Introduction and application of an automatic gait recognition method to diagnose movement disorders that arose of similar causes

Banaie, Masood; Pooyan, Mohammad; Mikaili, Mohammad

doi:10.1016/j.eswa.2010.12.091

Cited by 26 publications

(17 citation statements)

References 15 publications

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“…There are many approaches being successfully applied in diagnosing NDs which include the kernel Fisher discriminant (KFD), the naive Bayesian approach (NB), support vector machine (SVM) and nearest neighbor (NN) [3,[14][15][16]. In addition, neural networks have also been adopted in this field [4,17].…”

Section: Introductionmentioning

confidence: 99%

LSTM for diagnosis of neurodegenerative diseases using gait data

Zhao

et al. 2018

Ninth International Conference on Graphic and Image Processing (ICGIP 2017)

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Neurodegenerative diseases (NDs) usually cause gait disorders and postural disorders, which provides an important basis for NDs diagnosis. By observing and analyzing these clinical manifestations, medical specialists finally give diagnostic results to the patient, which is inefficient and can be easily affected by doctors' subjectivity. In this paper, we propose a two-layer Long Short-Term Memory (LSTM) model to learn the gait patterns exhibited in the three NDs. The model was trained and tested using temporal data that was recorded by force-sensitive resistors including time series, such as stride interval and swing interval. Our proposed method outperforms other methods in literature in accordance with accuracy of the predicted diagnostic result. Our approach aims at providing the quantitative assessment so that to indicate the diagnosis and treatment of these neurodegenerative diseases in clinic.

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Section: Introductionmentioning

confidence: 99%

LSTM for diagnosis of neurodegenerative diseases using gait data

Zhao

et al. 2018

Ninth International Conference on Graphic and Image Processing (ICGIP 2017)

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“…And the results were compared with one research in Table 4. Banaie et al, [8] Original LSTM The proposed method 86.96% 89.58% 95.67%…”

Section: Classification Of Ndsmentioning

confidence: 99%

“…Recently, machine learning technologies have been effectively applied to study the gait variability in neurological diseases including the kernel Fisher discriminant (KFD), the naive Bayesian approach (NB), support vector machine 2 (SVM) and nearest neighbor (NN) [5][6][7][8], neural networks [9,10]. However, these researches are not specifically designed to cope with temporal data, whereas the gait recorded by devices (cameras, force sensors) contains important temporal information which is significant for NDs diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Dual channel LSTM based multi-feature extraction in gait for diagnosis of Neurodegenerative diseases

Zhao

Dong

et al. 2018

Knowledge-Based Systems

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The performance of gait disturbances differ in various Neurodegenerative diseases (NDs), which is an important basis for the diagnosis of NDs. In the diagnosis, doctors can judge disease state by observing patients' gait features without quantification, such a subjective diagnosis has been seen as a problem because diagnostic results may differ among doctors. Moreover, there are some irresistible factors such as fatigue may effects diagnostic procedure. To make use of these observations, we build an automatic deep model based on Long Short-Term Memory (LSTM) for the gait recognition problem. In our model, a dual channel LSTM model is designed to combine time series and force series recorded from NDs patients for whole gait understanding. Experimental results demonstrate that our proposed model improves gait recognition performance compared to baseline methods. We believe the quantitative evaluation provided by our method will assist clinical diagnosis of Neurodegenerative diseases.

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“…This has led to a great deal of work in feature extraction within medical datasets. One example of this is the discrete cosine transform (DCT) algorithm that decreases the number of features and the computation time when processing signals (Banaie et al, 2011). DCT is used to calculate the trapped zone, under the curve, in special bands.…”

Section: Classification Algorithms For Neurodegenerative Diseasesmentioning

confidence: 99%

“…The results show that the quadratic Bayes normal classifier is better at identifying different neurodegenerative diseases compared to others. However, they have only evaluated this approach using two feature datasets (Banaie et al, 2011).…”

Section: Classification Algorithms For Neurodegenerative Diseasesmentioning

confidence: 99%

A classifier fusion strategy to improve the early detection of neurodegenerative diseases

Iram

Fergus

Al‐Jumeily

et al. 2015

IJAISC

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People in developed countries are living longer, and this has resulted in the prevalence of age-related diseases like Alzheimer's and dementia. Many believe that the early detection of neurodegenerative diseases will provide a much more sustainable framework for dealing with age-related diseases in the future. This paper considers this idea and proposes a new classifier fusion strategy that combines classification algorithms and rules (voting, product, mean, median, maximum and minimum) to measure specific behaviours in people suffering with neurodegenerative diseases. More specifically, the fusion strategy analyses the stride-to-stride intervals in gait and its correlation with neurological functions. This approach is compared with base level classifiers (a single classification algorithm) using a set of feature vectors associated with gait patterns obtained from neurodegenerative patients and healthy people. The results show that the fusion strategy improves classification. Our experiments successfully show that a fusion strategy generates better results and classifies subjects more accurately than base level classifiers.Keywords: combining classifiers; pattern recognition; behaviour classification; machine learning; Huntington's disease; Parkinson's disease amyotrophic lateral sclerosis; ALS; movement signals; artificial intelligence.Reference to this paper should be made as follows: Iram, S., Fergus, P., Al-Jumeily, D., Hussain, A. and Randles, M. (2015) 'A classifier fusion strategy to improve the early detection of neurodegenerative diseases', Int.

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Introduction and application of an automatic gait recognition method to diagnose movement disorders that arose of similar causes

Cited by 26 publications

References 15 publications

LSTM for diagnosis of neurodegenerative diseases using gait data

LSTM for diagnosis of neurodegenerative diseases using gait data

Dual channel LSTM based multi-feature extraction in gait for diagnosis of Neurodegenerative diseases

A classifier fusion strategy to improve the early detection of neurodegenerative diseases

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