Real-time control of active ankle foot orthosis using LabVIEW and Compact-RIO

Kanthi, M; Karteek, I.S.V.; Mruthyunjaya, H.S; George, V I

doi:10.1109/icobe.2012.6179024

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Computational methodologies for gait phase detection fall into two major categories. Firstly, the post-processing analysis uses algorithms which partition the gait phases through the threshold selection of raw data [ 20 – 22 ]. Secondly, the procedure is based on matching-learning schemes which extract patterns on the basis of Support Vector Machines (SVM) [ 23 ], Linear Discriminant Analysis (LDA) [ 24 ], Gaussian Mixture Model (GMM) [ 25 ], and, finally, Hidden Markov Model (HMM) [ 5 , 16 , 26 , 27 ]; this procedure recently received a great deal of interest for its potential better performance.…”

Section: Introductionmentioning

confidence: 99%

A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network

Taborri

Rossi

Palermo

et al. 2014

Sensors

109

101

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In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM) applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs) during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98) for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95) when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints.

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Section: Introductionmentioning

confidence: 99%

A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network

Taborri

Rossi

Palermo

et al. 2014

Sensors

109

101

View full text Add to dashboard Cite

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Design and evaluation of resilient infrastructures systems for smart cities

Pacheco

Tunc

Hariri

2016

2016 IEEE International Smart Cities Conference (ISC2)

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A Zero-Crossing Detection System Based on FPGA to Measure the Angular Vibrations of Rotating Shafts

Addabbo

Biondi

Cioncolini

et al. 2014

IEEE Trans. Instrum. Meas.

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In this paper, the efficient implementation of a reliable measurement device to measure the angular velocity vibrations of rotating shafts is discussed. The solution is particularly suitable to be fit in measurement embedded systems equipped with field-programmable gate arrays (FPGAs). The proposed measurement method is analyzed from a theoretical point of view, focusing the study on the frequency response of the technique. On the basis of the theoretical results, a reliable and low-complexity design of the measurement device has been proposed. In the adopted solution, the high clock rates of FPGAs are exploited to increase the accuracy of the zero-crossing times estimation, for a given A/D acquisition frequency, using an upsampling approach. The experimental results confirm the validity of the technique. The upsampling method allows an increase of the measurement accuracy and an improvement of about 10 dB for the output signal-to-noise ratio over a wide range of vibration frequencies

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Real-time control of active ankle foot orthosis using LabVIEW and Compact-RIO

Cited by 3 publications

References 5 publications

A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network

A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network

Design and evaluation of resilient infrastructures systems for smart cities

A Zero-Crossing Detection System Based on FPGA to Measure the Angular Vibrations of Rotating Shafts

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