Wearable Heart Rate monitors are used in sports to provide physiological insights into athletes’ well-being and performance. Their unobtrusive nature and ability to provide reliable heart rate measurements facilitate the estimation of cardiorespiratory fitness of athletes, as quantified by maximum consumption of oxygen uptake. Previous studies have employed data-driven models which use heart rate information to estimate the cardiorespiratory fitness of athletes. This signifies the physiological relevance of heart rate and heart rate variability for the estimation of maximal oxygen uptake. In this work, the heart rate variability features that were extracted from both exercise and recovery segments were fed to three different Machine Learning models to estimate maximal oxygen uptake of 856 athletes performing Graded Exercise Testing. A total of 101 features from exercise and 30 features from recovery segments were given as input to three feature selection methods to avoid overfitting of the models and to obtain relevant features. This resulted in the increase of model’s accuracy by 5.7% for exercise and 4.3% for recovery. Further, post-modelling analysis was performed to remove the deviant points in two cases, initially in both training and testing and then only in training set, using k-Nearest Neighbour. In the former case, the removal of deviant points led to a reduction of 19.3% and 18.0% in overall estimation error for exercise and recovery, respectively. In the latter case, which mimicked the real-world scenario, the average R value of the models was observed to be 0.72 and 0.70 for exercise and recovery, respectively. From the above experimental approach, the utility of heart rate variability to estimate maximal oxygen uptake of large population of athletes was validated. Additionally, the proposed work contributes to the utility of cardiorespiratory fitness assessment of athletes through wearable heart rate monitors.

In the event of large crisis, an instinctive task of the rescue services is the search for human survivors on the incident site. This is a complex and dangerous task which may lead to the loss of lives among the human crisis managers themselves. The proposed model in this paper aims to provide a solution to this problem by demonstrating the significance of robots exhibiting swarm behavior that can perform search and rescue operations. In this model, an assembly of robots is created that will scan the area for pits. MPU6050, having I2C bus interface, has been employed to monitor the straight movement of robots. The robot that finds it, will share its location with all other robots using nRF modules that use SPI protocol to communicate with the microcontroller. All the robots will assemble at the shared location, thereby helping to search for the people faster and save their lives preventing any serious casualties.