Real-time human activity recognition from wireless sensors using evolving fuzzy systems

Andréu, Javier; Angelov, Plamen

doi:10.1109/fuzzy.2010.5584280

Cited by 20 publications

(9 citation statements)

References 16 publications

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“…When the number of observations grows sample-by-sample iterative parameter learning methods can be a solution [140]. Another interesting option for scalable learning is to incrementally generate the set of required parameters or update the model structure whilst new data is being added [141], [142]. Online methods of variable selection and regularization are recommended to deactivate spurious variables in order to ease this scalability to large dimensions during learning [143], [144].…”

Section: A Processing Big Datamentioning

confidence: 99%

Big Data for Health

Andreu-Pérez

Poon

Merrifield

et al. 2015

IEEE J. Biomed. Health Inform.

623

349

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This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

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Section: A Processing Big Datamentioning

confidence: 99%

Big Data for Health

Andreu-Pérez

Poon

Merrifield

et al. 2015

IEEE J. Biomed. Health Inform.

623

349

View full text Add to dashboard Cite

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“…Wei et al [7] proposed the two-layer Hidden Markov Model (HMM) for continues and long-term daily activity monitoring which uses wearable body sensors. Andreu and Angelov [8] suggested the fuzzy based algorithm for real-time human activity recognition. In this work, rule-based and self-learning fuzzy classifier extracts class information from wearable wireless accelerometers.…”

Section: Related Workmentioning

confidence: 99%

Efficient FPGA Implementation of Multilayer Perceptron for Real-Time Human Activity Classification

et al. 2019

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The smartphone-based human activity recognition (HAR) systems are not capable to deliver high-end performance for challenging applications. We propose a dedicated hardware-based HAR system for smart military wearables, which uses a multilayer perceptron (MLP) algorithm to perform activity classification. To achieve the flexible and efficient hardware design, the inherent MLP architecture with parallel computation is implemented on FPGA. The system performance has been evaluated using the UCI human activity dataset with 7767 feature samples of 20 subjects. The three combinations of a dataset are trained, validated, and tested on ten different MLP models with distinct topologies. The MLP design with the 7-6-5 topology is finalized from the classification accuracy and cross entropy performance. The five versions of the final MLP design (7-6-5) with different data precision are implemented on FPGA. The analysis shows that the MLP designed with 16-bit fixed-point data precision is the most efficient MLP implementation in the context of classification accuracy, resource utilization, and power consumption. The proposed MLP design requires only 270 ns for classification and consumes 120 mW of power. The recognition accuracy and hardware results performance achieved are better than many of the recently reported works. INDEX TERMS Human activity recognition, multilayer perceptron, smart military wearables, classifier hardware implementation, field programmable gate array.

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“…This leads to the construction of evolving models for pattern recognition. Evolving fuzzy systems has the ability for the progressive evolution, which is present in human nature [14].…”

Section: Systemsmentioning

confidence: 99%

Comparative analysis study of human activity recognition using various techniques

Hassan

Ahmad

Ali

2014

17th IEEE International Multi Topic Conference 2014

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A number of efforts have been applied on human activity recognition such as face recognition and motion analysis. The major problems in recognition are: 1. Scale. 2. Shift. 3. Projection. Human Activities are identified by the pose of the body such as standing, sitting etc. and some of the activities are like the emotions, facial expressions etc. Generally, human activity recognition is achieved by two groups of instruments so far; one on the basis of wearable sensors and the other one on the basis of environment sensing instruments. Wearable sensors are used like accelerometers, gyroscopes, and microphones and environment sensing instruments we use video recorders, shock sensors etc. Since the installation of the environment sensing instruments is intrusive and quite expensive so Wearable sensors are preferred. The Human Activity is recognized by combining information of human pose, human location and elapsed time. The main objective of this research is to recognize and summarize various activities of the human body by the help of the various techniques like Machine Learning, Sensor Technology etc. It will be helpful in commercial use like the restaurants and food places full of the automated robots and many more. It is also helpful in Medical field like full automated nurse robots available for the patient care etc. it is also helpful in security purposes and many more.I.

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Real-time human activity recognition from wireless sensors using evolving fuzzy systems

Cited by 20 publications

References 16 publications

Big Data for Health

Big Data for Health

Efficient FPGA Implementation of Multilayer Perceptron for Real-Time Human Activity Classification

Comparative analysis study of human activity recognition using various techniques

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