Simple to complex modeling of breathing volume using a motion sensor

Science of the Total Environment

Volumn 454-455, Issue , 2013, Pages 184-188

  John, Dinesh ^a   Staudenmayer, John ^b   Freedson, Patty ^b  

^a NORTHEASTERN UNIVERSITY   (United States)

^b UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

Accelerometer; Breathing volume; Machine learning

Indexed keywords

ACTIVITY INTENSITIES; BREATHING VOLUME; COMPLEX MODELING TECHNIQUES; DOSE RESPONSE RELATIONSHIPS; MULTIPLE REGRESSION METHODS; MULTIPLE REGRESSIONS; OXYGEN CONSUMPTION; PREDICTION ACCURACY;

ACCELEROMETERS; ESTIMATION; FORECASTING; LEARNING SYSTEMS; REGRESSION ANALYSIS;

DECISION TREES;

ACCELEROMETER; DOSE-RESPONSE RELATIONSHIP; MODELING; MULTIPLE REGRESSION; OXYGEN; PREDICTION; SENSOR; SPORT; VENTILATION; WALKING;

ACCELEROMETER; ACCURACY; ACTIMETRY; ADULT; ARTICLE; BREATHING PATTERN; EMPLOYMENT; ENVIRONMENTAL EXPOSURE; FEMALE; HOUSEHOLD; HUMAN; HUMAN EXPERIMENT; MALE; MULTIPLE REGRESSION; NORMAL HUMAN; POLLUTANT; PREDICTION; PRIORITY JOURNAL; RANDOM FOREST; RESPIRATORY TRACT PARAMETERS; SENSOR; SPORT; TREADMILL EXERCISE; WALKING;

ACCELEROMETRY; ACTIGRAPHY; ACTIVITIES OF DAILY LIVING; ADULT; ARTIFICIAL INTELLIGENCE; BASAL METABOLISM; EXERCISE TEST; FEMALE; HUMANS; MALE; MIDDLE AGED; MODELS, THEORETICAL; MOTOR ACTIVITY; OXYGEN CONSUMPTION; RESPIRATION;

EID: 84875799780     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2013.02.092     Document Type: Article

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