{"type":"ScholarlyArticle","title":"AI-powered Posture Training: Application of Machine Learning in Sitting Posture Recognition Using the LifeChair Smart Cushion","authors":[{"name":"Katia Bourahmoune","slug":"katia-bourahmoune","given":"Katia","family":"Bourahmoune","affiliation":"Graduate School of Systems and Information Engineering , University of Tsukuba , Tsukuba , Japan"},{"name":"Toshiyuki Amagasa","slug":"toshiyuki-amagasa","given":"Toshiyuki","family":"Amagasa","affiliation":"Center for Computational Sciences , University of Tsukuba , Tsukuba , Japan"}],"abstract":"Humans spend on average more than half of their day sitting down. The ill-effects of poor sitting posture and prolonged sitting on physical and mental health have been extensively studied, and solutions for curbing this sedentary epidemic have received special attention in recent years. With the recent advances in sensing technologies and Artificial Intelligence (AI), sitting posture monitoring and correction is one of the key problems to address for enhancing human well-being using AI. We present the application of a sitting posture training smart cushion called LifeChair that combines a novel pressure sensing technology, a smartphone app interface and machine learning (ML) for real-time sitting posture recognition and seated stretching guidance. We present our experimental design for sitting posture and stretch pose data collection using our posture training system. We achieved an accuracy of 98.93% in detecting more than 13 different sitting postures using a fast and robust supervised learning algorithm. We also establish the importance of taking into account the divergence in user body mass index in posture monitoring. Additionally, we present the first ML-based human stretch pose recognition system for pressure sensor data and show its performance in classifying six common chair-bound stretches.","doi":"10.24963/ijcai.2019/805","url":"https://ijcai.org/proceedings/2019/805","pdf":"https://ijcai.org/proceedings/2019/0805.pdf","markdown":"https://ijcai.org/proceedings/2019/805.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/805","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"AI for Improving Human Well-being","track":"AI for Improving Human Well-being","number":805,"pages":{"first":5808,"last":5814},"keywords":["Special Track on AI for Improving Human-Well Being: Workplace applications (Special Track on AI and Human Wellbeing)","Special Track on AI for Improving Human-Well Being: Health applications (Special Track on AI and Human Wellbeing)","Special Track on AI for Improving Human-Well Being: Human wellbeing (Special Track on AI and Human Wellbeing)","Special Track on AI for Improving Human-Well Being: AI applications for Improving Human-Well Being (Special Track on AI and Human Wellbeing)"],"videos":[],"references":[{"position":1,"raw":"[Biswas et al., 2015] Aviroop Biswas, Paul I Oh, Guy E Faulkner, Ravi R Bajaj, Michael A Silver, Marc S Mitchell, and David A Alter. 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