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Frequency evaluation of gait trunk acceleration signal: A longitudinal study
... Method. The original signal has noise, especially the acceleration signal noise is more serious, and it appears as a more serious glitch phenomenon on the signal graph [16]. These noises will have a great impact on the feature extraction and the final dance action recognition accuracy. ...
In order to improve the accuracy and timeliness of folk dance movement recognition, this paper proposes an improved MCM-SVM recognition model to recognize the lower limb human motion of ethnic dance in rural areas based on sensors. In order to recognize these actions, the SVM algorithm is used to identify the current action, and the MCM is used to optimize the recognition result. The experimental results show that the proposed improved model achieves higher recognition rate compared to the SVM algorithm for the recognition of different dance moves. The average recognition rate exceeds 93%, and the average recognition time is about 0.6 ms, which verifies the effectiveness of the proposed model. The proposed model will provide guidance and practicality for the design and construction of future dance movement recognition systems.
In order to improve the accuracy and timeliness of folk dance movement recognition, this paper proposes an improved MCM-SVM recognition model to recognize the lower limb human motion of ethnic dance in rural areas based on sensors. In order to recognize these actions, the SVM algorithm is used to identify the current action, and the MCM model is used to optimize the recognition result. The experimental results show that the proposed improved model achieves a higher recognition rate compared to the SVM algorithm for the recognition of different dance moves. The average recognition rate exceeds 93%, and the average recognition time is about 0.6 ms, which verifies the effectiveness of the proposed model. The proposed model will provide guidance and practicality for the design and construction of future dance movement recognition systems.
The aim of this study was to apply a well-documented IMU-based method to measure gait spatio-temporal parameters in a large number of healthy and gait-impaired subjects, and evaluate its robustness and validity across two clinical centers.
Background
Quantifying gait stability is a topic of high relevance and a number of possible measures have been proposed. The problem in validating these methods is the necessity to identify a-priori unstable individuals. Since proposed methods do not make any assumption on the characteristics of the subjects, the aim of the present study was to test the performance of gait stability measures on individuals whose gait is a-priori assumed unstable: toddlers at the onset of independent walking.
Methods
Ten toddlers, ten adults and ten elderly subjects were included in the study. Data from toddlers were acquired longitudinally over a 6-month period to test if the methods detected the increase in gait stability with experience, and if they could differentiate between toddlers and young adults. Data from elderly subjects were expected to indicate a stability value in between the other two groups. Accelerations and angular velocities of the trunk and of the leg were measured using two tri-axial inertial sensors. The following methods for quantifying gait stability were applied: stride time variability, Poincaré plots, harmonic ratio, short term Lyapunov exponents, maximum Floquet multipliers, recurrence quantification analysis and multiscale entropy. An unpaired t-test (level of significance of 5%) was performed on the toddlers and the young adults for each method and, for toddlers, for each evaluated stage of gait development.
Results
Methods for discerning between the toddler and the adult groups were: stride time variability, Poincaré plots, harmonic ratio, short term Lyapunov exponents (state space composed by the three linear accelerations of the trunk), recurrence quantification analysis and multiscale entropy (when applied on the vertical or on the antero-posterior L5 accelerations).
Conclusions
Results suggested that harmonic ratio and recurrence quantification analysis better discern gait stability in the analyzed subjects, differentiating not only between unstable toddlers and stable healthy adults, but also evidencing the expected trend of the toddlers towards a higher stability with walking experience, and indicating elderly subjects as stable as or less stable than young adults.
It is proposed to scale gait data (e.g. steplength, velocity, force, moment, work) by leg length and body mass. It is concluded that temporal parameters are affected by the scaling as well.
One correction: dimensionless power P^ = P/(m.g^3/2.l^1/2
The smartphone, which contains inertial sensors, is currently available and affordable device and has the potential to provide a self-assessment tool for health management. The aims of this study were to use a smartphone to record trunk acceleration during walking and to compare accelerometry variables between poststroke subjects with and without a history of falling.
This cross-sectional study was conducted in 2 day care centers for elderly adults. Twenty-four community-dwelling adults with chronic stroke (mean age, 71.6 ± 9.7 years; mean time since stroke, 68.5 ± 38.7 months) were enrolled. Acceleration of the trunk during walking was recorded in the anteroposterior and mediolateral directions and quantified using the autocorrelation coefficient, harmonic ratio, and interstride variability (coefficient of variation of root mean square acceleration). Fall history in the past 12 months was obtained by self-report.
Eleven participants (45.8%) reported at least one fall in the past 12 months and were classified as fallers. Fallers exhibited significantly higher interstride variability of mediolateral trunk acceleration than nonfallers. In the logistic regression analysis, interstride variability of mediolateral trunk acceleration was significantly associated with fall history (adjusted odds ratio, 1.462; 95% confidence interval, 1.009-2.120). The area under the receiver operating characteristic curve for interstride variability of mediolateral trunk acceleration to discriminate fallers from nonfallers was .745 (95% confidence interval, .527-.963).
The results suggest that quantitative gait assessment using a smartphone can provide detailed and objective information about subtle changes in the gait pattern of stroke subjects at risk of falling.
Copyright © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.
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Trojaniello D, et al. J. Neuroeng. Rehabil 2014;11(1):152.
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O13 Time-frequency analysis of surface EMG signals for maximum energy localization during gait
http://dx.doi.org/10.1016/j.gaitpost.2017.07.057
O13
Time-frequency analysis of surface EMG signals
for maximum energy localization during gait