ArticleScientific reports2025
Design of a new method for occupancy monitoring in smart home care with autonomous mobile robot within Internet of Things.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
The integration of autonomous mobile robots in Smart Home and their secure communication within Internet of Things with 5G networks represents a transformative shift towards more efficient, responsive, and adaptable healthcare and service delivery systems to support independent living for older people at home. This article presents a unique proposal for the possibility of implementing interoperability and secure data transmission within the communication between autonomous mobile robots and building automation technology in a Smart Home using 5G networks and also presents a novel design and application of a time-ahead [Formula: see text]concentration prediction method for sending presence and occupancy information in monitored Smart Home Care spaces without the use of cameras to an autonomous mobile robot for time-ahead detection of deviations from the daily routine. In this study, nonlinear input-output neural network models and nonlinear autoregressive neural network model with exogenous inputs neural network models with the following best results ([Formula: see text] and MAPE = 0.0565) were used. Levenberg-Marquardt algorithm, Bayes regularization algorithm and Scaled Conjugate Gradient algorithm have been used as learning algorithms. Measured waveforms of operational and technical variables for indoor environmental quality (temperature, relative humidity, light intensity and [Formula: see text]concentration) and binary information from magnetic contacts placed on windows and doors (opening/closing of windows and doors) were used to monitor the presence of occupants in the Smart Home Care with autonomous mobile robots without the use of cameras within IoT platform with 5G networks.
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.