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PEOPLE@HES-SO – Directory and Skills inventory
PEOPLE@HES-SO – Directory and Skills inventory

PEOPLE@HES-SO
Directory and Skills inventory

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Bruegger Pascal

Bruegger Pascal

Professeur HES associé

Main skills

Software Engineering

Applications mobiles

Système distribués

Environnements intelligents

  • Contact

  • Teaching

  • Publications

Main contract

Professeur HES associé

Phone: +41 26 429 69 34

Desktop: HEIA_D20.17

Haute école d'ingénierie et d'architecture de Fribourg
Boulevard de Pérolles 80, 1700 Fribourg, CH
HEIA-FR
Institute
iCoSys - Institut des systèmes complexes
BSc HES-SO en Informatique - Haute école d'ingénierie et d'architecture de Fribourg
  • Développement Mobile Android
  • Développement Mobile iOS
  • Développement Mobile cross-plateforme
MSc HES-SO en Engineering - HES-SO Master
  • Systèmes d'exploitation mobiles et applications

2021

A Machine learning multi-class approach for fall detection systems based on wearable sensors with a study on sampling rates selection
Scientific paper ArODES

Nicolas Zurbuchen, Adriana Wilde, Pascal Bruegger

Sensors,  2021, vol. 21(3), no. 938

Link to the publication

Summary:

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection.

2020

A Comparison of Machine Learning Algorithms for Fall Detection using Wearable Sensors
Scientific paper

Zurbuchen Nicolas, Bruegger Pascal, Adriana Wilde

2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (ICAIIC 2020), 2020

Link to the publication

2019

Humanitarian Organization ICT field specialists training : Bridging theoretical and practical humanitarian knowledge
Scientific paper

Vallo Docampo Mariana, Bruegger Pascal

IEEExplore, 2019

Link to the publication

Summary:

Information and communication technologies are central tools for Humanitarian Organizations operations. ICT field specialists are real practical experts, but the lack of theoretical knowledge prevents them from being self-sufficient and efficient in unexpected situations. United Nations (UN) members have agreed on defining 17 Sustainable Development Goals (SDG). As a University of Applied Sciences, we are concerned about the development of skills in information and communication technologies (ICT). This concern is shared in the “SDG 4: Quality education”. Training can be a controversial topic in the humanitarian field because of the priority of operations activities. Our aim was to design a training device which could cope with this priority. Through our partnership with a Humanitarian Organization (HO), we built a full and innovating hybrid training methodology for ICT field specialists. This methodology is based on state-of-the-art education and ergonomics techniques. Work analysis and ergonomic theory assume that in order to offer a pertinent and transferable training, the learnings must be built on the real work the trainees do. In this study, we present our global training concept in broad terms. We focus here on the results of the first implemented phase. This phase concerns the assessment of the ICT theoretical knowledge of our HO partner's specialists.

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