Open AccessProceedings Article
Online classifier construction algorithm for human activity detection using a tri-axial accelerometer
Yen-Ping Chen,Jhun-Ying Yang,Shun-Nan Liou,Gwo-Yun Lee,Jeen-Shing Wang +4 more
- Vol. 205, Iss: 2, pp 849-860
TLDR
The proposed dynamic linear discriminant analysis (LDA) which can dynamically update the scatter matrices for online constructing FBF classifiers without storing all the training samples in memory can reduce computational burden and achieve satisfactory recognition accuracy.Abstract:
This paper presents an online construction algorithm for constructing fuzzy basis function (FBF) classifiers that are capable of recognizing different types of human daily activities using a tri-axial accelerometer. The activity recognition is based on the acceleration data collected from a wireless tri-axial accelerometer module mounted on users' dominant wrists. Our objective is to enable users to: (1) online add new training samples to the existing classes for increasing the recognition accuracy, (2) online add additional classes to be recognized, and (3) online delete an existing class. For this objective we proposed a dynamic linear discriminant analysis (LDA) which can dynamically update the scatter matrices for online constructing FBF classifiers without storing all the training samples in memory. Our experimental results have successfully validated the integration of the FBF classifier with the proposed dynamic LDA can reduce computational burden and achieve satisfactory recognition accuracy.read more
Citations
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Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches
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Human activity recognition based on multienvironment sensor data
TL;DR: Wang et al. as discussed by the authors proposed a HAR algorithm based on wide time-domain convolutional neural network and multienvironment sensor data (HAR_WCNN), which can adaptively constrain the sensor noise during human activities in multitenant smart home scenarios.
Dissertation
Sensors fusion and movement analysis for sports performance optimization
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Recognition of Everyday Activities through Wearable Sensors and Machine Learning
TL;DR: A two-tier recognition system is presented that is designed to identify health activities in a naturalistic setting based on accelerometer data of common activities, to explore and develop accurate and quantifiable sensing and machine learning techniques for eventual real-time health monitoring by wearable device systems.
Dissertation
Génération d'histoires à partir de données de téléphone intelligentes : une approche de script
TL;DR: In this paper, the authors propose an approach for the generation of recits articules autour of scripts using an approach based on a technique of sur-echantillonnage.
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Book ChapterDOI
Activity recognition from user-annotated acceleration data
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TL;DR: This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves, and suggests that multiple accelerometers aid in recognition.
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TL;DR: Using the Stone-Weierstrass theorem, it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy.
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A Survey on Human Activity Recognition using Wearable Sensors
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TL;DR: The state of the art in HAR based on wearable sensors is surveyed and a two-level taxonomy in accordance to the learning approach and the response time is proposed.
Proceedings Article
Activity recognition from accelerometer data
TL;DR: This paper reports on the efforts to recognize user activity from accelerometer data and performance of base-level and meta-level classifiers, and Plurality Voting is found to perform consistently well across different settings.