SynthesisSensors (Basel, Switzerland)2020
Unsupervised Human Activity Recognition Using the Clustering Approach: A Review.
Synthesis in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Review
- FAItH: Federated Analytics and Integrated Differential Privacy with Clustering for Healthcare Monitoring.Scientific reports · 2025Article
- Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends.Journal of imaging · 2025Review
- Integrating Wearable Sensor Signal Processing with Unsupervised Learning Methods for Tremor Classification in Parkinson's Disease.Bioengineering (Basel, Switzerland) · 2025Article
- Unsupervised Cluster Analysis of Walking Activity Data for Healthy Individuals and Individuals with Lower Limb Amputation.Sensors (Basel, Switzerland) · 2023Article
- Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification.Sensors (Basel, Switzerland) · 2023Article
- Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets.Sensors (Basel, Switzerland) · 2023Article
- Human Activity Recognition with an HMM-Based Generative Model.Sensors (Basel, Switzerland) · 2023Article
- Discovering Daily Activity Patterns from Sensor Data Sequences and Activity Sequences.Sensors (Basel, Switzerland) · 2021Article
- Review of Wearable Devices and Data Collection Considerations for Connected Health.Sensors (Basel, Switzerland) · 2021Review
- SISME, Estuarine Monitoring System Based on IOT and Machine Learning for the Detection of Salt Wedge in Aquifers: Case Study of the Magdalena River Estuary.Sensors (Basel, Switzerland) · 2021Article
- Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification.Sensors (Basel, Switzerland) · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.
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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.