The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jun-ming Lu - One of the best experts on this subject based on the ideXlab platform.
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real time Gait Cycle parameter recognition using a wearable accelerometry system
Sensors, 2011Co-Authors: Che-chang Yang, Kao-shang Shih, Jun-ming LuAbstract:This paper presents the development of a wearable accelerometry system for real-time Gait Cycle parameter recognition. Using a tri-axial accelerometer, the wearable motion detector is a single waist-mounted device to measure trunk accelerations during walking. Several Gait Cycle parameters, including cadence, step regularity, stride regularity and step symmetry can be estimated in real-time by using autocorrelation procedure. For validation purposes, five Parkinson’s disease (PD) patients and five young healthy adults were recruited in an experiment. The Gait Cycle parameters among the two subject groups of different mobility can be quantified and distinguished by the system. Practical considerations and limitations for implementing the autocorrelation procedure in such a real-time system are also discussed. This study can be extended to the future attempts in real-time detection of disabling Gaits, such as festinating or freezing of Gait in PD patients. Ambulatory rehabilitation, Gait assessment and personal telecare for people with Gait disorders are also possible applications.
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Real-time Gait Cycle parameters recognition using a wearable motion detector
Proceedings 2011 International Conference on System Science and Engineering, 2011Co-Authors: Che-chang Yang, Jun-ming Lu, Kao-shang Shih, Lung ChanAbstract:This paper presents the use of an accelerometry-based wearable motion detector for real-time recognizing Gait Cycle parameters of Parkinson's disease (PD) patients. The wearable motion detector uses a tri-axial accelerometer to measure trunk accelerations during walking. By using the autocorrelation procedure, several Gait Cycle parameters including cadence, Gait regularity, and symmetry can be derived in real-time from the measured trunk acceleration data. The Gait Cycle parameters derived from 5 elder PD patients and 5 young healthy subjects are also compared. The measures of the Gait Cycle parameters between the PD patients and the healthy subjects are distinct and therefore can be quantified and distinguished, which indicates that detection of abnormal Gaits of PD patients in real-time is also possible. The wearable motion detector developed in this paper is a practical system that enables quantitative and objective mobility assessment. The possible applications of this system are also discussed.
Gerhard Rigoll - One of the best experts on this subject based on the ideXlab platform.
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person identification from partial Gait Cycle using fully convolutional neural networks
Neurocomputing, 2019Co-Authors: Maryam Babaee, Gerhard RigollAbstract:Abstract Gait as a biometric property for person identification plays a key role in video surveillance and security applications. In Gait recognition, normally, Gait feature such as Gait Energy Image (GEI) is extracted from one full Gait Cycle. However in many circumstances, such a full Gait Cycle might not be available due to occlusion. Thus, the GEI is not complete giving rise to a degrading in Gait-based person identification rate. In this paper, we address this issue by proposing a novel method to identify individuals from Gait feature when a few (or even single) frame(s) is available. To do so, we propose a deep learning-based approach to transform incomplete GEI to the corresponding complete GEI obtained from a full Gait Cycle. More precisely, this transformation is done gradually by training several auto encoders independently and then combining these as a uniform model. Experimental results on two public Gait datasets, namely OULP and Casia-B demonstrate the validity of the proposed method in dealing with very incomplete Gait Cycles.
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Gait energy image reconstruction from degraded Gait Cycle using deep learning
European Conference on Computer Vision, 2018Co-Authors: Maryam Babaee, Gerhard RigollAbstract:Gait energy image (GEI) is considered as an effective Gait representation for Gait-based human identification. In Gait recognition, normally, GEI is computed from one full Gait Cycle. However in many circumstances, such a full Gait Cycle might not be available due to occlusion. Thus, the GEI is not complete, giving a rise to degrading Gait identification rate. In this paper, we address this issue by proposing a novel method to reconstruct a complete GEI from a few frames of Gait Cycle. To do so, we propose a deep learning-based approach to transform incomplete GEI to the corresponding complete GEI obtained from a full Gait Cycle. More precisely, this transformation is done gradually by training several fully convolutional networks independently and then combining these as a uniform model. Experimental results on a large public Gait dataset, namely OULP demonstrate the validity of the proposed method for Gait identification when dealing with very incomplete Gait Cycles.
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Gait Recognition from Incomplete Gait Cycle
2018 25th IEEE International Conference on Image Processing (ICIP), 2018Co-Authors: Maryam Babaee, Linwei Li, Gerhard RigollAbstract:In Gait recognition, which has been recently regarded as a biometric recognition tool, proposed approaches assume that an individual is observed for at least one Gait Cycle. However, in reality, there might be available only a few frames of full Gait Cycle of a subject due to occlusion. Therefore, Gait recognition systems would fail in these scenarios. In this paper, we propose a method to tackle this problem by proposing a Gait recognition algorithm from an incomplete Gait Cycle information. We achieve this by 1) creating an incomplete Energy Image (GEI) from a few available silhouettes of a subject and 2) reconstructing the complete GEI from incomplete GEI using a deep auto-encoder. The experimental results on a public Gait dataset demonstrate the validity of the proposed method.
Che-chang Yang - One of the best experts on this subject based on the ideXlab platform.
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real time Gait Cycle parameter recognition using a wearable accelerometry system
Sensors, 2011Co-Authors: Che-chang Yang, Kao-shang Shih, Jun-ming LuAbstract:This paper presents the development of a wearable accelerometry system for real-time Gait Cycle parameter recognition. Using a tri-axial accelerometer, the wearable motion detector is a single waist-mounted device to measure trunk accelerations during walking. Several Gait Cycle parameters, including cadence, step regularity, stride regularity and step symmetry can be estimated in real-time by using autocorrelation procedure. For validation purposes, five Parkinson’s disease (PD) patients and five young healthy adults were recruited in an experiment. The Gait Cycle parameters among the two subject groups of different mobility can be quantified and distinguished by the system. Practical considerations and limitations for implementing the autocorrelation procedure in such a real-time system are also discussed. This study can be extended to the future attempts in real-time detection of disabling Gaits, such as festinating or freezing of Gait in PD patients. Ambulatory rehabilitation, Gait assessment and personal telecare for people with Gait disorders are also possible applications.
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Real-time Gait Cycle parameters recognition using a wearable motion detector
Proceedings 2011 International Conference on System Science and Engineering, 2011Co-Authors: Che-chang Yang, Jun-ming Lu, Kao-shang Shih, Lung ChanAbstract:This paper presents the use of an accelerometry-based wearable motion detector for real-time recognizing Gait Cycle parameters of Parkinson's disease (PD) patients. The wearable motion detector uses a tri-axial accelerometer to measure trunk accelerations during walking. By using the autocorrelation procedure, several Gait Cycle parameters including cadence, Gait regularity, and symmetry can be derived in real-time from the measured trunk acceleration data. The Gait Cycle parameters derived from 5 elder PD patients and 5 young healthy subjects are also compared. The measures of the Gait Cycle parameters between the PD patients and the healthy subjects are distinct and therefore can be quantified and distinguished, which indicates that detection of abnormal Gaits of PD patients in real-time is also possible. The wearable motion detector developed in this paper is a practical system that enables quantitative and objective mobility assessment. The possible applications of this system are also discussed.
Adrian Steiner - One of the best experts on this subject based on the ideXlab platform.
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technical note validation of a semi automated software tool to determine Gait Cycle variables in dairy cows
Journal of Dairy Science, 2017Co-Authors: Maher Alsaaod, Ralf Kredel, Balthasar Hofer, Adrian SteinerAbstract:This paper presents the validation of a software tool called Cow-Gait-Analyzer (University of Bern, Switzerland) to determine Gait-Cycle variables in lame and non-lame dairy cows using features derived from low-cost, stand-alone 3-dimensional accelerometers (400 Hz). The Cow-Gait-Analyzer automatically extracts the relevant Gait events of foot load and toe off, which characterize Gait-Cycle duration, stance phase, and swing phase during walking. A nonautomatic step is visual inspection of the pedograms. If the software does not automatically choose the right peaks according to pedogram definitions, peaks can be manually chosen. We validated the algorithms by comparing the accelerometer data (pedogram) with the synchronized video data, which we used as a gold standard. We carried out the measurements at the metatarsal level of paired hind limbs during walking. We included 12 non-lame cows and 5 lame cows and expressed overall differences between the Cow-Gait-Analyzer and the gold standard as relative measurement error (RME). We analyzed 34 hind limbs with a mean of 9 Gait Cycles. The median RME for Gait-Cycle duration and stance phases were 0 and 1.69%, respectively. The peaks of Gait-Cycle variables showed RME of 0.67 and 0.24% for foot load and toe off, respectively. The semi-automated Cow-Gait-Analyzer can accurately determine Gait-Cycle variables in both lame and non-lame cows, and could be used to assess Gait patterns in routine clinical and research practice focusing on individual cows.
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the cow pedogram analysis of Gait Cycle variables allows the detection of lameness and foot pathologies
Journal of Dairy Science, 2017Co-Authors: Maher Alsaaod, Ralf Kredel, Michael Luternauer, T Hausegger, Adrian SteinerAbstract:Changes in Gait characteristics are important indicators in assessing the health and welfare of cattle. The aim of this study was to detect unilateral hind limb lameness and foot pathologies in dairy cows using 2 high-frequency accelerometers (400 Hz). The extracted Gait Cycle variables included temporal events (kinematic outcome = Gait Cycle, stance phase, and swing phase duration) and several peaks (kinetic outcome = foot load, toe-off). The study consisted of 2 independent experiments. Experiment 1 was carried out to compare the pedogram variables between the lateral claw and respective metatarsus (MT; n = 12) in sound cows (numerical rating system <3, n = 12) and the differences of pedogram variables across limbs within cows between lame cows (numerical rating system ≥3, n = 5) and sound cows (n = 12) using pedogram data that were visually compared with the synchronized cinematographic data. Experiment 2 was carried out to determine the differences across limbs within cows between cows with foot lesions (n = 12) and without foot lesions (n = 12) using only pedogram data. A receiver operator characteristic analysis was used to determine the performance of selected pedogram variables at the cow level. The pedogram of the lateral claw of sound cows revealed similarities of temporal events (Gait Cycle duration, stance and swing phases) but higher peaks (toe-off and foot load) as compared with the pedogram of the respective MT. In both experiments, comparison of the values between groups showed significantly higher values in lame cows and cows with foot lesions for all Gait Cycle variables. The optimal cutoff value of the relative stance phase duration for identifying lame cows was 14.79% and for cows with foot lesions was 2.53% with (both 100% sensitivity and 100% specificity) in experiments 1 and 2, respectively. The use of accelerometers with a high sampling rate (400 Hz) at the level of the MT is a promising tool to indirectly measure the kinematic variables of the lateral claw and to detect unilateral hind limb lameness and hind limb pathologies in dairy cows and is highly accurate.
David Howard - One of the best experts on this subject based on the ideXlab platform.
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predictive modelling of human walking over a complete Gait Cycle
2nd International Conference on Simulation for Biomechanics Biomaterial and Biomedicine, 2009Co-Authors: Lei Ren, Richard Jones, David HowardAbstract:An inverse dynamics multi-segment model of the body was combined with optimisation techniques to simulate normal walking in the sagittal plane on level ground. Walking is formulated as an optimal motor task subject to multiple constraints with minimisation of mechanical energy expenditure over a complete Gait Cycle being the performance criterion. All segmental motions and ground reactions were predicted from only three simple Gait descriptors (inputs): walking velocity, Cycle period and double stance duration. Quantitative comparisons of the model predictions with Gait measurements show that the model reproduced the significant characteristics of normal Gait in the sagittal plane. The simulation results suggest that minimising energy expenditure is a primary control objective in normal walking. However, there is also some evidence for the existence of multiple concurrent performance objectives.
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whole body inverse dynamics over a complete Gait Cycle based only on measured kinematics
Journal of Biomechanics, 2008Co-Authors: Richard Jones, David HowardAbstract:Abstract This paper presents a three-dimensional (3D) whole body multi-segment model for inverse dynamics analysis over a complete Gait Cycle, based only on measured kinematic data. The sequence of inverse dynamics calculations differs significantly from the conventional application of inverse dynamics using force plate data. A new validated “Smooth Transition Assumption” was used to solve the indeterminacy problem in the double support phase. Kinematic data is required for all major body segments and, hence, a whole body Gait measurement protocol is presented. Finally, sensitivity analyses were conducted to evaluate the effects of digital filtering and body segment parameters on the accuracy of the prediction results. The model gave reasonably good estimates of sagittal plane ground forces and moment; however, the estimates in the other planes were less good, which we believe is largely due to their small magnitudes in comparison to the sagittal forces and moment. The errors observed are most likely caused by errors in the kinematic data resulting from skin movement artefact and by errors in the estimated body segment parameters. A digital filtering cut-off frequency of 4.5 Hz was found to produce the best results. It was also shown that errors in the mass properties of body segments can play a crucial role, with changes in properties sometimes having a disproportionate effect on the calculated ground reactions. The implication of these results is that, even when force plate data is available, the estimated joint forces are likely to suffer from similar errors.
