Sensors fusion and movement analysis for sports performance optimization

Nowadays, smart wearables for sports and fitness have a huge market due to the growing concern of people in their health condition and improving their performance. Health and fitness trackers, such as smartwatches, have been developed to record activity and monitor vital signs, while being comfortable and not causing constraint in body movement. Heart rate is strongly related with energy expenditure, maximal oxygen uptake and physical activity. The benefits of using wearable sensors in human activity recognition and estimation of energy expenditure had been widely studied, achieving solutions with high accuracy and low computational cost. This work aims to gather information from feet and arms in order to make running and other sports more efficient. TUNE is a wearable device that monitors both feet to track technique and feet symmetry when running, which is an important measure in evaluation of injury occurrence and sports performance. However, a smartphone is needed to record data and to access to the information provided by TUNE. A smartwatch worn on the wrist is a great way to overcome the need of a smartphone during a run. In addiction, it can provide valuable information from the arms movement and heart rate. A wearable system, composed of TUNE and the smartwatch Moto 360, was used to measure relevant movements of upper and lower limbs and physiological signals in sports context. The acceleration and heart rate signals provided by the smartwatch were pre-processed, and acceleration signals were used to perform activity recognition. Energy expenditure was then estimated based on the activity being performed, the heart rate measures, the personal information of the subject and the GPS data from TUNE. Heart rate signal was used to determine HR zones of exercise and training intensity, and the movement of the arm was analysed through step counting and the measurement of the amplitude of the arm in terms of acceleration, which were compared with TUNE data, such as step counting and ground contact time. To validate the developed system, 4 subjects performed 2 cycles of activities. One was performed 3 times including walk very slow, normal walk, run, cycle and rope jump, and the other cycle consisted of running at a free speed. The developed system was validated against COSMED K4b2 including a Polar heart rate chest strap. The results obtained demonstrated that heart rate measurements from Moto 360 differ from the Polar chest strap ones with an error of 9% and a correlation coefficient of 0.78. Comparing the relation between speed and heart rate intensity with the physical/health information of the test subjects, there is a linear relationship between their BMI and this relation. All activities were classified with a precision above 90%, expect the walking ones. Walking very slow was most classified as normal walk, leading to low accuracies and precisions. In what regards running activity, it was classified with 90% of accuracy. Energy expenditure results showed that there are significant differences between the developed system and COSMED K4b2, with a correlation coefficient of 0.55. Walking and running presented the best results in terms of total energy expenditure value, being the overall error of the developed system of 14%. Energy expenditure estimation achieved a NRMSE of 18% for running activity, increasing to 37% when testing all activities. The observed