ArticleSensors (Basel, Switzerland)2022
Machine Learning-Based Classification of Human Behaviors and Falls in Restroom via Dual Doppler Radar Measurements.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 27 citations in OpenAlex.
- Radar-Based Activity Recognition in Strictly Privacy-Sensitive Settings Through Deep Feature Learning.Biomimetics (Basel, Switzerland) · 2025Article
- Radar-Based Fall Detection: A Survey.IEEE robotics & automation magazine · 2024Article
- Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review.Online journal of public health informatics · 2024Article
- Identifying sensors-based parameters associated with fall risk in community-dwelling older adults: an investigation and interpretation of discriminatory parameters.BMC geriatrics · 2024Article
- Patch-Transformer Network: A Wearable-Sensor-Based Fall Detection Method.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures.International journal of environmental research and public health · 2023Article
- Analysis of Signal Processing Methods to Reject the DC Offset Contribution of Static Reflectors in FMCW Radar-Based Vital Signs Monitoring.Sensors (Basel, Switzerland) · 2022Article
- Contactless Fall Detection by Means of Multiple Bioradars and Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- Enhanced mechanisms of pooling and channel attention for deep learning feature maps.PeerJ. Computer science · 2022Article
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 at 1 institution in 1 country.
Funding
Abstract
This study presents a radar-based remote measurement system for classification of human behaviors and falls in restrooms without privacy invasion. Our system uses a dual Doppler radar mounted onto a restroom ceiling and wall. Machine learning methods, including the convolutional neural network (CNN), long short-term memory, support vector machine, and random forest methods, are applied to the Doppler radar data to verify the model's efficiency and features. Experimental results from 21 participants demonstrated the accurate classification of eight realistic behaviors, including falling. Using the Doppler spectrograms (time-velocity distribution) as the inputs, CNN showed the best results with an overall classification accuracy of 95.6% and 100% fall classification accuracy. We confirmed that these accuracies were better than those achieved by conventional restroom monitoring techniques using thermal sensors and radars. Furthermore, the comparison results of various machine learning methods and cases using each radar's data show that the higher-order derivative parameters of acceleration and jerk, and the motion information in the horizontal direction are the efficient features for behavior classification in a restroom. These findings indicate that daily restroom monitoring using the proposed radar system accurately recognizes human behaviors and allows early detection of fall accidents.
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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.