TY - GEN
T1 - Estimating caloric intake in bedridden hospital patients with audio and neck-worn sensors
AU - Zhang, Shibo
AU - Nguyen, Dzung
AU - Zhang, Gan
AU - Xu, Runsheng
AU - Maglaveras, Nikolaos
AU - Alshurafa, Nabil
N1 - Publisher Copyright:
© 2018 ACM.
PY - 2018
Y1 - 2018
N2 - We present an approach for estimating calorie intake given a limited number of foods provided to patients in an in-bed setting. Data collected from a proximity sensor, inertial measurement unit, ambient light, and audio sensor placed around the neck are used to classify food-Type consumed by second using a random forest classifier. A multiple linear regression model is then developed for each food-Type to map second-level features to calories per second. We conducted a user study in a patient simulated lab setting, where 10 participants were asked to eat while sitting on a patient bed. A user-independent analysis demonstrated food-Type detection at 97.2% F1-Score, and an average Absolute Error of 3.0 kCal per food-Type. Our system shows promise in distinguishing food items and predicting calorie intake in a bedridden participant setting given a limited set of food items.
AB - We present an approach for estimating calorie intake given a limited number of foods provided to patients in an in-bed setting. Data collected from a proximity sensor, inertial measurement unit, ambient light, and audio sensor placed around the neck are used to classify food-Type consumed by second using a random forest classifier. A multiple linear regression model is then developed for each food-Type to map second-level features to calories per second. We conducted a user study in a patient simulated lab setting, where 10 participants were asked to eat while sitting on a patient bed. A user-independent analysis demonstrated food-Type detection at 97.2% F1-Score, and an average Absolute Error of 3.0 kCal per food-Type. Our system shows promise in distinguishing food items and predicting calorie intake in a bedridden participant setting given a limited set of food items.
KW - Calorie intake estimation
KW - Eating behavior
KW - Food identification
KW - Hospital malnutrition
KW - Wearable sensor
UR - http://www.scopus.com/inward/record.url?scp=85063253037&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063253037&partnerID=8YFLogxK
U2 - 10.1145/3278576.3278577
DO - 10.1145/3278576.3278577
M3 - Conference contribution
AN - SCOPUS:85063253037
T3 - Proceedings - 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018
SP - 1
EP - 2
BT - Proceedings - 2018 IEEE/ACM International Conference on Connected Health
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018
Y2 - 26 September 2018 through 28 September 2018
ER -
