ArticleSensors (Basel, Switzerland)2024
Intelligent Millimeter-Wave System for Human Activity Monitoring for Telemedicine.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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.
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Who cites it
9 citing papers in PubMed, 28 citations in OpenAlex.
- Real-Time Fall Detection in Indoor Environments Using FMCW Radar and Lightweight Ensemble CNN on Edge Devices.Sensors (Basel, Switzerland) · 2026Article
- Millimeter-wave technology for multi-person fall detection validated through wearable sensors and real-life scenarios.Scientific reports · 2026Article
- A comprehensive IMU dataset for evaluating sensor layouts in human activity and intensity recognition.Scientific data · 2026Article
- A Practical Case of Monitoring Older Adults Using mmWave Radar and UWB.Sensors (Basel, Switzerland) · 2026Article
- A vision transformer with recurrent neural network-based fall activity recognition system for disabled persons in smart IoT environments.Scientific reports · 2025Article
- Improving the Accuracy of mmWave Radar for Ethical Patient Monitoring in Mental Health Settings.Sensors (Basel, Switzerland) · 2024Article
- Human Multi-Activities Classification Using mmWave Radar: Feature Fusion in Time-Domain and PCANet.Sensors (Basel, Switzerland) · 2024Article
- Liability of Health Professionals Using Sensors, Telemedicine and Artificial Intelligence for Remote Healthcare.Sensors (Basel, Switzerland) · 2024Review
- Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.Journal of imaging · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can provide activity data reports, tracking maps, and fall alerts. Using radar helps to safeguard patients' privacy by abstaining from recording camera images. We evaluated our system for real-time operation and achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Our system would facilitate the ability to detect falls and monitor physical activity in home and institutional settings to improve telemedicine by providing objective data for more timely and targeted interventions. This work demonstrates the potential of artificial intelligence algorithms and mmwave sensors for HAR.
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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.