scholarly journals A semantics-based approach to sensor data segmentation in real-time Activity Recognition

2019 ◽  
Vol 93 ◽  
pp. 224-236 ◽  
Author(s):  
Darpan Triboan ◽  
Liming Chen ◽  
Feng Chen ◽  
Zumin Wang
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Sensor Data ◽  
Time Activity ◽  
Data Segmentation

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 Dynamic sensor data segmentation for real-time knowledge-driven activity recognition

2014 ◽  
Vol 10 ◽  
pp. 155-172 ◽  
Author(s):  
George Okeyo ◽  
Liming Chen ◽  
Hui Wang ◽  
Roy Sterritt
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Sensor Data ◽  
Data Segmentation

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home

2020 ◽  
Vol 16 (11) ◽  
pp. 155014772097151
Author(s):  
Yan Hu ◽  
Bingce Wang ◽  
Yuyan Sun ◽  
Jing An ◽  
Zhiliang Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Real Time ◽  
Activity Recognition ◽  
Smart Home ◽  
Feature Representation ◽  
Support Vector ◽  
Typical Application ◽  
Time Activity ◽  
Health Smart Home

Health smart home, as a typical application of Internet of things, provides a new solution for remote medical treatment. It can effectively relieve pressure from shortage of medical resources caused by aging population and help elderly people live at home more independently and safely. Activity recognition is the core of health smart home. This technology aims to recognize the activity patterns of users from a series of observations on the user’ actions and the environmental conditions, so as to avoid distress situations as much as possible. However, most of the existing researches focus on offline activity recognition, but not good at online real-time activity recognition. Besides, the feature representation techniques used for offline activity recognition are generally not suitable for online scenarios. In this article, the authors propose a real-time online activity recognition approach based on the genetic algorithm–optimized support vector machine classifier. In order to support online real-time activity recognition, a new sliding window-based feature representation technique enhanced by mutual information between sensors is devised. In addition, the genetic algorithm is used to automatically select optimal hyperparameters for the support vector machine model, thereby reducing the recognition inaccuracy caused by manual tuning of hyperparameters. Finally, a series of comprehensive experiments are conducted on freely available data sets to validate the effectiveness of the proposed approach.

scholarly journals Toward a Wearable RFID System for Real-Time Activity Recognition Using Radio Patterns

2017 ◽  
Vol 16 (1) ◽  
pp. 228-242 ◽  
Author(s):  
Liang Wang ◽  
Tao Gu ◽  
Xianping Tao ◽  
Jian Lu
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Time Activity ◽  
Rfid System

scholarly journals Salient pairwise spatio-temporal interest points for real-time activity recognition

2016 ◽  
Vol 1 (1) ◽  
pp. 14-29 ◽  
Author(s):  
Mengyuan Liu ◽  
Hong Liu ◽  
Qianru Sun ◽  
Tianwei Zhang ◽  
Runwei Ding
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Interest Points ◽  
Time Activity ◽  
Spatio Temporal
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