scholarly journals Feature Extraction from Sensor Data Streams for Real-Time Human Behaviour Recognition

2007 ◽  
pp. 115-126 ◽  
Author(s):  
Julia Hunter ◽  
Martin Colley
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Data Streams ◽  
Human Behaviour ◽  
Sensor Data ◽  
Behaviour Recognition

scholarly journals REAL-TIME EARTHQUAKE MONITORING WITH SPATIO-TEMPORAL FIELDS

2017 ◽  
Vol IV-4/W2 ◽  
pp. 215-222 ◽  
Author(s):  
J. C. Whittier ◽  
S. Nittel ◽  
I. Subasinghe
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Apache Spark ◽  
Sensor Data ◽  
Live Streaming ◽  
Earthquake Activity ◽  
Maximum Displacement ◽  
Earthquake Event ◽  
Spatio Temporal

With live streaming sensors and sensor networks, increasingly large numbers of individual sensors are deployed in physical space. Sensor data streams are a fundamentally novel mechanism to deliver observations to information systems. They enable us to represent spatio-temporal continuous phenomena such as radiation accidents, toxic plumes, or earthquakes almost as instantaneously as they happen in the real world. Sensor data streams discretely sample an earthquake, while the earthquake is continuous over space and time. Programmers attempting to integrate many streams to analyze earthquake activity and scope need to write code to integrate potentially very large sets of asynchronously sampled, concurrent streams in tedious application code. In previous work, we proposed the field stream data model (Liang et al., 2016) for data stream engines. Abstracting the stream of an individual sensor as a temporal field, the field represents the Earth’s movement at the sensor position as continuous. This simplifies analysis across many sensors significantly. In this paper, we undertake a feasibility study of using the field stream model and the open source Data Stream Engine (DSE) Apache Spark(Apache Spark, 2017) to implement a real-time earthquake event detection with a subset of the 250 GPS sensor data streams of the Southern California Integrated GPS Network (SCIGN). The field-based real-time stream queries compute maximum displacement values over the latest query window of each stream, and related spatially neighboring streams to identify earthquake events and their extent. Further, we correlated the detected events with an USGS earthquake event feed. The query results are visualized in real-time.

Real-Time Monitoring of Mobile Biological Sensor Data-Streams: Architecture and Cost-Model

2008 ◽  
Author(s):  
Alfredo Goni ◽  
Jimena Rodriguez ◽  
Alfredo Burgos ◽  
Arantza Illarramendi ◽  
Lacramioara Dranca
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Cost Model ◽  
Sensor Data ◽  
Real Time Monitoring ◽  
Biological Sensor

scholarly journals Feature Extraction from Sensor Data Streams for Optimizing Grinding Condition

2017 ◽  
Vol 229 ◽  
pp. 012032
Author(s):  
Y Kondo ◽  
Y Higashimoto ◽  
S Sakamoto ◽  
T Fujita ◽  
K Yamaguchi
Keyword(s):  
Feature Extraction ◽  
Data Streams ◽  
Sensor Data

scholarly journals New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2814
Author(s):  
Tsige Tadesse Alemayoh ◽  
Jae Hoon Lee ◽  
Shingo Okamoto
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Real Time ◽  
Activity Recognition ◽  
Time Domain ◽  
Sensor Data ◽  
Series Data ◽  
Smart Devices ◽  
Time Activity ◽  
Data Structuring

For the effective application of thriving human-assistive technologies in healthcare services and human–robot collaborative tasks, computing devices must be aware of human movements. Developing a reliable real-time activity recognition method for the continuous and smooth operation of such smart devices is imperative. To achieve this, light and intelligent methods that use ubiquitous sensors are pivotal. In this study, with the correlation of time series data in mind, a new method of data structuring for deeper feature extraction is introduced herein. The activity data were collected using a smartphone with the help of an exclusively developed iOS application. Data from eight activities were shaped into single and double-channels to extract deep temporal and spatial features of the signals. In addition to the time domain, raw data were represented via the Fourier and wavelet domains. Among the several neural network models used to fit the deep-learning classification of the activities, a convolutional neural network with a double-channeled time-domain input performed well. This method was further evaluated using other public datasets, and better performance was obtained. The practicability of the trained model was finally tested on a computer and a smartphone in real-time, where it demonstrated promising results.

scholarly journals INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS

2015 ◽  
Vol II-4/W2 ◽  
pp. 67-71 ◽  
Author(s):  
A. Bhushan ◽  
M. H. Sharker ◽  
H. A. Karimi
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Outlier Detection ◽  
Data Streams ◽  
Credit Card ◽  
Principal Component ◽  
Component Analysis ◽  
Spatiotemporal Data ◽  
Sensor Data ◽  
Detection Methods

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

Generating Linked Data in Real-time from Sensor Data Streams

2015 ◽  
pp. 103-126
Author(s):  
Nikolaos Konstantinou ◽  
Dimitrios-Emmanuel Spanos
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Linked Data ◽  
Sensor Data

Real-time sensor data streams

2015 ◽  
Vol 7 (2) ◽  
pp. 22-28 ◽  
Author(s):  
Silvia Nittel
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Sensor Data

scholarly journals Adaptive Segmentation of Streaming Sensor Data on Edge Devices

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6884
Author(s):  
Roman Dębski ◽  
Rafał Dreżewski
Keyword(s):  
Real Time ◽  
Data Streams ◽  
State Of The Art ◽  
Sensor Data ◽  
Approximation Accuracy ◽  
Look Ahead ◽  
Approximation Errors ◽  
Segmentation Quality ◽  
Streaming Algorithm ◽  
Signal Approximation

Sensor data streams often represent signals/trajectories which are twice differentiable (e.g., to give a continuous velocity and acceleration), and this property must be reflected in their segmentation. An adaptive streaming algorithm for this problem is presented. It is based on the greedy look-ahead strategy and is built on the concept of a cubic splinelet. A characteristic feature of the proposed algorithm is the real-time simultaneous segmentation, smoothing, and compression of data streams. The segmentation quality is measured in terms of the signal approximation accuracy and the corresponding compression ratio. The numerical results show the relatively high compression ratios (from 135 to 208, i.e., compressed stream sizes up to 208 times smaller) combined with the approximation errors comparable to those obtained from the state-of-the-art global reference algorithm. The proposed algorithm can be applied to various domains, including online compression and/or smoothing of data streams coming from sensors, real-time IoT analytics, and embedded time-series databases.

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