ArticleSensors (Basel, Switzerland)2023
Multi-Sensor Data Fusion and CNN-LSTM Model for Human Activity Recognition System.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- A Permutation-Invariant, Missing-View-Aware Multi-Perspective Recurrent Neural Network Cell for Time-Series Fusion.Sensors (Basel, Switzerland) · 2026Article
- Multimodal Information Fusion for Control of Rehabilitation Robots in Motor Dysfunction: A Review.Bioengineering (Basel, Switzerland) · 2026Review
- Early fusion of laser and acoustic features for human orientation detection in non-line-of-sight environments.Scientific reports · 2026Article
- IoT framework for sports activity safety monitoring based on wearable sensors and CRNN spatiotemporal analysis.Scientific reports · 2026Article
- A Lightweight Radar-Camera Fusion Deep Learning Model for Human Activity Recognition.Sensors (Basel, Switzerland) · 2026Article
- Risk classification assessment and early warning of large deformation of soft rock in tunnels based on CNN-LSTM model.Scientific reports · 2024Article
- A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring.IEEE open journal of engineering in medicine and biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Human activity recognition (HAR) is becoming increasingly important, especially with the growing number of elderly people living at home. However, most sensors, such as cameras, do not perform well in low-light environments. To address this issue, we designed a HAR system that combines a camera and a millimeter wave radar, taking advantage of each sensor and a fusion algorithm to distinguish between confusing human activities and to improve accuracy in low-light settings. To extract the spatial and temporal features contained in the multisensor fusion data, we designed an improved CNN-LSTM model. In addition, three data fusion algorithms were studied and investigated. Compared to camera data in low-light environments, the fusion data significantly improved the HAR accuracy by at least 26.68%, 19.87%, and 21.92% under the data level fusion algorithm, feature level fusion algorithm, and decision level fusion algorithm, respectively. Moreover, the data level fusion algorithm also resulted in a reduction of the best misclassification rate to 2%~6%. These findings suggest that the proposed system has the potential to enhance the accuracy of HAR in low-light environments and to decrease human activity misclassification rates.
Indexed as
Identifiers
What OpenQuestion holds
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.