Development of a Self-Constructing Neuro-Fuzzy Inference System for Online Classification of Physical Movements

Jatoba, L.; Großmann, U.; Ottenbacher, J.; Stork, Wilhelm; Müller-Glaser, Klaus D.

doi:10.1109/health.2007.381666

Cited by 5 publications

(4 citation statements)

References 1 publication

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“…Neural networks are currently the most popular method used in the recognition of signal status [34][35][36], due to their excellent learning capability in the discrimination of nonlinearly separable classes. Watanabe [36] used a feedforward neural network with triaxial accelerometer to recognize the movements of hemiplegic patients.…”

Section: Classifier For Recognizing the Status Of Signalmentioning

confidence: 99%

“…Watanabe [36] used a feedforward neural network with triaxial accelerometer to recognize the movements of hemiplegic patients. Jatoba et al [34] utilized an adaptive neuro-fuzzy inference system based on signals collected from a triaxial acceleration sensor. Yang et al [25] recently merged a fuzzy system with a neural network to identify human activities based on signals collected using only one accelerometer.…”

Section: Classifier For Recognizing the Status Of Signalmentioning

confidence: 99%

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A Low-Cost Add-On Sensor and Algorithm to Help Small- and Medium-Sized Enterprises Monitor Machinery and Schedule Processes

et al. 2019

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Since the concept of Industry 4.0 emerged, an increasing number of major manufacturers have incorporated relevant technologies to monitor machinery and schedule processes so as to increase yield and optimize production. However, most machinery monitoring technologies are far too expensive for small- and medium-sized enterprises. Furthermore, the production processes at small- and medium-sized enterprises are simpler and can thus be optimized without excessively complex scheduling systems. This study therefore proposed the use of cheaper add-on sensors for monitoring machinery and integrated them with an algorithm that can more swiftly produce results that meet multiple objectives. The proposed algorithm is meant to extend the capabilities of small- and medium-sized enterprises in monitoring machinery and scheduling processes, thereby enabling them to contend on an equal footing with larger competitors. Finally, we performed an experiment at an actual spring enterprise to demonstrate the validity of the proposed algorithm.

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Section: Classifier For Recognizing the Status Of Signalmentioning

confidence: 99%

Section: Classifier For Recognizing the Status Of Signalmentioning

confidence: 99%

A Low-Cost Add-On Sensor and Algorithm to Help Small- and Medium-Sized Enterprises Monitor Machinery and Schedule Processes

et al. 2019

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“… Decision tree (DT) [22], [23], [36]  K-Nearest-Neighbor classifier (k-NN) [23], [29], [33]  Support Vector Machine (SVM) [23], [24], [25], [27], [34]  Neural network [26], [28], [30] Most classification methods require a training of the classifier. As shown in [23] the classifier results in a worse accuracy if it was trained with a data set that was not obtained from the user.…”

Section: A State Of the Artmentioning

confidence: 99%

“…walking along a corridor, upstairs or downstairs [36], [37]. All of the cited systems are based on the standard method for classification that can be described as follows:  Standard deviation [23], [24], [30]  Signal entropy [24], [30]  Signal energy [23], [24], [30]  Correlation between the axis of two or all accelerometers [23], [24]  Signal magnitude area (SMA) [22], [28]  Autoregressive coefficients [25], [28]  Frequency derived features [36] Most groups used the Weka toolkit or MatLab for their work. These platforms offer the most common classifiers that were employed as listed in the following:…”

Section: A State Of the Artmentioning

confidence: 99%

Accelerometer Based Real-Time Activity Analysis on a Microcontroller

Czabke¹,

Marsch²,

Lueth³

2011

Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare

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In this article we present a new algorithm implemented on a microcontroller for the classification of human physical activity based on a triaxial accelerometer. In terms of long term monitoring of activity patterns, it is important to keep the amount of data as small as possible and to use efficient data processing. Hence the aim of this work was to provide an algorithm that classifies the activities "resting", "walking", "running" and "unknown activity" in real-time. Using this approach memory intensive storing of raw data becomes unnecessary. Whenever the state of activity changes, a unix time stamp and the new state of activity, as well as the number of steps taken during the last activity period are stored to an external flash memory. Unlike most accelerometer based approaches this one does not depend on a certain positioning of the sensor and for the classification algorithm no set of training data is needed. The algorithm runs on the developed device Motionlogger which has the size of a key fob and can be worn unobtrusively in a pocket or handbag. The testing of the algorithm with 10 subjects wearing the Motionlogger in their pockets resulted in an average accuracy higher than 90%.

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Obtaining Energy Expenditure and Physical Activity from Acceleration Signals for Context-aware Evaluation of Cardiovascular Parameters

Jatoba

Großmann

Ottenbacher

et al. 2007

IV Latin American Congress on Biomedical Engineering 2007, Bioengineering Solutions for Latin America Health

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Development of a Self-Constructing Neuro-Fuzzy Inference System for Online Classification of Physical Movements

Cited by 5 publications

References 1 publication

A Low-Cost Add-On Sensor and Algorithm to Help Small- and Medium-Sized Enterprises Monitor Machinery and Schedule Processes

A Low-Cost Add-On Sensor and Algorithm to Help Small- and Medium-Sized Enterprises Monitor Machinery and Schedule Processes

Accelerometer Based Real-Time Activity Analysis on a Microcontroller

Obtaining Energy Expenditure and Physical Activity from Acceleration Signals for Context-aware Evaluation of Cardiovascular Parameters

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