ReviewSensors (Basel, Switzerland)2025
RGB-D Cameras and Brain-Computer Interfaces for Human Activity Recognition: An Overview.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Accelerator-Free Thermal Pose Estimation: Accuracy-Efficiency Trade-Offs on the Raspberry Pi 5.Sensors (Basel, Switzerland) · 2026Article
- DHA-eGCN: Differential Hyperedge Attention-Enhanced Graph Convolution Network for Skeleton-Based Human Action Recognition.Sensors (Basel, Switzerland) · 2026Article
- Efficient Thermal Pose Estimation: Balancing Accuracy and Edge Deployment for Smart Home Activity Recognition.Sensors (Basel, Switzerland) · 2026Article
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
Abstract
This paper provides a perspective on the use of RGB-D cameras and non-invasive brain-computer interfaces (BCIs) for human activity recognition (HAR). Then, it explores the potential of integrating both the technologies for active and assisted living. RGB-D cameras can offer monitoring of users in their living environments, preserving their privacy in human activity recognition through depth images and skeleton tracking. Concurrently, non-invasive BCIs can provide access to intent and control of users by decoding neural signals. The synergy between these technologies may allow holistic understanding of both physical context and cognitive state of users, to enhance personalized assistance inside smart homes. The successful deployment in integrating the two technologies needs addressing critical technical hurdles, including computational demands for real-time multi-modal data processing, and user acceptance challenges related to data privacy, security, and BCI illiteracy. Continued interdisciplinary research is essential to realize the full potential of RGB-D cameras and BCIs as AAL solutions, in order to improve the quality of life for independent or impaired people.
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.