ArticleSensors (Basel, Switzerland)2021
Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
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Who cites it
4 citing papers in PubMed, 25 citations in OpenAlex.
- SVM action recognition model based on skeletal key point analysis with posture sensors to help sports training.BMC sports science, medicine & rehabilitation · 2025Article
- Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches.Sensors (Basel, Switzerland) · 2024Article
- Enhancing Human Activity Recognition in Smart Homes with Self-Supervised Learning and Self-Attention.Sensors (Basel, Switzerland) · 2024Article
- Online Activity Recognition Combining Dynamic Segmentation and Emergent Modeling.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
Abstract
The recent growth of the elderly population has led to the requirement for constant home monitoring as solitary living becomes popular. This protects older people who live alone from unwanted instances such as falling or deterioration caused by some diseases. However, although wearable devices and camera-based systems can provide relatively precise information about human motion, they invade the privacy of the elderly. One way to detect the abnormal behavior of elderly residents under the condition of maintaining privacy is to equip the resident's house with an Internet of Things system based on a non-invasive binary motion sensor array. We propose to concatenate external features (
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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.