title: Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.
abstract: Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.
authors.affiliation: Australian e-Health Research Centre, CSIRO, Digital Productivity Flagship.
authors.forename: Soumya
authors.initials: S
authors.lastname: Ghose
authors.affiliation: Australian e-Health Research Centre, CSIRO, Digital Productivity Flagship.
authors.forename: Jhimli
authors.initials: J
authors.lastname: Mitra
authors.affiliation: Australian e-Health Research Centre, CSIRO, Digital Productivity Flagship.
authors.forename: Mohan
authors.initials: M
authors.lastname: Karunanithi
authors.affiliation: Australian e-Health Research Centre, CSIRO, Digital Productivity Flagship.
authors.forename: Jason
authors.initials: J
authors.lastname: Dowling
mesh_terms: D056044:Actigraphy / Q000295:instrumentation / Q000379:methods*; D001315:Australia; D015444:Exercise / Q000502:physiology*; D006699:Home Care Services; D006801:Humans; D000069550:Machine Learning; D063731:Mobile Applications; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012648:Self Care / Q000295:instrumentation / Q000379:methods; D012680:Sensitivity and Specificity; D000068997:Smartphone; D017216:Telemedicine / Q000295:instrumentation / Q000379:methods*; D014159:Transducers