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Publication . Conference object . 2020

On the analysis of human posture for detecting social interactions with wearable devices

Paolo Baronti; Michele Girolami; Fabio Mavilia; Filippo Palumbo; Giancarlo Luisetto;
Open Access
English
Published: 01 Jan 2020
Country: Italy
Abstract
Detecting the dynamics of the social interaction represents a difficult task also with the adoption of sensing devices able to collect data with a high-temporal resolution. Under this context, this work focuses on the effect of the body posture for the purpose of detecting a face-to-face interactions between individuals. To this purpose, we describe the NESTORE sensing kit that we used to collect a significant dataset that mimics some common postures of subjects while interacting. Our experimental results distinguish clearly those postures that negatively affect the quality of the signals used for detecting an interactions, from those postures that do not have such a negative impact. We also show the performance of the SID (Social Interaction Detector) algorithm with different settings, and we present its performance in terms of accuracy during the classification of interaction and non-interaction events.
Subjects by Vocabulary

Microsoft Academic Graph classification: Body posture Task (project management) Quality (business) media_common.quotation_subject media_common Computer science Social relation Wearable technology business.industry business Context (language use) Bluetooth Low Energy computer.internet_protocol computer Human–computer interaction

Subjects

Social Interactions, Proximity, Bluetooth Low Energy, Social Interactions, Proximity, Bluetooth Low Energy

Funded by
EC| NESTORE
Project
NESTORE
Novel Empowering Solutions and Technologies for Older people to Retain Everyday life activities
  • Funder: European Commission (EC)
  • Project Code: 769643
  • Funding stream: H2020 | RIA
Validated by funder
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