Publications

@inproceedings{augelloMultimodalMoodRecognition2022,

title = {Multimodal Mood Recognition for Assistive Scenarios},

author = { Agnese Augello and Giulia Di Bella and Ignazio Infantino and Giovanni Pilato and Gianluigi Vitale},

editor = { Samsonovich A.V. Ramos Corchado F.F.},

doi = {10.1016/j.procs.2022.11.098},

year  = {2022},

date = {2022-01-01},

booktitle = {Procedia Computer Science},

volume = {213},

pages = {510--517},

publisher = {Elsevier B.V.},

abstract = {We illustrate a system performing multimodal human emotion detection from video input through the integration of audio emotional recognition, text emotional recognition, facial emotional recognition, and emotional recognition from a spectrogram. The outcomes of the four emotion recognition modalities are compared, and a final evaluation provides the most likely perceived emotion. The system has been designed to be easily implemented on cheap mini-computer based boards. It is conceived to be used as auxiliary tool in the field of telemedicine to remotely monitor the mood of patients and observe their healing process, which is closely related to their emotional condition. textcopyright 2022 The Author(s).},

keywords = {Assistive Robots, Emotion Analysis, Emotion Recognition, Mood},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{augello_multimodal_2022,

title = {Multimodal Mood Recognition for Assistive Scenarios},

author = {Agnese Augello and Giulia Di Bella and Ignazio Infantino and Giovanni Pilato and Gianluigi Vitale},

editor = {Samsonovich A. V. Ramos Corchado F.F.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146111070&doi=10.1016%2fj.procs.2022.11.098&partnerID=40&md5=5f02507ae4efe3f3fe476ce3f7dbc63d},

doi = {10.1016/j.procs.2022.11.098},

year  = {2022},

date = {2022-01-01},

booktitle = {Procedia Computer Science},

volume = {213},

pages = {510–517},

publisher = {Elsevier B.V.},

abstract = {We illustrate a system performing multimodal human emotion detection from video input through the integration of audio emotional recognition, text emotional recognition, facial emotional recognition, and emotional recognition from a spectrogram. The outcomes of the four emotion recognition modalities are compared, and a final evaluation provides the most likely perceived emotion. The system has been designed to be easily implemented on cheap mini-computer based boards. It is conceived to be used as auxiliary tool in the field of telemedicine to remotely monitor the mood of patients and observe their healing process, which is closely related to their emotional condition. © 2022 The Author(s).},

keywords = {Assistive Robots, Emotion Analysis, Emotion Recognition, Mood},

pubstate = {published},

tppubtype = {inproceedings}

}