title: Personalizing energy expenditure estimation using physiological signals normalization during activities of daily living.
abstract: In this paper we propose a generic approach to reduce inter-individual variability of different physiological signals (HR, GSR and respiration) by automatically estimating normalization parameters (e.g. baseline and range). The proposed normalization procedure does not require a dedicated personal calibration during system setup. On the other hand, normalization parameters are estimated at system runtime from sedentary and low intensity activities of daily living (ADLs), such as lying and walking. When combined with activity-specific energy expenditure (EE) models, our normalization procedure improved EE estimation by 15 to 33% in a study group of 18 participants, compared to state of the art activity-specific EE models combining accelerometer and non-normalized physiological signals.
authors.affiliation: Body Area Networks, imec The Netherlands, High Tech Campus 31, Eindhoven, The Netherlands. Signal Processing Systems, Eindhoven University of Technology, Den Dolech 2, Eindhoven, The Netherlands.
authors.forename: Marco
authors.initials: M
authors.lastname: Altini
authors.forename: Julien
authors.initials: J
authors.lastname: Penders
authors.forename: Ruud
authors.initials: R
authors.lastname: Vullers
authors.forename: Oliver
authors.initials: O
authors.lastname: Amft
mesh_terms: D061725:Accelerometry; D000203:Activities of Daily Living; D000328:Adult; D004562:Electrocardiography / Q000295:instrumentation / Q000379:methods; D004734:Energy Metabolism / Q000502:physiology*; D005260:Female; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D008297:Male; D008954:Models, Biological; D011187:Posture / Q000502:physiology; D057285:Precision Medicine / Q000295:instrumentation / Q000379:methods*; D012119:Respiration; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D016138:Walking / Q000502:physiology