5,090 research outputs found
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
Extraction of bodily features for gait recognition and gait attractiveness evaluation
This is the author's accepted manuscript. The final publication is available at Springer via
http://dx.doi.org/10.1007/s11042-012-1319-2. Copyright @ 2012 Springer.Although there has been much previous research on which bodily features are most important in gait analysis, the questions of which features should be extracted from gait, and why these features in particular should be extracted, have not been convincingly answered. The primary goal of the study reported here was to take an analytical approach to answering these questions, in the context of identifying the features that are most important for gait recognition and gait attractiveness evaluation. Using precise 3D gait motion data obtained from motion capture, we analyzed the relative motions from different body segments to a root marker (located on the lower back) of 30 males by the fixed root method, and compared them with the original motions without fixing root. Some particular features were obtained by principal component analysis (PCA). The left lower arm, lower legs and hips were identified as important features for gait recognition. For gait attractiveness evaluation, the lower legs were recognized as important features.Dorothy Hodgkin Postgraduate Award and HEFCE
A cane-based low cost sensor to implement attention mechanisms in telecare robots
Telepresence robots have been recently used for
Comprehensive Geriatric Assessment (CGA). Since the robot
can not track a person continuously, there are several strategies
to decide when to check them, from cyclic checks to simple
requests from users and/or caregivers. In order to adapt to the
user needs and condition, it is preferable to perform CGA as
soon as regularities appear. However, this requires detection
of potential issues in users to offer immediate service. In this
work we propose a new low cost force sensor system to detect
user’s condition and attract attention of CGA robots, so they
can perform a full examination on a need basis. The main
advantages of this system are: i) it can be attached to any
standard commercial cane; ii) its power consumption is very
reduced; and iii) it provides continuous information as long as
the user walks. It has been tested with several elderly volunteers
in care facilities. Results have proven that the sensor readings
are indeed correlated with the users’ condition.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tec
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