BALANCE: towards a usable pervasive wellness application with accurate activity inference

Technology offers the potential to objectively monitor people's eating and activity behaviors and encourage healthier lifestyles. BALANCE is a mobile phone-based system for long term wellness management. The BALANCE system automatically detects the user's caloric expenditure via sensor data from a Mobile Sensing Platform unit worn on the hip. Users manually enter information on foods eaten via an interface on an N95 mobile phone. Initial validation experiments measuring oxygen consumption during treadmill walking and jogging show that the system's estimate of caloric output is within 87% of the actual value. Future work will refine and continue to evaluate the system's efficacy and develop more robust data input and activity inference methods.

[1]  M. Marmot,et al.  Dietary assessment in Whitehall II: comparison of 7 d diet diary and food-frequency questionnaire and validity against biomarkers , 2001, British Journal of Nutrition.

[2]  Da‐hong Wang,et al.  Development of a new instrument for evaluating individuals' dietary intakes. , 2006, Journal of the American Dietetic Association.

[3]  G. Johansson,et al.  Underreporting of energy intake in repeated 24-hour recalls related to gender, age, weight status, day of interview, educational level, reported food intake, smoking habits and area of living , 2001, Public Health Nutrition.

[4]  N. Miller,et al.  American College of Sports Medicine's Guidelines for Exercise Testing and Prescription , 1995 .

[5]  Kuan Zhang,et al.  Improving energy expenditure estimation for physical activity. , 2004, Medicine and science in sports and exercise.

[6]  P. Thompson,et al.  ACSM's Guidelines for Exercise Testing and Prescription , 1995 .

[7]  Takaki Itoh,et al.  Development of a new instrument for evaluating leg motions using acceleration sensors (II) , 2011, Environmental Health and Preventive Medicine.

[8]  Deborah Estrin,et al.  Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype , 2007, EmNets '07.

[9]  Barbara Bruemmer,et al.  Worksite Study Promoting Activity and Changes in Eating (PACE): Design and Baseline Results , 2007, Obesity.

[10]  H. S. Bayley,et al.  Four-day multimedia diet records underestimate energy needs in middle-aged and elderly women as determined by doubly-labeled water. , 2000, The Journal of nutrition.

[11]  Tarek F. Abdelzaher,et al.  SATIRE: a software architecture for smart AtTIRE , 2006, MobiSys '06.

[12]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[13]  Gaetano Borriello,et al.  A Practical Approach to Recognizing Physical Activities , 2006, Pervasive.

[14]  Yvonne Rogers,et al.  When Do We Eat? An Evaluation of Food Items Input into an Electronic Food Monitoring Application , 2006, 2006 Pervasive Health Conference and Workshops.

[15]  Christine Thomson,et al.  American Dietetic Association , 1948 .

[16]  William G. Griswold,et al.  Usability and Feasibility of PmEB: A Mobile Phone Application for Monitoring Real Time Caloric Balance , 2006, 2006 Pervasive Health Conference and Workshops.

[17]  Caj Södergård,et al.  HyperFit: Hybrid media in personal nutrition and exercise management , 2008, PervasiveHealth.

[18]  M C Limacher,et al.  Can sedentary adults accurately recall the intensity of their physical activity? , 2001, Preventive medicine.

[19]  David W. McDonald,et al.  Activity sensing in the wild: a field trial of ubifit garden , 2008, CHI.