MINDSET: A benchMarking suIte exploring seNsing Data for SElf sTates inference
C Karagianni, E Paraschou… - 2023 IEEE 10th …, 2023 - ieeexplore.ieee.org
Ubiquitous devices, such as smartphones and wearables, are becoming increasingly
popular for monitoring user behavior, health, and well-being. Through omnipresent …
popular for monitoring user behavior, health, and well-being. Through omnipresent …
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 …
Investigating the reliability of self-report data in the wild: The quest for ground truth
Inferring human mental state (eg, emotion, depression, engagement) with sensing
technology is one of the most valuable challenges in the affective computing area, which …
technology is one of the most valuable challenges in the affective computing area, which …
Mobile sensing: Leveraging machine learning for efficient human behavior modeling
EK Barrett, CM Fard, HN Katinas… - 2020 Systems and …, 2020 - ieeexplore.ieee.org
Smartphones can collect millions of data points from each of its users daily, contributing to a
significant change in how the healthcare community approaches health monitoring. This …
significant change in how the healthcare community approaches health monitoring. This …
Intosis: Interactive observation of smartphone inferred symptoms for in-the-wild data
Current research in passive health monitoring utilizes machine learning methods to infer
users' symptoms and health status from smartphone-sensed data, which can be gathered on …
users' symptoms and health status from smartphone-sensed data, which can be gathered on …
Using wearable sensors and real time inference to understand human recall of routine activities
Users' ability to accurately recall frequent, habitual activities is fundamental to a number of
disciplines, from health sciences to machine learning. However, few, if any, studies exist that …
disciplines, from health sciences to machine learning. However, few, if any, studies exist that …
Physiological stress level estimation based on smartphone logs
N Yamamoto, K Ochiai, A Inagaki… - … on mobile computing …, 2018 - ieeexplore.ieee.org
Recently, inferring the state of people's mental health via passive mobile sensing has
attracted significant attention. Previous studies have used the self-assessed stress levels as …
attracted significant attention. Previous studies have used the self-assessed stress levels as …
Measuring self-esteem with passive sensing
Self-esteem encompasses how individuals evaluate themselves and is an important
contributor to their success. Self-esteem has been traditionally measured using survey …
contributor to their success. Self-esteem has been traditionally measured using survey …
Estimating individualized daily self-reported affect with wearable sensors
S Yan, H Hosseinmardi, HT Kao… - 2019 IEEE …, 2019 - ieeexplore.ieee.org
Wearable sensors (smart watches, health/fitness trackers, etc.) are experiencing an
explosion in popularity. Their pervasiveness allows for effective data collections to quantify …
explosion in popularity. Their pervasiveness allows for effective data collections to quantify …
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 …