The recent growth and spread of smart sensor technologies make these connected devices suitable for diagnostic and monitoring in different fields. In particular, these sensors are useful in diagnostics for control of diseases or during rehabilitation. They are also extensively used in the monitoring field, both by non-expert and expert users, to monitor health status and progress during a sports activity. For athletes, these devices could be used to control and enhance their performance. This development has led to the realization of miniaturized sensors that are wearable during different sporting activities without interfering with the movements of the athlete. The use of these sensors, during training or racing, opens new frontiers for the understanding of motions and causes of injuries. This pilot study introduced a motion analysis system to monitor Alpine ski activities during training sessions. Through five inertial measurement units (IMUs), placed on five points of the athletes, it is possible to compute the angle of each joint and evaluate the ski run. Comparing the IMU data, firstly, with a video and then proposing them to an expert coach, it is possible to observe from the data the same mistakes visible in the camera. The aim of this work is to find a tool to support ski coaches during training sessions. Since the evaluation of athletes is now mainly developed with the support of video, we evaluate the use of IMUs to support the evaluation of the coach with more precise data.
In this paper, the dynamic experimental identification of an inductive energy harvester for the conversion of vibration energy into electric power is presented. Recent advances and requirements in structural monitoring and vehicle diagnostic allow defining Autonomous Internet of Things (AIoT) systems that combine wireless sensor nodes with energy harvester devices properly designed considering the specific duty cycle. The proposed generator was based on an asymmetrical magnetic suspension and was addressed to structural monitoring applications on vehicles. The design of the interfaces of the electric, magnetic, and structural coupled systems forming the harvester are described including dynamic modeling and simulation. Finally, the results of laboratory tests were compared with the harvester dynamic response calculated through numerical simulations, and a good correspondence was obtained.
The recent growth of the IoT (Internet of Things) technologies makes these connected devices suitable for monitoring and diagnostic in different applications. Through these devices, a wireless sensor network has become a smart solution for monitoring structures, vehicles, and other devices. Each node in the network can be placed in an inaccessible or unsafe location for human intervention and provide a real-time data stream, useful for the diagnostic and maintenance of the structure. In this context, the power node becomes a fundamental problem since the replacement of batteries is a disadvantage both for environmental disposal and for the related costs. Thus, the interest in the so-called AIOT (Autonomous Internet of Things) is growing, and the energy harvester generators can be a possible solution to this problem. In this scenario, an inductive linear generator having a non-symmetrical gravitational suspension is presented. The main characteristics of the generator and the magnetic suspension are introduced with the description of the Matlab/Simulink model that simulates the same behavior. In this work, a first study of the duty cycle of the generator to power a wireless sensor node for industrial application is presented as well. This study is carried out with a particular focus on the acceleration frequency evaluation of railway vehicles to better understand the possible effective power that can be extracted from the harvester. The relevance of this work lies in the fact that the generator sizing cannot be separated from the detailed knowledge of the energy source and of the sensing/monitoring system that must be powered.
In this paper it is presented a brief introduction about the Micro ElectroMechanical Systems (MEMS) sensors technology and their application and use in healthcare and sport activity in the literature. In these two fields, our research group’s applications will then be analyzed with the support of a numerical tool able to replicate human body behavior performing a sport activity, in particular Nordic Walking and Alpine Skiing. The main goal was to obtain a comparison between the numerical and experimental results, in order to validate of the numerical tool and to better understand the sport gesture. The integrated monitoring systems enable a new interpretation of the sport gesture providing the athletes the maximum freedom of movement and allowing them to better perform in their natural training environment.
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