Journal of physical activity & health · 2011
Recognition of physical activities in overweight Hispanic youth using KNOWME Networks.
СтатьяПоддержка исследования: вне правительства СШАПоддержка службы здравоохранения СШАНазвание и аннотация: Перевод в очереди
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Emken B Adar, Li Ming, Thatte Gautam, Lee Sangwon, Annavaram Murali, Mitra Urbashi, Narayanan Shrikanth, Spruijt-Metz Donna
Аннотация
BACKGROUND: KNOWME Networks is a wireless body area network with 2 triaxial accelerometers, a heart rate monitor, and mobile phone that acts as the data collection hub. One function of KNOWME Networks is to detect physical activity (PA) in overweight Hispanic youth. The purpose of this study was to evaluate the in-laboratory recognition accuracy of KNOWME.
METHODS: Twenty overweight Hispanic participants (10 males; age 14.6 ± 1.8 years), underwent 4 data collection sessions consisting of 9 activities/session: lying down, sitting, sitting fidgeting, standing, standing fidgeting, standing playing an active video game, slow walking, brisk walking, and running. Data were used to train activity recognition models. The accuracy of personalized and generalized models is reported.
RESULTS: Overall accuracy for personalized models was 84%. The most accurately detected activity was running (96%). The models had difficulty distinguishing between the static and fidgeting categories of sitting and standing. When static and fidgeting activity categories were collapsed, the overall accuracy improved to 94%. Personalized models demonstrated higher accuracy than generalized models.
CONCLUSIONS: KNOWME Networks can accurately detect a range of activities. KNOWME has the ability to collect and process data in real-time, building the foundation for tailored, real-time interventions to increase PA or decrease sedentary time.
METHODS: Twenty overweight Hispanic participants (10 males; age 14.6 ± 1.8 years), underwent 4 data collection sessions consisting of 9 activities/session: lying down, sitting, sitting fidgeting, standing, standing fidgeting, standing playing an active video game, slow walking, brisk walking, and running. Data were used to train activity recognition models. The accuracy of personalized and generalized models is reported.
RESULTS: Overall accuracy for personalized models was 84%. The most accurately detected activity was running (96%). The models had difficulty distinguishing between the static and fidgeting categories of sitting and standing. When static and fidgeting activity categories were collapsed, the overall accuracy improved to 94%. Personalized models demonstrated higher accuracy than generalized models.
CONCLUSIONS: KNOWME Networks can accurately detect a range of activities. KNOWME has the ability to collect and process data in real-time, building the foundation for tailored, real-time interventions to increase PA or decrease sedentary time.