AI Article Synopsis
- Activity recognition is essential in sensor-based applications like smart homes, where the goal is to identify activities based on sensor data.
- The article highlights the use of spatial information to improve the recognition process, noting that fixed sensor locations provide clues about activity locations but that different activities may involve different sensors.
- By utilizing rough sets instead of standard sets, the article addresses the imprecision in sensor activity associations and demonstrates through data sets that rough sets can enhance activity recognition and support Explainable Artificial Intelligence (XAI).
Article Abstract
Activity recognition plays a central role in many sensor-based applications, such as smart homes for instance. Given a stream of sensor data, the goal is to determine the activities that triggered the sensor data. This article shows how spatial information can be used to improve the process of recognizing activities in smart homes. The sensors that are used in smart homes are in most cases installed in fixed locations, which means that when a particular sensor is triggered, we know approximately where the activity takes place. However, since different sensors may be involved in different occurrences of the same type of activity, the set of sensors associated with a particular activity is not precisely defined. In this article, we use rough sets rather than standard sets to denote the sensors involved in an activity to model, which enables us to deal with this imprecision. Using publicly available data sets, we will demonstrate that rough sets can adequately capture useful information to assist with the activity recognition process. We will also show that rough sets lend themselves to creating Explainable Artificial Intelligence (XAI). activity recognition; context awareness; spatial reasoning; rough sets.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146264 | PMC |
| http://dx.doi.org/10.3390/s20061779 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Hierarchical Porous Microspheres-Assisted Serum Metabolic Profile for the Early Diagnosis and Surveillance of Postmenopausal Osteoporosis.
Anal Chem
December 2024
Department of Chemistry, Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, China.
With the aging global population, the incidence of osteoporosis (OP) is increasing, putting more individuals at risk. Since postmenopausal osteoporosis (PMOP) often remains asymptomatic until a fracture occurs, making the early clinical diagnosis of PMOP particularly challenging. In this work, the AuNPs-anchored hierarchical porous ZrO microspheres (Au/HPZOMs) is designed to assist laser desorption/ionization mass spectrometry (LDI-MS) for the requirement of serum metabolic fingerprints of PMOP, postmenopausal osteopenia (PMON), and healthy controls (HC) and realize the early diagnosis and surveillance of PMOP.
View Article and Find Full Text PDFCaterpillar-Inspired Multi-Gait Generation Method for Series-Parallel Hybrid Segmented Robot.
Biomimetics (Basel)
December 2024
College of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.
The body structures and motion stability of worm-like and snake-like robots have garnered significant research interest. Recently, innovative serial-parallel hybrid segmented robots have emerged as a fundamental platform for a wide range of motion modes. To address the hyper-redundancy characteristics of these hybrid structures, we propose a novel caterpillar-inspired Stable Segment Update (SSU) gait generation approach, establishing a unified framework for multi-segment robot gait generation.
View Article and Find Full Text PDFKey concepts and a world-wide look at plant recruitment networks.
Biol Rev Camb Philos Soc
December 2024
Andalusian Interuniversity Institute for Earth System Research (IISTA), Avenida del Mediterráneo, Granada, 18071, Spain.
Plant-plant interactions are major determinants of the dynamics of terrestrial ecosystems. There is a long tradition in the study of these interactions, their mechanisms and their consequences using experimental, observational and theoretical approaches. Empirical studies overwhelmingly focus at the level of species pairs or small sets of species.
View Article and Find Full Text PDFMask-guided network for finger vein feature extraction and biometric identification.
Biomed Opt Express
December 2024
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China (UESTC), Huzhou 313001, China.
The problems of complex background, low quality of finger vein images, and poor discriminative features have been the bottleneck of feature extraction and finger vein recognition. To this end, we propose a feature extraction algorithm based on the open-set testing protocol. In order to eliminate the interference of irrelevant areas, this paper proposes the idea of segmentation-assisted classification, that is, using the rough mask of the finger vein to constrain the feature learning process so that the network can focus on the vein area and learn greater weight for the vein.
View Article and Find Full Text PDFMulti-criteria decision support models under fuzzy credibility rough numbers and their application in green supply selection.
Heliyon
February 2024
Department of Mathematics, College of Science and Arts, Mohayil, King of Khalid University, 61413 Abha, Saudi Arabia.
As the increasing environmental issues, various companies have take initiatives to produce green products or to select green suppliers which maximize the business performance and minimize the environmental pollution. The real numbers data have imbiguity and uncertainty due to described by classical tools. Therefore, we consider a new type of fuzzy set, fuzzy credibility rough sets.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!