ArticleIEEE transactions on mobile computing2022
Memory-Aware Active Learning in Mobile Sensing Systems.
Article in IEEE transactions on mobile computing, 2022. 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
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.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Cost-Effective Multitask Active Learning in Wearable Sensor Systems.Sensors (Basel, Switzerland) · 2025Article
- Optimizing Forecasted Activity Notifications with Reinforcement Learning.Sensors (Basel, Switzerland) · 2023Article
- Model-Agnostic Structural Transfer Learning for Cross-Domain Autonomous Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
We propose a novel active learning framework for activity recognition using wearable sensors. Our work is unique in that it takes limitations of the oracle into account when selecting sensor data for annotation by the oracle. Our approach is inspired by human-beings' limited capacity to respond to prompts on their mobile device. This capacity constraint is manifested not only in the number of queries that a person can respond to in a given time-frame but also in the time lag between the query issuance and the oracle response. We introduce the notion of
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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.