SNF |

EXperiMental: Wearable Technology and EXplainable AI for Mental Health and Inclusivity in Schools

Tue, 01 Jul 2025 00:00:00 +0000

Funding: SNF, grant 230189 · July 2025 – December 2028

Mental health is a vital part of our overall well-being, especially during early adolescence, a time of rapid growth and emotional development. The initiative explores how AI, smartphones, and smartwatches can promote mental well-being in schools through collaboration among psychology, sports science, computer science, and ethics experts across Romania, Croatia, and Switzerland.

The research involves two major studies: (1) observing daily routines using digital tools and mental health check-ins, and (2) applying AI methods to identify patterns signaling mood or mental health changes. The aim is creating personalized, understandable suggestions for young people, teachers, and school psychologists.

Objectives

Understand how physical activity, sleep, and phone use relate to adolescent mental health
Design youth-friendly, privacy-protective monitoring methods via mobile devices
Develop personalised suggestions, such as encouraging a walk or improving sleep habits
Create ethically sound guidelines for technology use supporting adolescent mental well-being in schools

XAI-PAC: Towards Explainable and Private Affective Computing

Mon, 01 Jan 2024 00:00:00 +0000

Funding: SNF · January 2024 – January 2028

Wearable devices combined with AI methods can improve lives through affective computing, which tracks factors related to emotional states and stress for remote mental-health management. However, current AI methods are “black-boxed” and require vast sensitive data. The project addresses the need for explainable and privacy-aware affective computing.

Objectives

Develop novel eXplainable AI (XAI) tools for affective-computing methods based on wearable sensor data with multimodal, interactive capabilities
Develop privacy-aware ML methods for wearable sensor data, including privacy-aware personalization and domain-adaptation
Fuse privacy awareness and explainability into a single integrated approach

TRUST-ME: TRUstworthy enhancement of job SaTisfaction and productivity using Micro-sensing in work Environments

Sun, 01 Oct 2023 00:00:00 +0000

Funding: SNF · October 2023 – October 2026

The TRUST-ME initiative addresses employee well-being in modern workplaces by examining the complex relationship between employee productivity, job satisfaction, and well-being. The endeavor combines sensing technology and AI to create personalized monitoring tools, emphasizing multimodal monitoring and modeling of job satisfaction and productivity, as well as secure and private AI for productivity assessment. The approach relies on workplace sensors and privacy-preserving techniques, such as federated learning, to deliver insights while protecting employee autonomy.

PROSELF: Semi-automated Self-Tracking Systems to Improve Personal Productivity

Sat, 01 Oct 2022 00:00:00 +0000

Funding: SNF, grant 197242 · October 2022 – September 2025

PROSELF aims at investigating how emerging mobile and wearable technology can help provide an understanding of what makes people feel (and be) productive, and subsequently, to assist users with managing their productivity on a daily basis.

The project addresses concerns about workplace monitoring by focusing on knowledge workers’ empowerment rather than employer control. While existing “productivity apps” help monitor work activities, PROSELF advocates for systems that consider environmental, behavioral, and psychological factors in one’s feeling of (and indeed, observed) performance at work.

Objectives

Provide novel methods to enable self-monitoring at work in a semi-automated manner
Develop a sound modeling framework to identify proxies of specific performance indicators in users’ context data
Devise an adequate architecture to support the definition and execution of anticipatory decisions
Critically evaluate these approaches in-situ in a range of field studies

BASE: Behavioral Analytics for Smart Environments

Tue, 01 Dec 2020 00:00:00 +0000

Funding: SNF · December 2020 – November 2023

Behavioral analytics is widely recognized as being key to providing new services and solutions in many application domains. The key to behavioral analytics is to be able to identify and track individual user interactions across time and space (e.g., Web server logs). However, currently there are no viable mechanisms for service providers to track user interactions in the real world without raising significant privacy concerns. BASE develops a new privacy-aware method for understanding human behavior through activity traces (e.g., GPS, smartphone app launches). The work is based on state-of-the-art federated machine-learning approaches.

SHARING21: Future Digital Sharing Interfaces

Wed, 01 Oct 2014 00:00:00 +0000

Funding: SNF · October 2014 – September 2018

The SHARING21 project aims at understanding current practices of sharing personal content, both explicitly (e.g., with friends and family) and implicitly (e.g., with service providers through the use of their services), and to uncover corresponding end-user concerns across a wide variety of content, in order to map the design space for future user interfaces that empower end-users to stay in control of their shared data in a world full of autonomous IoT devices.

PALS: Privacy-Aware Location Sharing

Thu, 01 Apr 2010 00:00:00 +0000

Funding: SNF · Apr 2010 – Aug 2013

Many consumers today willingly give up their current location information in exchange for location-based social services. This development poses great privacy risks. PALS aims at designing, implementing, and evaluating novel methods and tools to facilitate the privacy-aware sharing of location data with friends, strangers, and operators. Its goal is to offer a decentralized approach that supports all needed features of location sharing systems without the need to centrally collect location data.