Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Prajod P"'
Autor:
Arora, Rhythm, Prajod, Pooja, Nicora, Matteo Lavit, Panzeri, Daniele, Tauro, Giovanni, Vertechy, Rocco, Malosio, Matteo, André, Elisabeth, Gebhard, Patrick
Individuals with diverse motor abilities often benefit from intensive and specialized rehabilitation therapies aimed at enhancing their functional recovery. Nevertheless, the challenge lies in the restricted availability of neurorehabilitation profes
Externí odkaz:
http://arxiv.org/abs/2406.12035
The limited size of pain datasets are a challenge in developing robust deep learning models for pain recognition. Transfer learning approaches are often employed in these scenarios. In this study, we investigate whether deep learned feature represent
Externí odkaz:
http://arxiv.org/abs/2406.11808
Automatic stress detection using heart rate variability (HRV) features has gained significant traction as it utilizes unobtrusive wearable sensors measuring signals like electrocardiogram (ECG) or blood volume pulse (BVP). However, detecting stress t
Externí odkaz:
http://arxiv.org/abs/2405.09563
Autor:
Mondellini, Marta, Nicora, Matteo Lavit, Prajod, Pooja, André, Elisabeth, Vertechy, Rocco, Antonietti, Alessandro, Malosio, Matteo
In industrial scenarios, there is widespread use of collaborative robots (cobots), and growing interest is directed at evaluating and measuring the impact of some characteristics of the cobot on the human factor. In the present pilot study, the effec
Externí odkaz:
http://arxiv.org/abs/2402.00808
Autor:
Prajod, Pooja, Nicora, Matteo Lavit, Mondellini, Marta, Tauro, Giovanni, Vertechy, Rocco, Malosio, Matteo, André, Elisabeth
Collaborative robots (cobots) are widely used in industrial applications, yet extensive research is still needed to enhance human-robot collaborations and operator experience. A potential approach to improve the collaboration experience involves adap
Externí odkaz:
http://arxiv.org/abs/2312.06643
Attention (and distraction) recognition is a key factor in improving human-robot collaboration. We present an assembly scenario where a human operator and a cobot collaborate equally to piece together a gearbox. The setup provides multiple opportunit
Externí odkaz:
http://arxiv.org/abs/2303.17619
Autor:
Heimerl, Alexander, Prajod, Pooja, Mertes, Silvan, Baur, Tobias, Kraus, Matthias, Liu, Ailin, Risack, Helen, Rohleder, Nicolas, André, Elisabeth, Becker, Linda
We present a multi-modal stress dataset that uses digital job interviews to induce stress. The dataset provides multi-modal data of 40 participants including audio, video (motion capturing, facial recognition, eye tracking) as well as physiological i
Externí odkaz:
http://arxiv.org/abs/2303.07742
Autor:
Rhythm Arora, Pooja Prajod, Matteo Lavit Nicora, Daniele Panzeri, Giovanni Tauro, Rocco Vertechy, Matteo Malosio, Elisabeth André, Patrick Gebhard
Publikováno v:
Frontiers in Artificial Intelligence, Vol 7 (2024)
IntroductionIndividuals with diverse motor abilities often benefit from intensive and specialized rehabilitation therapies aimed at enhancing their functional recovery. Nevertheless, the challenge lies in the restricted availability of neurorehabilit
Externí odkaz:
https://doaj.org/article/443185c9d5a94261bae5e35de31c5b47
Autor:
Prajod, Pooja, André, Elisabeth
Stress is prevalent in many aspects of everyday life including work, healthcare, and social interactions. Many works have studied handcrafted features from various bio-signals that are indicators of stress. Recently, deep learning models have also be
Externí odkaz:
http://arxiv.org/abs/2210.06225
Autor:
Matteo Lavit Nicora, Pooja Prajod, Marta Mondellini, Giovanni Tauro, Rocco Vertechy, Elisabeth André, Matteo Malosio
Publikováno v:
Frontiers in Robotics and AI, Vol 11 (2024)
Introduction: In this work we explore a potential approach to improve human-robot collaboration experience by adapting cobot behavior based on natural cues from the operator.Methods: Inspired by the literature on human-human interactions, we conducte
Externí odkaz:
https://doaj.org/article/f145a86c0f5e47f0b5f7337a84ada0db