Human Activity Recognition with User-Free Accelerometers in the Sensor Networks

Wang, Shuangquan; Yang, Jie; Chen, Ningjiang; Chen, Xin; Zhang, Qinfeng

doi:10.1109/icnnb.2005.1614831

Cited by 52 publications

(8 citation statements)

References 4 publications

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“…Finally, they used Lego NXT to create contact between a computer and the robot wirelessly through Bluetooth (Wijayasekara and Manic 2013). Shuangquan, Jie, Ningjiang, Xin, and Qinfeng (2005) recognized the human activities using tri-axial accelerometers, through matching the patterns of the objects movement's used for detecting and transforming the changes in capacitance into an output voltage of the analog system. Here, an onboard A/D converter was used to digitize this output voltage and contacted through a Serial Peripheral Interface (SPI).…”

Section: Research Backgroundmentioning

confidence: 99%

“…They also used Decision Tree (DT) C4.5, Support Vector Machine (SVM), and Multiple-Layer Perception (MLP) neural networks for performing the activities of recognition. Finally, they developed the data in a centralized way in the application of almost all recognition (Shuangquan, Jie, Ningjiang, Xin, and Qinfeng 2005). Suzuki, Suzuki, Baba, and Furuta (2005) focused on the characteristics of human in machine operation.…”

Section: Research Backgroundmentioning

confidence: 99%

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Efficient Method to Improve Human Brain Sensor Activities Using Proposed Neuroheadset Device Embedded With Sensors: A Comprehensive Study

Chowdhury

Saif

2019

IJSECS

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The main purpose of this research is to investigate the human brain sensor activities related prior researches towards the needs of an efficient method to improve the human brain sensor activities. Human brain activities mainly measured by brain signal acquired from the brain sensor electrodes positioned on several parts of the brain cortex. Although previous researches investigated human brain activities in various aspects, the improvement of the human brain sensor activities is still unsolved. In today's world, it is very crucial need for improving the sensor activities of the human brain using that human brain improved signal externally. This research demonstrated a comprehensive critical analysis of human brain activities related prior researches to claim for an efficient method integrated with proposed neuroheadset device. This research presented a comprehensive review in various aspects like previous methods, existing frameworks analysis and existing results analysis with the discussion to establish an efficient method for acquiring human brain signal, improving the acquired signal and developing the sensor activities of the human brain using that human brain improved signal. Demonstrated critical review has expected for constituting an efficient method to improve the performance of maneuverability, visualization, subliminal activities and so forth on human brain activities.

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Section: Research Backgroundmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

Efficient Method to Improve Human Brain Sensor Activities Using Proposed Neuroheadset Device Embedded With Sensors: A Comprehensive Study

Chowdhury

Saif

2019

IJSECS

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“…As features may not always contain discriminative information about the activities and many features could contain redundant or noisy information, selecting relevant features could greatly improve the accuracy and simplify the computation [78,83]. Feature selection techniques, such as filter based [83], marginal based [84], and Support Vector Machine (SVM) based [85] have been proposed to be used in BSN applications. Another approach to reduce the feature space for analysis is to use a dimensionality reduction approach, such as Principal Component Analysis (PCA) [86], which projects the features into new spaces with lower dimensionality to represent higher variance of the data.…”

Section: A Feature Extraction and Selectionmentioning

confidence: 99%

“…The power is recovered from the external ambient such as solar and RF or vibration and motion from the human body, and then regulated or stored to provide power supply for the wireless body sensor device. Decision Tables [89,92] C4.5 decision tree [80,82,85,89,[93][94][95][96][97][98][99] J48 decision tree [96,100,101] k-Nearest Neighbor (k-NN) [80,82,89,93,96,102,103] k-means clustering [94,104] Expectation Maximization (EM) [105,106] Naïve Bayes [80, 89, 92, 93, 95-97, 99, 107, 108] Bayesian Network [82,92,93,96,105,109] Hidden Markov Model (HMM) [82,104,107,110] Conditional Random Fields [111] Support Vector Machine (SVM) [79,85,86,89,[112][113][114] Multi-Layered Perceptron (MLP) …”

Section: Towards Self-powered Sensingmentioning

confidence: 99%

Body Sensor Networks: In the Era of Big Data and Beyond

Poon

Yuce

et al. 2015

IEEE Rev. Biomed. Eng.

114

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Body sensor networks (BSN) have emerged as an active field of research to connect and operate sensors within, on or at close proximity to the human body. BSN have unique roles in health applications, particularly to support real-time decision making and therapeutic treatments. Nevertheless, challenges remain in designing BSN nodes with antennas that operate efficiently around, ingested or implanted inside the human body, as well as new methods to process the heterogeneous and growing amount of data on-node and in a distributed system for optimized performance and power consumption. As the battery operating time and sensor size are two important factors in determining the usability of BSN nodes, ultralow power transceivers, energy-aware network protocol, data compression, on-node processing, and energy-harvesting techniques are highly demanded to ultimately achieve a self-powered BSN.

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“…The triaxial accelerometer is one of the most important sensors used in sensor-based HAR [13]. The relations between axes are called hidden relations in [14] and correlation in [15]- [17]. Hidden relations between axes can help the deep activity recognition model increase its accuracy [14], [16], especially for discriminating between activities that involve translation in just one dimension [18].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Axis-Wise Triaxial Acceleration Data Fusion in CNN-Based Human Activity Recognition

Han

Luo

et al. 2020

IEICE Trans. Inf. & Syst.

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The triaxial accelerometer is one of the most important sensors for human activity recognition (HAR). It has been observed that the relations between the axes of a triaxial accelerometer plays a significant role in improving the accuracy of activity recognition. However, the existing research rarely focuses on these relations, but rather on the fusion of multiple sensors. In this paper, we propose a data fusion-based convolutional neural network (CNN) approach to effectively use the relations between the axes. We design a single-channel data fusion method and multichannel data fusion method in consideration of the diversified formats of sensor data. After obtaining the fused data, a CNN is used to extract the features and perform classification. The experiments show that the proposed approach has an advantage over the CNN in accuracy. Moreover, the single-channel model achieves an accuracy of 98.83% with the WISDM dataset, which is higher than that of state-of-the-art methods.

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Human Activity Recognition with User-Free Accelerometers in the Sensor Networks

Cited by 52 publications

References 4 publications

Efficient Method to Improve Human Brain Sensor Activities Using Proposed Neuroheadset Device Embedded With Sensors: A Comprehensive Study

Efficient Method to Improve Human Brain Sensor Activities Using Proposed Neuroheadset Device Embedded With Sensors: A Comprehensive Study

Body Sensor Networks: In the Era of Big Data and Beyond

The Effect of Axis-Wise Triaxial Acceleration Data Fusion in CNN-Based Human Activity Recognition

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