Personal llm agents: Insights and survey about the capability, efficiency and security
Since the advent of personal computing devices, intelligent personal assistants (IPAs) have
been one of the key technologies that researchers and engineers have focused on, aiming …
been one of the key technologies that researchers and engineers have focused on, aiming …
Digital biomarker applications across the spectrum of opioid use disorder
Opioid use disorder (OUD) is one of the most pressing public health problems of the past
decade, with over eighty thousand overdose-related deaths in 2021 alone. Digital …
decade, with over eighty thousand overdose-related deaths in 2021 alone. Digital …
Opitrack: a wearable-based clinical opioid use tracker with temporal convolutional attention networks
Opioid use disorder is a medical condition with major social and economic consequences.
While ubiquitous physiological sensing technologies have been widely adopted and …
While ubiquitous physiological sensing technologies have been widely adopted and …
Pulseimpute: A novel benchmark task for pulsative physiological signal imputation
Abstract The promise of Mobile Health (mHealth) is the ability to use wearable sensors to
monitor participant physiology at high frequencies during daily life to enable temporally …
monitor participant physiology at high frequencies during daily life to enable temporally …
The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol
Smoking is the leading preventable cause of death and disability in the US Empirical
evidence suggests that engaging in evidence-based self-regulatory strategies (eg …
evidence suggests that engaging in evidence-based self-regulatory strategies (eg …
Classification of lapses in smokers attempting to stop: A supervised machine learning approach using data from a popular smoking cessation smartphone app
Introduction Smoking lapses after the quit date often lead to full relapse. To inform the
development of real time, tailored lapse prevention support, we used observational data …
development of real time, tailored lapse prevention support, we used observational data …
mRisk: continuous risk estimation for smoking lapse from noisy sensor data with incomplete and positive-only labels
Passive detection of risk factors (that may influence unhealthy or adverse behaviors) via
wearable and mobile sensors has created new opportunities to improve the effectiveness of …
wearable and mobile sensors has created new opportunities to improve the effectiveness of …
The iLog methodology for fostering valid and reliable Big Thick Data
M Busso - 2024 - iris.unitn.it
Nowadays, the apparent promise of Big Data is that of being able to understand in real-time
people's behavior in their daily lives. However, as big as these data are, many useful …
people's behavior in their daily lives. However, as big as these data are, many useful …
[HTML][HTML] Towards Predicting Smoking Events for Just-in-time Interventions
Consumer-grade heart rate (HR) sensors are widely used for tracking physical and mental
health status. We explore the feasibility of using Polar H10 electrocardiogram (ECG) sensor …
health status. We explore the feasibility of using Polar H10 electrocardiogram (ECG) sensor …
mHealth interventions for health behaviors.
The use of mHealth to address modifiable health risk factors has increased exponentially in
recent years. This chapter focuses on how mHealth has been used to intervene in substance …
recent years. This chapter focuses on how mHealth has been used to intervene in substance …