ArticleSensors (Basel, Switzerland)2016
Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- A Study on the Influence of Sensors in Frequency and Time Domains on Context Recognition.Sensors (Basel, Switzerland) · 2023Article
- Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL.Medicine, health care, and philosophy · 2023Article
- Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications.Sensors (Basel, Switzerland) · 2019Review
- Multi-Sensor Fusion for Activity Recognition-A Survey.Sensors (Basel, Switzerland) · 2019Review
- Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview.Sensors (Basel, Switzerland) · 2019Article
- Virtual Sensors for Optimal Integration of Human Activity Data.Sensors (Basel, Switzerland) · 2019Article
- Feature Extraction in Motor Activity Signal: Towards a Depression Episodes Detection in Unipolar and Bipolar Patients.Diagnostics (Basel, Switzerland) · 2019Article
- The Key Factors in Physical Activity Type Detection Using Real-Life Data: A Systematic Review.Frontiers in physiology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human Activity Recognition (HAR) is an important part of ambient intelligence systems since it can provide user-context information, thus allowing a greater personalization of services. One of the problems with HAR systems is that the labeling process for the training data is costly, which has hindered its practical application. A common approach is to train a general model with the aggregated data from all users. The problem is that for a new target user, this model can perform poorly because it is biased towards the majority type of users and does not take into account the particular characteristics of the target user. To overcome this limitation, a user-dependent model can be trained with data only from the target user that will be optimal for this particular user; however, this requires a considerable amount of labeled data, which is cumbersome to obtain. In this work, we propose a method to build a personalized model for a given target user that does not require large amounts of labeled data. Our method uses data already labeled by a community of users to complement the scarce labeled data of the target user. Our results showed that the personalized model outperformed the general and the user-dependent models when labeled data is scarce.
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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.