Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors

Autor: Lei Wang, Mengqi Wu, Tao Liu, Qingquan Li, Wei Wang, Gaojing Wang
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Support Vector Machine
Boosting (machine learning)
Movement
Posture
Decision tree
window length
02 engineering and technology
lcsh:Chemical technology
01 natural sciences
Biochemistry
Article
Pattern Recognition
Automated
Analytical Chemistry
Machine Learning
Activity recognition
smartphone sensors
Sliding window protocol
human motion mode
0202 electrical engineering
electronic engineering
information engineering
Humans
lcsh:TP1-1185
Electrical and Electronic Engineering
Instrumentation
business.industry
010401 analytical chemistry
machine-learning method
Window (computing)
Bayes Theorem
Pattern recognition
Data segment
Atomic and Molecular Physics
and Optics
0104 chemical sciences
Support vector machine
Pattern recognition (psychology)
human pose pattern
020201 artificial intelligence & image processing
Smartphone
Artificial intelligence
business
Zdroj: Sensors
Volume 18
Issue 6
Sensors, Vol 18, Iss 6, p 1965 (2018)
Sensors (Basel, Switzerland)
ISSN: 1424-8220
DOI: 10.3390/s18061965
Popis: Human activity recognition (HAR) is essential for understanding people&rsquo
s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applied to HAR. Data segmentation using a sliding window is a basic step during the HAR procedure, wherein the window length directly affects recognition performance. However, the window length is generally randomly selected without systematic study. In this study, we examined the impact of window length on smartphone sensor-based human motion and pose pattern recognition. With data collected from smartphone sensors, we tested a range of window lengths on five popular machine-learning methods: decision tree, support vector machine, K-nearest neighbor, Gaussian naï
ve Bayesian, and adaptive boosting. From the results, we provide recommendations for choosing the appropriate window length. Results corroborate that the influence of window length on the recognition of motion modes is significant but largely limited to pose pattern recognition. For motion mode recognition, a window length between 2.5&ndash
3.5 s can provide an optimal tradeoff between recognition performance and speed. Adaptive boosting outperformed the other methods. For pose pattern recognition, 0.5 s was enough to obtain a satisfactory result. In addition, all of the tested methods performed well.
Databáze: OpenAIRE
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