Experience with using the sensewear BMS sensor system in the context of a health and wellbeing application

  • Authors:
  • Val Jones;Richard Bults;Rene De Wijk;Ing Widya;Ricardo Batista;Hermie Hermens

  • Affiliations:
  • Telemedicine Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands;Telemedicine Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands;Consumer Science & Intelligent Systems, Food & Biobased Research, Wageningen, The Netherlands;Telemedicine Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands;Telemedicine Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands;Telemedicine Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente and Cluster Non-Invasive Neuromuscular Assessment, Roessingh Research and Development, ...

  • Venue:
  • International Journal of Telemedicine and Applications
  • Year:
  • 2011

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Abstract

An assessment of a sensor designed for monitoring energy expenditure, activity, and sleep was conducted in the context of a research project which develops a weight management application. The overall goal of this project is to affect sustainable behavioural change with respect to diet and exercise in order to improve health and wellbeing. This paper reports results of a pretrial in which three volunteers wore the sensor for a total of 11 days. The aim was to gain experience with the sensor and determine if it would be suitable for incorporation into the ICT system developed by the project to be trialled later on a larger population. In this paper we focusmainly on activitymonitoring and user experience. Data and results including visualizations and reports are presented and discussed. User experience proved positive inmost respects. Exercise levels and sleep patterns correspond to user logs relating to exercise sessions and sleep patterns. Issues raised relate to accuracy, one source of possible interference, the desirability of enhancing the system with real-time data transmission, and analysis to enable real-time feedback. It is argued that automatic activity classification is needed to properly analyse and interpret physical activity data captured by accelerometry.