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.

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Comprehensive fitness training involves both cardiorespiratory and power components. Often power/muscle strength training is confused with cardiorespiratory endurance training. However, each of them target different physiological aspects of fitness. Although, wearable based fitness trackers designed towards cardiorespiratory endurance training are available in the market, a dedicated wearable based fitness application designed for power training/tracking is still not readily available to fitness enthusiasts. With growing usage of wearable technology to manage and track personal health, it is imperative to develop health/fitness applications for wearables. A wearable based application for power training will allow the user to track build-up of muscle strength unobtrusively over a period of time. This work provides a framework and design for automatic detection, counting repetitions of strength training Gym exercises (covering all the major muscle groups), estimate personalized calories spent in each session and track power on a standalone Gear watch (both analysis and display including User Experience(UX) design). Our proposed method detects activity with ~96% sensitivity and ~96% specificity on an average and count repetitions with an overall accuracy of >95% using motion sensor data (accelerometer, gyroscope) for eight major Gym exercises. Additionally, using heart rate sensor data we have provided a mechanism to individually track the power/muscle strength of a person. This work will give further impetus towards developing wearable based systems for personalized fitness tracking and training. This will also give an option for wearable users to address both the crucial aspects of fitness (cardiorespiratory and muscle strength).