Designing adaptive passive personal mobile sensing methods using reinforcement learning framework
Smartphone embedded sensors have created unprecedented opportunities to study human
behavior in natural conditions through continuous mobile sensing. However, continuous …
behavior in natural conditions through continuous mobile sensing. However, continuous …
Adaptive passive mobile sensing using reinforcement learning
Continuous passive sensing using smartphone embedded sensors can drain the battery
quickly, interrupting other usages of the device. In order to improve the energy efficiency in …
quickly, interrupting other usages of the device. In order to improve the energy efficiency in …
Smartwatch-Based Sensing Framework for Continuous Data Collection: Design and Implementation
Y Nishiyama, K Sezaki - Adjunct Proceedings of the 2023 ACM …, 2023 - dl.acm.org
Smartwatches are an increasingly popular technology that employs advanced sensors (eg,
location, motion, and microphone) comparable to those used by smartphones. Passive …
location, motion, and microphone) comparable to those used by smartphones. Passive …
Xuhai “Orson” Xu:“Toward Building Computational Well-Being Ecosystems”
L Meegahapola - IEEE Pervasive Computing, 2024 - ieeexplore.ieee.org
Xuhai “Orson” Xu: Passive sensing data from mobile and wearable devices could be used to
train machine learning (ML) models that infer stress, depression, energy expenditure, and …
train machine learning (ML) models that infer stress, depression, energy expenditure, and …
Smartphone sensing offloading for efficiently supporting social sensing applications
Mobile phones play a pivotal role in supporting ubiquitous and unobtrusive sensing of
human activities. However, maintaining a highly accurate record of a user's behavior …
human activities. However, maintaining a highly accurate record of a user's behavior …
Memory-aware active learning in mobile sensing systems
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 …
sensors. Our work is unique in that it takes limitations of the oracle into account when …
A framework for adaptive mobile ecological momentary assessments using reinforcement learning
Mobile ecological momentary assessments (mEMAs) require substantial user efforts to
complete, resulting in low user compliance. One major source of incompliance is triggering …
complete, resulting in low user compliance. One major source of incompliance is triggering …
EA^ 2: Energy Efficient Adaptive Active Learning for Smart Wearables
Mobile Health (mHealth) applications rely on supervised Machine Learning (ML) algorithms,
requiring end-user-labeled data for the training phase. The gold standard for obtaining such …
requiring end-user-labeled data for the training phase. The gold standard for obtaining such …
Automated mobile sensing strategies generation for human behaviour understanding
Mobile sensing plays a crucial role in generating digital traces to understand human daily
lives. However, studying behaviours like mood or sleep quality in smartphone users requires …
lives. However, studying behaviours like mood or sleep quality in smartphone users requires …
Adaptive duty cycling for place-centric mobility monitoring using zero-cost information in smartphone
Smartphones enable the collection of mobility data using various sensors. The key
challenge in the collection of continuous data is to overcome the limited battery capacity of …
challenge in the collection of continuous data is to overcome the limited battery capacity of …