Multimodal analysis of body sensor network data streams for real-time healthcare

2010 ◽  
Author(s):  
Manoj K. Garg ◽  
Duk-Jin Kim ◽  
Deepak S. Turaga ◽  
Balakrishnan Prabhakaran
Keyword(s):  
Real Time ◽  
Sensor Network ◽  
Data Streams ◽  
Network Data ◽  
Body Sensor Network ◽  
Multimodal Analysis

Sensor Field Resource Management for Sensor Network Data Mining

2010 ◽  
pp. 280-304
Author(s):  
David J. Yates ◽  
Jennifer Xu
Keyword(s):  
Data Mining ◽  
Response Time ◽  
Data Quality ◽  
Real Time ◽  
Sensor Network ◽  
Resource Consumption ◽  
Data Accuracy ◽  
Network Data ◽  
Sensor Field ◽  
User Queries

This research is motivated by data mining for wireless sensor network applications. The authors consider applications where data is acquired in real-time, and thus data mining is performed on live streams of data rather than on stored databases. One challenge in supporting such applications is that sensor node power is a precious resource that needs to be managed as such. To conserve energy in the sensor field, the authors propose and evaluate several approaches to acquiring, and then caching data in a sensor field data server. The authors show that for true real-time applications, for which response time dictates data quality, policies that emulate cache hits by computing and returning approximate values for sensor data yield a simultaneous quality improvement and cost saving. This “win-win” is because when data acquisition response time is sufficiently important, the decrease in resource consumption and increase in data quality achieved by using approximate values outweighs the negative impact on data accuracy due to the approximation. In contrast, when data accuracy drives quality, a linear trade-off between resource consumption and data accuracy emerges. The authors then identify caching and lookup policies for which the sensor field query rate is bounded when servicing an arbitrary workload of user queries. This upper bound is achieved by having multiple user queries share the cost of a sensor field query. Finally, the authors discuss the challenges facing sensor network data mining applications in terms of data collection, warehousing, and mining techniques.

scholarly journals An Internet of Things Wireless Sensor Network Data Exchange Model Based on Hierarchical Address Automatic Configuration and Header Compression Encoding Strategy

2017 ◽  
Vol 13 (07) ◽  
pp. 140 ◽  
Author(s):  
Yuankun Yang ◽  
Yongqing Ji
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Real Time ◽  
Sensor Network ◽  
Data Exchange ◽  
Wireless Sensor ◽  
The Internet ◽  
Network Data ◽  
Sink Node

<p><span style="font-size: medium;"><span style="font-family: 宋体;">To explore the wireless sensor network data exchange model, an addressing strategy is applied to the Internet of Things, and the real-time communication between the underlying wireless sensor network and the Internet based on the IEEE 802.15.4 protocol is realized. In addition, Hierarchical address auto configuration strategy is adopted. First of all, inside the bottom network, it allows nodes to use link local address derived by 16-bit short address for data packet transmission. Secondly, Sink node in each underlying network accesses to the global routing prefix through the upper IP router, and combined with interface identifier, it forms Sink node global address, and realizes wireless sensor network and Internet data exchange. The research results show that the strategy has certain superiority in network cost, throughput, energy consumption and other performances. In summary, the proposed addressing strategy has the characteristics of effectively integrating heterogeneous networks, reducing system energy consumption, increasing network throughput and ensuring real-time system performance for the future Internet of things.</span></span></p>

scholarly journals Real-time information processing of environmental sensor network data using bayesian gaussian processes

2012 ◽  
Vol 9 (1) ◽  
pp. 1-32 ◽  
Author(s):  
Michael A. Osborne ◽  
Stephen J. Roberts ◽  
Alex Rogers ◽  
Nicholas R. Jennings
Keyword(s):  
Information Processing ◽  
Real Time ◽  
Sensor Network ◽  
Gaussian Processes ◽  
Network Data ◽  
Environmental Sensor ◽  
Real Time Information ◽  
Time Information

scholarly journals Artificial Intelligence Based Body Sensor Network Framework—Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ashwin A. Phatak ◽  
Franz-Georg Wieland ◽  
Kartik Vempala ◽  
Frederik Volkmar ◽  
Daniel Memmert
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Collection ◽  
Real Time ◽  
Sensor Network ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Body Sensor Network

AbstractWith the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality ‘big data’ in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

Real-time Work Environment Optimization using Multimodal Media and Body Sensor Network

Smart Health ◽  
2020 ◽  
pp. 100164
Author(s):  
Nan Zhao ◽  
Susanne Seitinger ◽  
Robert Richer ◽  
Joseph A. Paradiso
Keyword(s):  
Work Environment ◽  
Real Time ◽  
Sensor Network ◽  
Body Sensor Network ◽  
Time Work
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