Multiple Classification of Gait Using Time-Frequency Representations and Deep Convolutional Neural Networks

Jung, Dawoon; Nguyen, Mau Dung; Park, Mina; Kim, Jin Wook; Mun, Kyung-Ryoul

doi:10.1109/tnsre.2020.2977049

Cited by 14 publications

(8 citation statements)

References 27 publications

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“…Classification of a quality-related outcome [97] x x [98] x [99] x [100] x [101] x [102] x [103] x [104] x [105] x [106] x [107] x [108] x [109] x [110] x [111] x HMM: hidden Markov model; LSTM etc. : long short-term memory model/recurrent neural network/CNN-LSTM; CNN: convolutional neural network; Seq: sequence mapping; DTW: dynamic time warping; LCSS: longest common subsequence; overlap: overlap in the feature calculation; other: other classification method/no details.…”

Section: Refmentioning

confidence: 99%

Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review

et al. 2023

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Inertial measurement units (IMU) are widely used in sports applications to digitise human motion by measuring acceleration and rotational velocity in three-dimensional space. A common machine learning problem is the classification of human motion primitives from IMU data. In order to investigate the classification methods used in the existing literature and to analyse whether and how the time-dependent data structure is considered in the classification process of motion data analysis in sports, a scoping review was conducted. Based on a keyword search, articles from 2010 to 2021 were extracted, and 93 articles were relevant for data extraction. Over- and undersampling of data and data augmentation techniques were rarely used. The classification methods applied can be divided into three main branches: classic machine learning and deep learning models, threshold-based approaches, and dynamic time warping. The most often applied algorithms were support vector machines (SVM), followed by neural networks and k-nearest neighbours. In comparative works, when more than one classifier was applied, random forests, neural networks, boosting models and SVM were found to be the methods that achieved the highest accuracy. If the time-dependent data structure was taken into account, it was incorporated either within the models, for example, by using long-short-term memory models or within the feature calculation step by using rolling windows with an overlap, which was the most common method of considering the time dependency of the IMU data.

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Section: Refmentioning

confidence: 99%

Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review

et al. 2023

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“…Today, NNs are generally software simulations implemented by conventional computer programming. They are called artificial neural networks (ANNs) to differentiate from biological neural networks [18], [19]. The basic computational elements of an ANN are the nodes (or neurons).…”

Section: Deep-learning Systemsmentioning

confidence: 99%

“…By adopting a DL model, the algorithm itself is capable of determining whether a prediction is accurate or not. DL applications use a layered structure of algorithms called Artificial Neural Network (ANN) since they vaguely imitate the interconnected structure of the human brain to provide multi-level functionality [18], [19]. In this work, a monitoring system installed inside a house is proposed.…”

Section: Introductionmentioning

confidence: 99%

A New Convolutional Neural Network-Based System for NILM Applications

Ciancetta

Bucci

Fiorucci

et al. 2021

IEEE Trans. Instrum. Meas.

104

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Electrical load planning and demand response programs are often based on the analysis of individual load level measurements obtained from houses or buildings. The identification of individual appliances' power consumption is essential, since it allows improvements, which can reduce appliances' power consumption. In this paper, the problem of identifying the electrical loads connected to a house, starting from the total electric current measurement, is investigated. The proposed system is capable of extracting the energy demand of each individual device using a non-intrusive load monitoring (NILM) technique. A NILM algorithm based on a convolutional neural network is proposed. The proposed algorithm allows simultaneous detection and classification of events without having to perform double processing. As a result, the calculation times can be reduced. Another important advantage is that only the acquisition of current is required. The proposed measurement system is also described in this paper. Measurements are conducted using a test system, which is capable of generating the electrical loads found on a typical house. The most important experimental results are also included and discussed in the paper.

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“…For clinical physicians in vertigo clinics, the judgement of various vestibular exams was complicated and time-consuming. The artificial neural network (ANN) has been utilized to detect time-frequency plots in the pulse audiogram, the electroencephalography, and the gait analysis [19] , [20] , [21] . In this study, we took advantage of the ANN-assisted image classification of coherence plots to predict the hearing prognosis in patients with SSNHLV, facilitate clinical judgement, and develop the foundation of smart healthcare application in otoneurology.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Assisted Classification of Hearing Prognosis of Sudden Sensorineural Hearing Loss With Vertigo

Lin

Kang

et al. 2023

IEEE J. Transl. Eng. Health Med.

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This study aimed to determine the impact on hearing prognosis of the coherent frequency with high magnitude-squared wavelet coherence (MSWC) in video head impulse test (vHIT) among patients with sudden sensorineural hearing loss with vertigo (SSNHLV) undergoing high-dose steroid treatment. This study was a retrospective cohort study. SSNHLV patients treated at our referral center from December 2016 to December 2020 were examined. The cohort comprised 64 patients with SSNHLV undergoing high-dose steroid treatment. MSWC was measured by calculating the wavelet coherence analysis (WCA) at various frequencies from a vHIT. The hearing prognosis were analyzed using a multivariable Cox regression model and convolution neural network (CNN) of WCA. There were 64 patients with a male-to-female ratio of 1:1.67. The greater highest coherent frequency of the posterior semicircular canal (SCC) was associated with the complete recovery (CR) of hearing. After adjustment for other factors, the result remained robust (hazard ratio [HR] 2.11, 95% confidence interval [CI] 1.86-2.35). In the feature extraction with Resnet-50 and proceeding SVM in the horizontal image cropping style, the classification accuracy [STD] for (CR vs. partial + no recovery [PR + NR]), (over-sampling of CR vs. PR + NR), (extensive data extraction of CR vs. PR + NR), and (interpolation of time series of CR vs. PR + NR) were 83.6% [7.4], 92.1% [6.8], 88.9% [7.5], and 91.6% [6.4], respectively. The high coherent frequency of the posterior SCC was a significantly independent factor that was associated with good hearing prognosis in the patients who have SSNHLV. WCA may be provided with comprehensive ability in vestibulo-ocular reflex (VOR) evaluation. CNN could be utilized to classify WCA, predict treatment outcomes, and facilitate vHIT interpretation. Feature extraction in CNN with proceeding SVM and horizontal cropping style of wavelet coherence plot performed better accuracy and offered more stable model for hearing outcomes in patients with SSNHLV than pure CNN classification. Clinical and Translational Impact Statement—High coherent frequency in vHIT results in good hearing outcomes in SSNHLV and facilitates AI classification.

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Multiple Classification of Gait Using Time-Frequency Representations and Deep Convolutional Neural Networks

Cited by 14 publications

References 27 publications

Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review

Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review

A New Convolutional Neural Network-Based System for NILM Applications

Artificial Neural Network-Assisted Classification of Hearing Prognosis of Sudden Sensorineural Hearing Loss With Vertigo

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