scholarly journals Deep Unsupervised Methods towards Behavior Analysis in Ubiquitous Sensor Data

2021 ◽  
pp. 100486
Author(s):  
Manan Sharma ◽  
Shivam Tiwari ◽  
Gaurav Ruhela ◽  
Suchetana Chakraborty ◽  
Dip Sankar Banerjee
Keyword(s):  
Behavior Analysis ◽  
Sensor Data ◽  
Unsupervised Methods

scholarly journals Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5197
Author(s):  
Beomjun Kim ◽  
Yunju Baek
Keyword(s):  
Behavior Analysis ◽  
Electronic Control Unit ◽  
Driver Behavior ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Steering Wheel ◽  
Automatic Extraction ◽  
Area Network ◽  
Vehicle Information

Advances in vehicle technology have resulted in the development of vehicles equipped with sensors to acquire standardized information such as engine speed and vehicle speed from the in-vehicle controller area network (CAN) system. However, there are challenges in acquiring proprietary information from CAN frames, such as the brake pedal and steering wheel operation, which are essential for driver behavior analysis. Such information extraction requires electronic control unit identifier analysis and accompanying data interpretation. In this paper, we present a system for the automatic extraction of proprietary in-vehicle information using sensor data correlated with the desired information. First, the proposed system estimates the vehicle’s driving status through threshold-, random forest-, and long short-term memory-based techniques using inertial measurement unit and global positioning system values. Then, the system segments in-vehicle CAN frames using the estimation and evaluates each segment with our scoring method to select suitable candidates by examining the similarity between each candidate and its estimation through the suggested distance matching technique. We conduct comprehensive experiments of the proposed system using real vehicles in an urban environment. Performance evaluation shows that the estimation accuracy of the driving condition is 84.20%, and the extraction accuracy of the in-vehicle information is 82.31%, which implies that the presented approaches are quite feasible for automatic extraction of proprietary in-vehicle information.

Review of Applied Behavior Analysis.

1988 ◽  
Vol 33 (3) ◽  
pp. 271-271
Author(s):  
Cyril M. Franks
Keyword(s):  
Behavior Analysis ◽  
Applied Behavior Analysis

So, Behaviorists Actually Think: A Review of an Upbeat Guide to Applications of and Careers in Behavior Analysis

PsycCRITIQUES ◽  
2007 ◽  
Vol 52 (4) ◽  
Author(s):  
John R. Lutzker ◽  
Katherine V. Powers
Keyword(s):  
Behavior Analysis

Basic research in behavior analysis.

2018 ◽  
Vol 18 (2) ◽  
pp. 117-118 ◽  
Author(s):  
Anita Li ◽  
Amanda Mahoney ◽  
Alan Poling
Keyword(s):  
Behavior Analysis ◽  
Basic Research
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