A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers

Physiological Measurement

Volumn 35, Issue 11, 2014, Pages 2191-2203

  Ellis, Katherine ^a   Kerr, Jacqueline ^b   Godbole, Suneeta ^b   Lanckriet, Gert ^a   Wing, David ^b   Marshall, Simon ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

heart rate; machine learning; random forest

Indexed keywords

ARTIFICIAL INTELLIGENCE; BICYCLES; DECISION TREES; FORECASTING; HEART; LEARNING SYSTEMS; MEAN SQUARE ERROR; STAIRS; WALKING AIDS;

ACTIVITY BEHAVIOR; ADDED VALUES; ENERGY EXPENDITURE; HEART-RATE; LOCOMOTION ACTIVITY; METABOLIC EQUIVALENTS; PHYSICAL ACTIVITY; POPULATION LEVELS; RANDOM FOREST CLASSIFIER; RANDOM FORESTS;

ACCELEROMETERS;

ACCELEROMETRY; ADULT; AMBULATORY MONITORING; ARTIFICIAL INTELLIGENCE; CALORIMETRY; DEVICES; ENERGY METABOLISM; FEMALE; HEART RATE; HIP; HUMAN; MALE; MOTOR ACTIVITY; PHYSIOLOGY; WRIST;

ACCELEROMETRY; ADULT; ARTIFICIAL INTELLIGENCE; CALORIMETRY, INDIRECT; ENERGY METABOLISM; FEMALE; HEART RATE; HIP; HUMANS; MALE; MONITORING, AMBULATORY; MOTOR ACTIVITY; WRIST;

EID: 84908413187     PISSN: 09673334     EISSN: 13616579     Source Type: Journal    
DOI: 10.1088/0967-3334/35/11/2191     Document Type: Article

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