RESUMEN
Monitoring health and fitness is emerging as an important benefit that smartphone users could expect from their mobile devices today. Rule of thumb calorie tracking and recommendation based on selective activity monitoring is widely available today, as both on-device and server based solutions. What is surprisingly not available to the users is a simple application geared towards quantitative fitness tracking. Such an application potentially can be a direct indicator of one's cardio-vascular performance and associated long term health risks. Since wearable devices with various inbuilt sensors like accelerometer, gyroscope, SPO2 and heart rate are increasingly becoming available, it is vital that the enormous data coming from these sensors be used to perform analytics to uncover hidden health and fitness associated facts. A continuous estimation of fitness level employing these wearable devices can potentially help users in setting personalized short and long-term exercise goals leading to positive impact on one's overall health. The present work describes a step in this direction. This work involves an unobtrusive method to track an individual's physical activity seamlessly, estimate calorie consumption during a day by mapping the activity to the calories spent and assess fitness level using heart rate data from wearable sensors. We employ a heart rate based parameter called Endurance to quantitatively estimate cardio-respiratory fitness of a person. This opens up avenues for personalization and adaptiveness by dynamically using individual's personal fitness data towards building robust modeling based on analytical principles.