Welcome to YODA – Open Digital Archive for CSEM

The YODA archive gives access to CSEM's publications, such as its annual reports and brochures. For technical papers such as scientific publications, bibliographic information is provided, along with the full paper where this is possible.

This comprehensive database is part of CSEM's Open Access Publishing policy. For further information refer to the YODA support guide or contact repository@csem.ch.

Recent Submissions

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    ROBIOTICS—an Energy Autonomous Miniature Wireless Sensor Mote Interacting with your Phone
    (2029) Ruffieux, David; Bailat, Julien; Beuchat, P.-A.; Biselli, S,; Nussbaum, Pascal
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    Non-invasive Continuous Measurement of the Intra- Abdominal Pressure
    (2024-09-06) Wacker, Josias; Djordjevic, Srdjan; Trotovšek, Blaž; Krašna, Simon; Žumer, Jan; Haenni, Etienne; Banderet, Grégoire; Richard, Patrick; Yilmaz, Gürkan
    Abdominal compartment syndrome (ACS) is characterized by progressive intra-abdominal organ dysfunction resulting from elevated intra-abdominal pressure (IAP). Measuring the IAP with periodic intervals is essential for timely intervention, i.e., for keeping the IAP at normal levels. The current clinical method is invasive and offers only intermittent assessments, limiting its effectiveness in continuously monitoring the IAP. Here, we present a continuous multimodal IAP monitoring system (IAP-CMM), which is non-invasive and provides continuous readings. The IAP-CMM device is equipped with bioimpedance measurement (BioZ) and a mechanical muscle contraction force (MC) sensor. Electrical measurements are performed via dry electrodes. Following performance and safety verifications, the device was tested on 4 patients who underwent laparoscopic surgery. The preliminary results suggest MC has a high linear relationship with IAP and the BioZ exhibits a second order relationship with IAP. An extensive clinical study recruiting more patients is needed to draw statistically meaningful conclusions.
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    Abnormal Cardiac Rhythm Detection Based on Photoplethysmography Signals and a Recurrent Neural Network
    (2023-01-10) Jeanningros, Loic; Van Zaen, Jérôme; Aguet, Clémentine; Le Bloa, Mathieu; Porretta, Alessandra; Teres, Cheryl; Herrera, Claudia; Domenichini, Giulia; Pascale, Patrizio; Luca, Adrian; Solana Muñoz, Jorge; Vesin, Jean-Marc; Thiran, Jean-Philippe; Pruvot, Etienne; Lemay, Mathieu; Braun, Fabian
    Wearable devices based on photoplethysmography (PPG) allow for the screening of large populations at risk of cardiovascular disease. While PPG has shown the ability to discriminate atrial fibrillation (AF) - the most common cardiac arrhythmia (CA) - versus normal sinus rhythm, it is not clear whether such AF detectors are efficient in presence of CAs other than AF. We propose to apply a simple recurrent neural network (RNN) on a newly acquired dataset containing eight different types of CAs. The classifier takes sequences of inter-beat intervals (IBIs) as input and discriminates between normal and abnormal rhythm. The RNN achieved 84% accuracy in detecting abnormal rhythms. Some CAs were well detected (AF: 99.6%; atrial tachycardia: 100%), whereas other CAs were more difficult to detect (atrial flutter: 65.4%; bigeminy: 72.4%; ventricular tachycardia 80%). This study shows the potential of PPG technology to detect not only AF but also other types of CA. It highlights the strengths and weaknesses of IBI-based detection of abnormal rhythms and paves the way towards continuous monitoring of CAs in everyday life.
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    A Comparative Study on Detecting Heart Beats in Photoplethysmography Signals in Presence of Various Cardiac Arrhythmias
    (2023-01-10) Jeanningros, Loic; Le Bloa, Mathieu; Teres, Cheryl; Herrera, Claudia; Porretta, Alessandra; Pascale, Patrizio; Luca, Adrian; Solana Muñoz, Jorge; Domenichini, Giulia; Vesin, Jean-Marc; Thiran, Jean-Philippe; Pruvot, Etienne; Lemay, Mathieu; Braun, Fabian
    Wearable devices based on photoplethysmography (PPG) allow for the screening of large populations at risk of cardiovascular disease. While PPG has shown the ability to discriminate atrial fibrillation (AF) - the most common cardiac arrhythmia (CA) - versus normal sinus rhythm, it is not clear whether such AF detectors are efficient in presence of CAs other than AF. We propose to apply a simple recurrent neural network (RNN) on a newly acquired dataset containing eight different types of CAs. The classifier takes sequences of inter-beat intervals (IBIs) as input and discriminates between normal and abnormal rhythm. The RNN achieved 84% accuracy in detecting abnormal rhythms. Some CAs were well detected (AF: 99.6%; atrial tachycardia: 100%), whereas other CAs were more difficult to detect (atrial flutter: 65.4%; bigeminy: 72.4%; ventricular tachycardia 80%). This study shows the potential of PPG technology to detect not only AF but also other types of CA. It highlights the strengths and weaknesses of IBI-based detection of abnormal rhythms and paves the way towards continuous monitoring of CAs in everyday life.
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    Custom Voice Coil Actuator for flexure-based electromechanical RF switch
    (2024-06-14) Grivon, Daniel; Gumy, Mathias; Onillon, Emmanuel; Pache, Christophe
    In electromechanical RF switches the rotor is moved between stable angular positions using a DC motor sustained using ball bearings. For space applications, compliant mechanisms may overcome the well-known drawbacks of ball bearings. Nevertheless, high reluctant forces in conventional motors prevent using them as actuators for compliant mechanisms. The design of an actuator to minimize the impact of radial forces and ease the mate with flex mechanisms is presented.

Communities in YODA

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  • CSEM Archive
    The YODA archive contains two collections. The “Research Publications” collection provides bibliographic information for scientific papers including conference proceedings and presentations. And the "Marketing Material" collection includes corporate reports, brochures, and more.