Multilayer perceptron neural network classification for human vertical ground reaction forces

Goh, Kwang Leng; Lim, King Hann; Gopalai, Alpha Agape; Chong, Yu Zheng

doi:10.1109/iecbes.2014.7047559

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…However, such simple approaches are common in clinical practice as they overcome the before-mentioned limitations of 3DGA. To date, few attempts have been published arXiv:1712.06405v2 [cs.CV] 24 Dec 2017 that use only GRF data for automated gait pattern classification [16], [23]. Most of these gait classification approaches show promising results.…”

Section: Introductionmentioning

confidence: 99%

“…The presented approach builds upon the aforementioned studies, e.g. [16], [17], [23], investigates the suitability of frequently used state-of-theart GRF parameterization techniques for gait classification and analyzes their discriminative power. In the experiments we evaluate the individual representations on a large-scale and real-world dataset for different classification tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Such small datasets make it difficult to train robust and reliable classifiers that are applicable in complex real-world scenarios. Furthermore, a majority of studies [10], [17], [23] relies solely on the vertical ground reaction force for classification purposes, rather than considering all available GRF components, including the center of pressure (COP), for a more conclusive picture of the underlying gait pattern. Previous classification attempts mainly focused on the differentiation between specific diseases rather than drawing a distinction between functional gait disorders.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Classification of Functional Gait Disorders

Slijepčević

Zeppelzauer

Gorgas

et al. 2018

IEEE J. Biomed. Health Inform.

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This paper proposes a comprehensive investigation of the automatic classification of functional gait disorders (GDs) based solely on ground reaction force (GRF) measurements. The aim of this study is twofold: first, to investigate the suitability of the state-of-the-art GRF parameterization techniques (representations) for the discrimination of functional GDs; and second, to provide a first performance baseline for the automated classification of functional GDs for a large-scale dataset. The utilized database comprises GRF measurements from 279 patients with GDs and data from 161 healthy controls (N). Patients were manually classified into four classes with different functional impairments associated with the "hip", "knee", "ankle", and "calcaneus". Different parameterizations are investigated: GRF parameters, global principal component analysis (PCA) based representations, and a combined representation applying PCA on GRF parameters. The discriminative power of each parameterization for different classes is investigated by linear discriminant analysis. Based on this analysis, two classification experiments are pursued: distinction between healthy and impaired gait (N versus GD) and multiclass classification between healthy gait and all four GD classes. Experiments show promising results and reveal among others that several factors, such as imbalanced class cardinalities and varying numbers of measurement sessions per patient, have a strong impact on the classification accuracy and therefore need to be taken into account. The results represent a promising first step toward the automated classification of GDs and a first performance baseline for future developments in this direction.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Classification of Functional Gait Disorders

Slijepčević

Zeppelzauer

Gorgas

et al. 2018

IEEE J. Biomed. Health Inform.

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“…The limitation of the decision tree lies in model updating and once the decision tree model is made, it might be costly to update the model to suit new training examples (Su, Tong, & Ji, 2014). Lastly, we used the Multilayer Perceptron (MLP) neural network classifier for the classification task due to its flexibility structure and nonlinearity transformation to accommodate various patterns (Goh et al, 2014). The number of neurons in the hidden layer (hidden nodes) affect the performance result; the least number of nodes will result in under fitting and more nodes will result in over fitting.…”

Section: Human Activity Recognition Applicationsmentioning

confidence: 99%

Multi-Label Classification for Physical Activity Recognition From Various Accelerometer Sensor Positions

Mohamed

Zainudin

Sulaiman

et al. 2018

JICT

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In recent years, the use of accelerometers embedded in smartphones for Human Activity Recognition (HAR) has been well considered. Nevertheless, the role of the sensor placement is yet to be explored and needs to be further investigated. In this study, we investigated the role of sensor placements for recognizing various types of physical activities using the accelerometer sensor embedded in the smartphone. In fact, most of the reported work in HAR utilized traditional multi-class classification approaches to determine the types of activities. Hence, this study was to recognize the activity based on the best sensor placements that are appropriate to the activity performed. The traditional multi-class classification approach required more manual work and was time consuming to run the experiment separately. Thus, this study proposed the multilabel classification technique with the Label Combination (LC) approach in order to tackle this issue. The result was compared

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“…[2] Bai et al has used image classification based on shapes and texture. It has been done by the feature extraction process, the purpose of feature extraction is to determine the most relevant and the least amount of data representation of the image characteristics in order to minimize the within-class pattern variability, whilst, enhancing the between-class pattern variability [3].Alsmandi et al has done Fish recognition based on the combination between robust features selection from the size and shape measurement using neural network [4]. F. Storbecka et al used a fish image which was easy to extract a fish region with a white background or uniform background for automatic processing.…”

mentioning

confidence: 99%

Detection and Recognition of Bangladeshi Fishes Using Surf and Convolutional Neural Network.

Pavel¹

2019

IJAR

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This paper represents a model to detection and recognize local fishes of Bangladesh implementing image processing and neural networking approaches. The aim of the research work is to apply computer vision and AI techniques so that people of next generation can recognize Bangladeshi fishes as most of the young people in city, have less idea to classify traditional and deshi fishes. We implemented our custom Dataset consisting of 400 sample images for the experiment method to measure out its credibility. In the proposed, model sequential grassfire algorithm is used along with pre-processing techniques like noise cancelation, gray scaling, flood-fill method, binarization to detect and analysis shape of fish. Further, to do classification and recognition of the detected fishes, convolutional neural network (CNN) and method of Speeded up robust feature (SURF) had been applied to visualize difference between to techniques. CNN architecture got better accuracy with 90.9% score for recognition and classify fishes where SURF algorithm visualize better recognition matching putative points after extracting features.

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Multilayer perceptron neural network classification for human vertical ground reaction forces

Cited by 13 publications

References 11 publications

Automatic Classification of Functional Gait Disorders

Automatic Classification of Functional Gait Disorders

Multi-Label Classification for Physical Activity Recognition From Various Accelerometer Sensor Positions

Detection and Recognition of Bangladeshi Fishes Using Surf and Convolutional Neural Network.

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