A machine learning approach to measure and monitor physical activity in children

Publication date: 8 March 2017 Source:Neurocomputing, Volume 228 Author(s): Paul Fergus, Abir J. Hussain, John Hearty, Stuart Fairclough, Lynne Boddy, Kelly Mackintosh, Gareth Stratton, Nicky Ridgers, Dhiya Al-Jumeily, Ahmed J. Aljaaf, Jenet Lunn The growing trend of obesity and overweight worldwide has reached epidemic proportions with one third of the global population now considered obese. This is having a significant medical impact on children and adults who are at risk of developing osteoarthritis, coronary heart disease and stroke, type 2 diabetes, cancers, respiratory problems, and non-alcoholic fatty liver disease. In an attempt to redress the issue, physical activity is being promoted as a fundamental component for maintaining a healthy lifestyle. Recommendations for physical activity levels are issued by most governments as part of their public health measures. However, current techniques and protocols, including those used in laboratory settings, have been criticised. The main concern is that it is not feasible to use multiple pieces of measurement hardware, such as VO2 masks and heart rate monitors, to monitor children in free-living environments due to weight and encumbrance constraints. This has prompted research in the use of wearable sensing and machine learning technology to produce classifications for specific physical activity events. This paper builds on this approach and presents a supervised machine learning method that utilises data obtaine...
Source: Neurocomputing - Category: Neuroscience Source Type: research