ArticleSensors (Basel, Switzerland)2019
A Novel Human Activity Recognition and Prediction in Smart Home Based on Interaction.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- On the Necessity of Multidisciplinarity in the Development of at-Home Health Monitoring Platforms for Older Adults: Systematic Review.JMIR human factors · 2025Pooled it
- STAR: A Privacy-Preserving, Energy-Efficient Edge AI Framework for Human Activity Recognition via Wi-Fi CSI in Mobile and Pervasive Computing Environments.Sensors (Basel, Switzerland) · 2026Article
- Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and edge cloud continuum.Scientific reports · 2025Article
- Human Activity Recognition Through Augmented WiFi CSI Signals by Lightweight Attention-GRU.Sensors (Basel, Switzerland) · 2025Article
- Wearable sensors-based assistive technologies for patient health monitoring.Frontiers in bioengineering and biotechnology · 2025Article
- X-CHAR: A Concept-based Explainable Complex Human Activity Recognition Model.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- Incremental Learning of Human Activities in Smart Homes.Sensors (Basel, Switzerland) · 2022Article
- Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data.Methods of information in medicine · 2022Article
- Human Activity Recognition: Review, Taxonomy and Open Challenges.Sensors (Basel, Switzerland) · 2022Review
- Human activity recognition in artificial intelligence framework: a narrative review.Artificial intelligence review · 2022Article
- A CSI-Based Human Activity Recognition Using Deep Learning.Sensors (Basel, Switzerland) · 2021Article
- From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning.Sensors (Basel, Switzerland) · 2021Article
- Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors.Sensors (Basel, Switzerland) · 2021Article
- Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores.IEEE access : practical innovations, open solutions · 2021Article
- Optimizing Sensor Deployment for Multi-Sensor-Based HAR System with Improved Glowworm Swarm Optimization Algorithm.Sensors (Basel, Switzerland) · 2020Article
- Using UHF RFID Properties to Develop and Optimize an Upper-Limb Rehabilitation System.Sensors (Basel, Switzerland) · 2020Article
- Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review.Sensors (Basel, Switzerland) · 2020Review
- A Multi-task Learning Model for Daily Activity Forecast in Smart Home.Sensors (Basel, Switzerland) · 2020Article
- Using Rough Sets to Improve Activity Recognition Based on Sensor Data.Sensors (Basel, Switzerland) · 2020Article
- Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Smart Homes are generally considered the final solution for living problem, especially for the health care of the elderly and disabled, power saving, etc. Human activity recognition in smart homes is the key to achieving home automation, which enables the smart services to automatically run according to the human mind. Recent research has made a lot of progress in this field; however, most of them can only recognize default activities, which is probably not needed by smart homes services. In addition, low scalability makes such research infeasible to be used outside the laboratory. In this study, we unwrap this issue and propose a novel framework to not only recognize human activity but also predict it. The framework contains three stages: recognition after the activity, recognition in progress, and activity prediction in advance. Furthermore, using passive RFID tags, the hardware cost of our framework is sufficiently low to popularize the framework. In addition, the experimental result demonstrates that our framework can realize good performance in both activity recognition and prediction with high scalability.
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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.