Gait speeds classifications by supervised modulation based machine-learning using Kinect camera

Early indication of some diseases such as Parkinson and Multiple Sclerosis often manifests with walking difficulties. Gait analysis provides vital information for assessing the walking patterns during the locomotion, especially when the outcomes are quantitative measures. This paper explores methods that can respond to the changes in the gait features during the swing stage using Kinect Camera, a low cost, marker-free, and portable device offered by Microsoft. Kinect has been exploited for tracking the skeletal positional data of body joints to assess and evaluate the gait performance. Linear kinematic gait features are extracted to discriminate between walking speeds by using five supervised modulation based machine-learning classifiers as follow: Decision Trees (DT), linear/nonlinear Support Vector Machines (SVMs), subspace discriminant and k-Nearest Neighbour (k-NN). The role of modulation techniques such as Frequency Modulation (FM) for increasing the efficiency of classifiers have been explored. The experimental results show that all five classifiers can successfully distinguish gait futures signal associated with walking patterns with high accuracy (average expected value of 86.19% with maximum of 92.9%). This validates the capability of the presented methodology in detecting key “indicators” of health events. Keywords: Gait Analysis, Kinematic Gait Features, Amplitude and Frequency Modulations, Baseband Signal, Passband Mapping, Machine-Learning, Classification Techniqu

Gait Recognition from Motion Capture Data

Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU MoCap database show that the suggested method outperforms thirteen relevant methods based on geometric features and a method to learn the features by a combination of Principal Component Analysis and Linear Discriminant Analysis. The methods are evaluated in terms of the distribution of biometric templates in respective feature spaces expressed in a number of class separability coefficients and classification metrics. Results also indicate a high portability of learned features, that means, we can learn what aspects of walk people generally differ in and extract those as general gait features. Recognizing people without needing group-specific features is convenient as particular people might not always provide annotated learning data. As a contribution to reproducible research, our evaluation framework and database have been made publicly available. This research makes motion capture technology directly applicable for human recognition.Comment: Preprint. Full paper accepted at the ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans. 18 pages. arXiv admin note: substantial text overlap with arXiv:1701.00995, arXiv:1609.04392, arXiv:1609.0693

Using the Microsoft Kinect to assess human bimanual coordination

Optical marker-based systems are the gold-standard for capturing three-dimensional (3D) human kinematics. However, these systems have various drawbacks including time consuming marker placement, soft tissue movement artifact, and are prohibitively expensive and non-portable. The Microsoft Kinect is an inexpensive, portable, depth camera that can be used to capture 3D human movement kinematics. Numerous investigations have assessed the Kinect\u27s ability to capture postural control and gait, but to date, no study has evaluated it\u27s capabilities for measuring spatiotemporal coordination. In order to investigate human coordination and coordination stability with the Kinect, a well-studied bimanual coordination paradigm (Kelso, 1984, Kelso; Scholz, & Schöner, 1986) was adapted. ^ Nineteen participants performed ten trials of coordinated hand movements in either in-phase or anti-phase patterns of coordination to the beat of a metronome which was incrementally sped up and slowed down. Continuous relative phase (CRP) and the standard deviation of CRP were used to assess coordination and coordination stability, respectively.^ Data from the Kinect were compared to a Vicon motion capture system using a mixed-model, repeated measures analysis of variance and intraclass correlation coefficients (2,1) (ICC(2,1)).^ Kinect significantly underestimated CRP for the the anti-phase coordination pattern (p \u3c.0001) and overestimated the in-phase pattern (p\u3c.0001). However, a high ICC value (r=.097) was found between the systems. For the standard deviation of CRP, the Kinect exhibited significantly higher variability than the Vicon (p \u3c .0001) but was able to distinguish significant differences between patterns of coordination with anti-phase variability being higher than in-phase (p \u3c .0001). Additionally, the Kinect was unable to accurately capture the structure of coordination stability for the anti-phase pattern. Finally, agreement was found between systems using the ICC (r=.37).^ In conclusion, the Kinect was unable to accurately capture mean CRP. However, the high ICC between the two systems is promising and the Kinect was able to distinguish between the coordination stability of in-phase and anti-phase coordination. However, the structure of variability as movement speed increased was dissimilar to the Vicon, particularly for the anti-phase pattern. Some aspects of coordination are nicely captured by the Kinect while others are not. Detecting differences between bimanual coordination patterns and the stability of those patterns can be achieved using the Kinect. However, researchers interested in the structure of coordination stability should exercise caution since poor agreement was found between systems