ReviewSensors (Basel, Switzerland)2019
Multi-Sensor Fusion for Activity Recognition-A Survey.
Review in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
20 citing papers in PubMed.
- Enhancing Nutritional Ingestive Behavior Microstructure Detection: Video Annotation and Passive Sensing Approaches.Nutrients · 2026Article
- Multimodal Utility Data for Appliance Recognition: A Case Study with Rule-Based Algorithms.Sensors (Basel, Switzerland) · 2026Article
- A multimodal spatio-temporal graph neural network framework for fatigue detection in tennis serving.Frontiers in bioengineering and biotechnology · 2026Article
- Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing.Sensors (Basel, Switzerland) · 2025Article
- Deep neural networks for wearable sensor-based activity recognition in Parkinson's disease: investigating generalizability and model complexity.Biomedical engineering online · 2024Article
- Detecting Medication-Taking Gestures Using Machine Learning and Accelerometer Data Collected via Smartwatch Technology: Instrument Validation Study.JMIR human factors · 2023Article
- Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge.Sensors (Basel, Switzerland) · 2022Article
- Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- Facial Expression Recognition from Multi-Perspective Visual Inputs and Soft Voting.Sensors (Basel, Switzerland) · 2022Article
- Markerless 3D Skeleton Tracking Algorithm by Merging Multiple Inaccurate Skeleton Data from Multiple RGB-D Sensors.Sensors (Basel, Switzerland) · 2022Article
- Acoustic- and Radio-Frequency-Based Human Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies.Sensors (Basel, Switzerland) · 2022Article
- Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains.Sensors (Basel, Switzerland) · 2021Article
- Human Activity Recognition: A Comparative Study to Assess the Contribution Level of Accelerometer, ECG, and PPG Signals.Sensors (Basel, Switzerland) · 2021Article
- Optimizing Sensor Deployment for Multi-Sensor-Based HAR System with Improved Glowworm Swarm Optimization Algorithm.Sensors (Basel, Switzerland) · 2020Article
- Intelligent Tensioning Method for Prestressed Cables Based on Digital Twins and Artificial Intelligence.Sensors (Basel, Switzerland) · 2020Article
- Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?Sensors (Basel, Switzerland) · 2020Article
- Choosing the Best Sensor Fusion Method: A Machine-Learning Approach.Sensors (Basel, Switzerland) · 2020Article
- Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets.Sensors (Basel, Switzerland) · 2020Article
- Laser Ranging-Assisted Binocular Visual Sensor Tracking System.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.