Eklem tabanlı etkili düşme tespiti

Aslan, Muzaffer; Akbulut, Yaman; Şengür, Abdulkadir; İnce, Melih Cevdet

doi:10.17341/gazimmfd.369347

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…However, later it started to be used in both classification and nonlinear data classifications. Its basic principle is based on finding the best hyperplane to separate into two classes of data (Aslan et al, 2017;Demir et al, 2020). However, whilst there are many hyperplanes that can separate two-class data, SVM is able to find the hyperplane that will maximize the distance between the points closest to it.…”

Section: Support Vector Machinementioning

confidence: 99%

A multi-division convolutional neural network-based plant identification system

Türkoğlu

Aslan

Arı

et al. 2021

PeerJ Computer Science

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Background Plants have an important place in the life of all living things. Today, there is a risk of extinction for many plant species due to climate change and its environmental impact. Therefore, researchers have conducted various studies with the aim of protecting the diversity of the planet’s plant life. Generally, research in this area is aimed at determining plant species and diseases, with works predominantly based on plant images. Advances in deep learning techniques have provided very successful results in this field, and have become widely used in research studies to identify plant species. Methods In this paper, a Multi-Division Convolutional Neural Network (MD-CNN)-based plant recognition system was developed in order to address an agricultural problem related to the classification of plant species. In the proposed system, we divide plant images into equal nxn-sized pieces, and then deep features are extracted for each piece using a Convolutional Neural Network (CNN). For each part of the obtained deep features, effective features are selected using the Principal Component Analysis (PCA) algorithm. Finally, the obtained effective features are combined and classification conducted using the Support Vector Machine (SVM) method. Results In order to test the performance of the proposed deep-based system, eight different plant datasets were used: Flavia, Swedish, ICL, Foliage, Folio, Flower17, Flower102, and LeafSnap. According to the results of these experimental studies, 100% accuracy scores were achieved for the Flavia, Swedish, and Folio datasets, whilst the ICL, Foliage, Flower17, Flower102, and LeafSnap datasets achieved results of 99.77%, 99.93%, 97.87%, 98.03%, and 94.38%, respectively.

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Section: Support Vector Machinementioning

confidence: 99%

A multi-division convolutional neural network-based plant identification system

Türkoğlu

Aslan

Arı

et al. 2021

PeerJ Computer Science

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“…In the case of Context-Aware Systems (CAS), different research groups have also published datasets containing the measurements captured by fixed video camera, motion and depth sensors (such as Kinect), and/or other ambient sensors (vibration detectors, pressure, infrared, and Doppler sensors, and near-field imaging systems), while a set of volunteers emulate falls and ADLs in a predefined testbed. Among these databases, we can mention the following: CIRL Fall Recognition [ 12 ], Le2i FDD [ 13 ], SDUFall [ 14 ], EDF&OCCU [ 15 ], eHomeSeniors [ 16 ], Multiple Camera Fall [ 17 ] KUL High-Quality Fall Simulation [ 18 ], UTA [ 19 ], FUKinect-Fall [ 20 ], or MEBIOMEC [ 21 ] datasets, as well as the infrared video clips described by Mastoraky and Makris in [ 22 ] or those sequences provided by Adhikari et al in [ 23 ]. These datasets are out of the scope of this paper although we do consider those databases, such as UR Fall or UP Fall, which were conceived to test hybrid CAS-type and wearable FDSs, i.e., systems that make their detection decision from the joint analysis of video images (and/or magnitudes collected by environmental sensors) and measurements from inertial sensors transported by the users.…”

Section: Revision and Selection Of Public Datasetsmentioning

confidence: 99%

On the Heterogeneity of Existing Repositories of Movements Intended for the Evaluation of Fall Detection Systems

Casilari

Santoyo-Ramón

Cano-García

2020

Journal of Healthcare Engineering

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Due to the serious impact of falls on the autonomy and health of older people, the investigation of wearable alerting systems for the automatic detection of falls has gained considerable scientific interest in the field of body telemonitoring with wireless sensors. Because of the difficulties of systematically validating these systems in a real application scenario, Fall Detection Systems (FDSs) are typically evaluated by studying their response to datasets containing inertial sensor measurements captured during the execution of labelled nonfall and fall movements. In this context, during the last decade, numerous publicly accessible databases have been released aiming at offering a common benchmarking tool for the validation of the new proposals on FDSs. This work offers a comparative and updated analysis of these existing repositories. For this purpose, the samples contained in the datasets are characterized by different statistics that model diverse aspects of the mobility of the human body in the time interval where the greatest change in the acceleration module is identified. By using one-way analysis of variance (ANOVA) on the series of these features, the comparison shows the significant differences detected between the datasets, even when comparing activities that require a similar degree of physical effort. This heterogeneity, which may result from the great variability of the sensors, experimental users, and testbeds employed to generate the datasets, is relevant because it casts doubt on the validity of the conclusions of many studies on FDSs, since most of the proposals in the literature are only evaluated using a single database.

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“…SVM is a supervised learning algorithm frequently preferred for linear and nonlinear data classification [17]. Its basic structure is to find the best hyperplane that will provide the maximum margin among the output classes.…”

Section: Support Vector Machinementioning

confidence: 99%

Normal Cumulative Distribution Function and Dispersion Entropy Based EMG Classification

Aslan

2020

Sakarya University Journal of Computer and Information Sciences

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Electromyography (EMG) is used to measure muscle activity. EMG signals are widely used in many biomedical practices such as motion recognition, prosthetic control, physical rehabilitation, and human-computer interfaces. The effective use of EMG in such practices depends on distinctive feature extraction. In this study, Dispersion Entropy (DisEn) and Normal Cumulative Distribution Function (NCDF) methods are used for feature extraction from EMG signals. The suggested method was tested with a data set containing immersion of six different objects. In the experimental studies, the proposed method distinguished the movements with an accuracy performance of 98%. When compared to other methods using the same data set, the suggested method has about 1.2% better performance.

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Eklem tabanlı etkili düşme tespiti

Cited by 6 publications

References 27 publications

A multi-division convolutional neural network-based plant identification system

A multi-division convolutional neural network-based plant identification system

On the Heterogeneity of Existing Repositories of Movements Intended for the Evaluation of Fall Detection Systems

Normal Cumulative Distribution Function and Dispersion Entropy Based EMG Classification

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