## 8 Sensor Fusion
A further paper by the same authors deals with a machine learning algorithm that divides the stroke cycle into the propulsion and recovery phases and obtains a quantitative kinematic analysis together with assessing the intra-limb joint symmetry in the padding action [275]. The application of wireless sensor fusion for heart rate and motion acceleration in individual competitive sports has been shown in recent studies to be much more precise and accurate than traditional methods [276]. However, as different applications require different numbers and types of sensors, it is difficult to define an overarching optimal number of sensors for any given system. On this topic, the SmartSki prototype, proposed by [277], has been designed employing IoT technologies to connect ski equipment and body sensors. Such a system can measure the force applied to the skis and their corresponding bend exploiting some force and bend sensors wired connection to a central unit stored in a backpack worn by the skier and placed in different positions on each of the skis with the aim of assesses the distribution of the force applied from the skier to each ski. In particular, the force sensors have been placed in the left and right positions at each ski's toe and heel bindings, respectively, while the bend ones have been positioned in the front and rear skies sections. In addition, the system hosts a six DoF IMU attached to the skier's torso and a high-definition camera mounted on his helmet. All the sensor signals are acquired at a sampling rate of 100 Hz and synchronized using a cRIO board (by National Instruments) (Figure 12). By processing some of the raw acquired signals together, SmartSki can obtain, among other parameters, the total dynamic load, the tilt, and edge balance, which are not directly measurable by any single sensor. A proper sensor fusion algorithm, combining accelerometer data with those acquired by the skies' load sensors, can detect their carving motion more reliably. Furthermore, the images recorded with the helmet camera can also be exploited to assess the skier's technique. They could be integrated with the outputs of other sensors to improve the reliability of the evaluation. Ref. [278] presents a system that has been developed for the identification of basketball shooting postures exploiting deep learning-based algorithms. This system consists of two IMU sensors placed on the main force hand and foot, respectively (e.g., right hand and left foot for right-hand players). The sensors acquire kinematic data (linear acceleration and angular velocity) at a sample rate of 100 Hz, and they are wirelessly connected to a Personal Computer. As reported in the paper, not all basketball shooting postures can be identified  
Figure 12: SmartSki system represents force and bend measurements on skis. It includes sensors, electronics, an IMU device, and a backpack for hosting the measurement devices. cRio system receives 100 Hz sensor signals. by the movement of the main force hand. Indeed, for composite shooting postures such as the stop-jump shot, the kinematic data acquired by the IMU of such an arm cannot describe the features of the movement. Thus, as the first step, the algorithm proposed by the authors performs a merge of the kinematic data obtained by hand and foot sensors to obtain the input to feed the designed deep learning posture identification algorithm.