Wearable Accelerometer and sEMG-Based Upper Limb BSN for Tele-Rehabilitation

Baraka, Ahmed; Shaban, Heba; El-Nasr, Mohamad Abou; Attallah, Omneya

doi:10.3390/app9142795

Cited by 27 publications

(26 citation statements)

References 30 publications

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“…Preprocessing is used to enhance noisy signals and remove artifacts. Sources of noise and artifacts may include power-line interference, electrocardiography (ECG), electromyography (EMG), electrooculogram (EOG), and individuals’ movements [ 14 , 50 ]. In this study, the preprocessing phase is divided into four steps.…”

Section: Methodsmentioning

confidence: 99%

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A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

et al. 2020

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Motor deficiencies constitute a significant problem affecting millions of people worldwide. Such people suffer from a debility in daily functioning, which may lead to decreased and incoherence in daily routines and deteriorate their quality of life (QoL). Thus, there is an essential need for assistive systems to help those people achieve their daily actions and enhance their overall QoL. This study proposes a novel brain–computer interface (BCI) system for assisting people with limb motor disabilities in performing their daily life activities by using their brain signals to control assistive devices. The extraction of useful features is vital for an efficient BCI system. Therefore, the proposed system consists of a hybrid feature set that feeds into three machine-learning (ML) classifiers to classify motor Imagery (MI) tasks. This hybrid feature selection (FS) system is practical, real-time, and an efficient BCI with low computation cost. We investigate different combinations of channels to select the combination that has the highest impact on performance. The results indicate that the highest achieved accuracies using a support vector machine (SVM) classifier are 93.46% and 86.0% for the BCI competition III–IVa dataset and the autocalibration and recurrent adaptation dataset, respectively. These datasets are used to test the performance of the proposed BCI. Also, we verify the effectiveness of the proposed BCI by comparing its performance with recent studies. We show that the proposed system is accurate and efficient. Future work can apply the proposed system to individuals with limb motor disabilities to assist them and test their capability to improve their QoL. Moreover, the forthcoming work can examine the system’s performance in controlling assistive devices such as wheelchairs or artificial limbs.

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Section: Methodsmentioning

confidence: 99%

“…For the k-NN, the Euclidean distance was used. The three classifiers used in this study were implemented in MATLAB using the X, Y, Z libraries/packages versions X, Y, Z [ 50 , 62 , 63 ].…”

Section: Methodsmentioning

confidence: 99%

A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

et al. 2020

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“…There are several architectures for DL. Among them is the convolutional neural network (CNN), which is the most used architecture for medical problems, especially dealing with medical images [ 38 , 39 , 40 ]. A CNN contains a huge number of layers; thus, it is denoted deep networks.…”

Section: Methodsmentioning

confidence: 99%

MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

Attallah

2021

Diagnostics

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Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists’ expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.

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“…Moreover, choosing the appropriate feature extraction approach that has a strong capacity to extract significant features from such complicated patterns and textures ( Xie et al, 2019 ). Feature extraction is the main step in the accurate diagnosis of medical conditions ( Baraka et al, 2019 ) such as BC ( Ragab, Sharkas & Attallah, 2019 ), gastrointestinal diseases ( Khan et al, 2020 ; Ali et al, 2019 ; Khan et al, 2019 ; Attallah & Sharkas, 2021 ), heart diseases ( Attallah et al, 2017a ; Attallah & Ma, 2014 ; Karthikesalingam et al, 2015 ; Attallah & Ma, 2015 ), and brain disorders ( Attallah, 2020 ; Ceschin et al, 2018 ; Attallah, Sharkas & Gadelkarim, 2019 ; Attallah, Gadelkarim & Sharkas, 2018 ). Accordingly, CADx systems are characterized into two categories.…”

Section: Introductionmentioning

confidence: 99%

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Attallah

Anwar

Ghanem

et al. 2021

PeerJ Computer Science

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Breast cancer (BC) is one of the most common types of cancer that affects females worldwide. It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. Automatic diagnosis of BC could reduce death rates, by creating a computer aided diagnosis (CADx) system capable of accurately identifying BC at an early stage and decreasing the time consumed by pathologists during examinations. This paper proposes a novel CADx system named Histo-CADx for the automatic diagnosis of BC. Most related studies were based on individual deep learning methods. Also, studies did not examine the influence of fusing features from multiple CNNs and handcrafted features. In addition, related studies did not investigate the best combination of fused features that influence the performance of the CADx. Therefore, Histo-CADx is based on two stages of fusion. The first fusion stage involves the investigation of the impact of fusing several deep learning (DL) techniques with handcrafted feature extraction methods using the auto-encoder DL method. This stage also examines and searches for a suitable set of fused features that could improve the performance of Histo-CADx. The second fusion stage constructs a multiple classifier system (MCS) for fusing outputs from three classifiers, to further improve the accuracy of the proposed Histo-CADx. The performance of Histo-CADx is evaluated using two public datasets; specifically, the BreakHis and the ICIAR 2018 datasets. The results from the analysis of both datasets verified that the two fusion stages of Histo-CADx successfully improved the accuracy of the CADx compared to CADx constructed with individual features. Furthermore, using the auto-encoder for the fusion process has reduced the computation cost of the system. Moreover, the results after the two fusion stages confirmed that Histo-CADx is reliable and has the capacity of classifying BC more accurately compared to other latest studies. Consequently, it can be used by pathologists to help them in the accurate diagnosis of BC. In addition, it can decrease the time and effort needed by medical experts during the examination.

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Wearable Accelerometer and sEMG-Based Upper Limb BSN for Tele-Rehabilitation

Cited by 27 publications

References 30 publications

A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

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