Technological advancement has profoundly impacted how people share meals, fostering research interest in new forms of commensality such as tele-dining and eating with artificial companions. Consequently, there is a need to develop computational methods for recognizing commensal activities, that is, actions related to food consumption and social signals displayed during meal-time. This paper introduces the first dataset that consists of synchronized video data of co-located dining dyads. The dataset is annotated with key social signals such as speaking activity, smiling, and food-related activities like chewing and food intake. Unlike previous studies that use remote settings, this work emphasizes the differences between online and co-located setups. A set of machine learning experiments is conducted on our and existing datasets, reaching the best F-score of 0.82. The cross-dataset analysis between co-located and online datasets also evidences the significant disparity between these two settings. While mixing co-located and online recordings may increase the model’s generalizability, we notice strong differences between the two settings, highlighting the importance of in-person data recordings for accurate recognition.

Automatic Recognition of Commensal Activities in Co-located and Online settings

Cigdem Beyan
;
2024-01-01

Abstract

Technological advancement has profoundly impacted how people share meals, fostering research interest in new forms of commensality such as tele-dining and eating with artificial companions. Consequently, there is a need to develop computational methods for recognizing commensal activities, that is, actions related to food consumption and social signals displayed during meal-time. This paper introduces the first dataset that consists of synchronized video data of co-located dining dyads. The dataset is annotated with key social signals such as speaking activity, smiling, and food-related activities like chewing and food intake. Unlike previous studies that use remote settings, this work emphasizes the differences between online and co-located setups. A set of machine learning experiments is conducted on our and existing datasets, reaching the best F-score of 0.82. The cross-dataset analysis between co-located and online datasets also evidences the significant disparity between these two settings. While mixing co-located and online recordings may increase the model’s generalizability, we notice strong differences between the two settings, highlighting the importance of in-person data recordings for accurate recognition.
2024
Activity recognition, commensality, datasets, social interactions, co-located, in-person
File in questo prodotto:
File Dimensione Formato  
IC33_Automatic Recognition of Commensal Activities.pdf

accesso aperto

Tipologia: Documento in Post-print
Licenza: Creative commons
Dimensione 1.58 MB
Formato Adobe PDF
1.58 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1133206
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact