomical frame definition of limb segments used by the Xsens and Optotrak systems. Implementation of a protocol to align these two systems is necessary when comparing joint angle waveforms measured by the Xsens and other motion capture systems.
Article
Tradition gait analysis systems capture the image of a walking subject from either front view or side view. Since the walking direction allowed by the systems is highly restricted, they are inconvenient for long-term evaluation in casual environments (such as home). This study proposes a human gait analysis system with much less restriction on walking direction. In the system, we use the images obtained from multi-viewing angles and performs human gait analysis based on a set of chosen features, including the center of gravity (COG) and pace length, obtained from human silhouette images. The system successfully extracts gait features from various walking directions and integrates the features obtained from two cameras having orthogonal views. The intergration principle is discussed in details. The integrated feature is then compared with the ideal gait feature obtained from the camera whose viewing direction is perpendicular to the walking path, resulting in very high correlation. This study shows that a vision-based gait analyzer with two orthogonally arranged cameras has the potential to remove the walking direction restriction.
Article
This paper presents a walking pattern classification and a walking distance estimation algorithm using gait phase information. A gait phase information retrieval algorithm was developed to analyze the duration of the phases in a gait cycle (i.e., stance, push-off, swing, and heel-strike phases). Based on the gait phase information, a decision tree based on the relations between gait phases was constructed for classifying three different walking patterns (level walking, walking upstairs, and walking downstairs). Gait phase information was also used for developing a walking distance estimation algorithm. The walking distance estimation algorithm consists of the processes of step count and step length estimation. The proposed walking pattern classification and walking distance estimation algorithm have been validated by a series of experiments. The accuracy of the proposed walking pattern classification was 98.87%, 95.45%, and 95.00% for level walking, walking upstairs, and walking downstairs, respectively. The accuracy of the proposed walking distance estimation algorithm was 96.42% over a walking distance.
Article
Clinically feasible methods of assessing postural control such as timed standing balance and functional reach tests provide important information, however, they cannot accurately quantify specific postural control mechanisms. The Microsoft Kinect™ system provides real-time anatomical landmark position data in three dimensions (3D), and given that it is inexpensive, portable and simple to setup it may bridge this gap. This study assessed the concurrent validity of the Microsoft Kinect™ against a benchmark reference, a multiple-camera 3D motion analysis system, in 20 healthy subjects during three postural control tests: (i) forward reach, (ii) lateral reach, and (iii) single-leg eyes-closed standing balance. For the reach tests, the outcome measures consisted of distance reached and trunk flexion angle in the sagittal (forward reach) and coronal (lateral reach) planes. For the standing balance test the range and deviation of movement in the anatomical landmark positions for the sternum, pelvis, knee and ankle and the lateral and anterior trunk flexion angle were assessed. The Microsoft Kinect™ and 3D motion analysis systems had comparable inter-trial reliability (ICC difference=0.06±0.05; range, 0.00-0.16) and excellent concurrent validity, with Pearson's r-values >0.90 for the majority of measurements (r=0.96±0.04; range, 0.84-0.99). However, ordinary least products analyses demonstrated proportional biases for some outcome measures associated with the pelvis and sternum. These findings suggest that the Microsoft Kinect™ can validly assess kinematic strategies of postural control. Given the potential benefits it could therefore become a useful tool for assessing postural control in the clinical setting.
Conference Paper
In this paper, a novel robust markerless image processing system capable of extracting gait features which can be used for gait analysis is presented. The presented system can deal with images of persons captured in natural indoor scenes. The system's robustness against external influences and different person appearance is achieved by employing the idea of improving the image processing robustness by including feedback control at the image segmentation level. The effectiveness of the proposed system is demonstrated by the comparison of gait features, namely knee angles, extracted automatically with the features directly measured using a goniometer. Also a small database is created to extract the gait pattern of healthy subjects. The obtained data is compared to data from medical literature and is also compared to data obtained from persons having a pathological gait.
Article
This study evaluated a modified, timed version of the "Get-Up and Go" Test (Mathias et al, 1986) in 60 patients referred to a Geriatric Day Hospital (mean age 79.5 years). The patient is observed and timed while he rises from an arm chair, walks 3 meters, turns, walks back, and sits down again. The results indicate that the time score is (1) reliable (inter-rater and intra-rater); (2) correlates well with log-transformed scores on the Berg Balance Scale (r = -0.81), gait speed (r = -0.61) and Barthel Index of ADL (r = -0.78); and (3) appears to predict the patient's ability to go outside alone safely. These data suggest that the timed "Up & Go" test is a reliable and valid test for quantifying functional mobility that may also be useful in following clinical change over time. The test is quick, requires no special equipment or training, and is easily included as part of the routine medical examination.
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