TASG: An Augmented Classification Method for Impersonal HAR

Zheng, Zengwei; Du, Junjie; Sun, Lin; Huo, Meimei; Chen, Yuanyi

doi:10.1155/2018/6751363

Cited by 10 publications

(3 citation statements)

References 25 publications

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“…In total the following methods were constructed for comparison purposes WSVM, AKNN-baseline and hybrid methods LDA/SVM-KNN, LDA/WSVM-KNN, KDA/SVM-KNN, KDA/WSVM-KNN, KDA/SVM-AKNN. In addition to the preceding, the results reported by some state-of-the-art methods that used the same data set (Abidine et al , 2019; Abidine and Fergani, 2020; Anguita et al , 2012; Bharathi and Bhuvana, 2020; Wan et al , 2020; Zainudin et al , 2017; Zheng et al , 2018; Li et al , 2018; Lv et al , 2020) are acknowledged and discussed as well. These findings are now summarized in Table 11.…”

Section: Resultsmentioning

confidence: 97%

Activity recognition on smartphones using an AKNN based support vectors

Abidine

Oussalah

Fergani

et al. 2022

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Purpose Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform. Design/methodology/approach The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set. Findings Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation. Practical implications This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring. Originality/value The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.

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Section: Resultsmentioning

confidence: 97%

Activity recognition on smartphones using an AKNN based support vectors

Abidine

Oussalah

Fergani

et al. 2022

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“…The details of the two datasets are presented in Table 3. Different from previous studies [7,22,45], we use only accelerometer data to train the activity detection model to recognize all sixteen actions of the MobiAct dataset. The sampling frequency for all activities is around 85 Hz.…”

Section: Experimental Datasetsmentioning

confidence: 99%

A Novel Feature Set Extraction Based on Accelerometer Sensor Data for Improving the Fall Detection System

Nguyen

2022

Electronics

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Because falls are the second leading cause of injury deaths, especially in the elderly according to WHO statistics, there have been a lot of studies on developing a fall detection and warning system. Many approaches based on wearable sensors, cameras, Infrared sensors, radar, etc., have been proposed to detect falls efficiently. However, it still faces many challenges due to noise and no clear definition of fall activities. This paper proposes a new way to extract 44 features based on the time domain, frequency domain, and Hjorth parameters to deal with this. The effect of the proposed feature set has been evaluated on several classification algorithms, such as SVM, k-NN, ANN, J48, and RF. Our method achieves a relative high performance (F1-Score metric) in detecting fall and non-fall activities, i.e., 95.23% (falls), 99.11% (non-falls), and 96.16% (falls), 99.90% (non-falls) for the MobileAct 2.0 and UP-Fall datasets, respectively.

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“…Con UCI HAPT, Taufeeq [61] obtiene excelentes resultados utilizando arboles de decisión. Zheng [70] utiliza máquinas de soporte vectorial combinado un modelo de agrupación dispersa de dos capas.…”

Section: Métodos Basados En MLunclassified

Evolución de parámetros de optimización de redes neuronales profundas para el reconocimiento y clasificación de imágenes

Morales

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The importance of the application of pattern recognition for image recognition becomes apparent when the number and complexity of images increases. Artificial neural networks have proven their effectiveness in tackling complex pattern problems in the last fifteen years or so and have evolved into deep, hybrid architectures. The construction of neural networks is divided into two stages; the training stage and the test stage, in these stages expert knowledge is needed for the selection and adjustment of the parameters of the neural networks.In this work we present the implementation of a system that optimizes the parameters of a deep neural network based on the state of the art by means of evolutionary computation and parallel computation. This deep Coarse-Fine neural network performs a parallel feeding of the patterns at different levels of granularity: fine, medium and coarse; seeking to form an analysis of the robust characteristic pattern applied to image recognition and classification. We have modified and oriented the deep network from which we started, from patterns of recognition of human activities (HAR), towards attacking the complexity of the images, with promising results of 99 % recognition in the tests carried out with the database MNIST.Two versions of the system were made; Sequential Evolutionary Algorithm and Parallel Evolutionary Algorithm, the latter based on the message passing communication model, using the MPI protocol. In addition, the use of graphic cards (GPU) and multiprocessor computer for the massive handling of operations. We end with a set of system tests with different mechanisms applied to the system, analyzing and comparing the results with the state of the art.

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TASG: An Augmented Classification Method for Impersonal HAR

Cited by 10 publications

References 25 publications

Activity recognition on smartphones using an AKNN based support vectors

Activity recognition on smartphones using an AKNN based support vectors

A Novel Feature Set Extraction Based on Accelerometer Sensor Data for Improving the Fall Detection System

Evolución de parámetros de optimización de redes neuronales profundas para el reconocimiento y clasificación de imágenes

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