## 5 Kinematic Assessment of Movement Performance
### Gym Physical Activity Tracking  
Within the physical activity context, most wearable devices and sensors to assess performance are designed for aerobic and cardiorespiratory activity, monitoring and tracking different parameters such as the heart rate and the localization through GNSS. Gym physical activity, such as weightlifing, is less assessed if compared to walking, running, swimming, etc., probably because of its diversity and complexity [169]. Furthermore, many of these devices are based on machine learning models, such as random forests, decision trees, hidden Markov models, support vector machines, and naive Bayes classifiers. Thanks to the possibility of collecting more data from novel smartphones, fitness bands, and smartwatches, many studies have based their models on deep learning algorithms [170], which seems to be leading to interesting outcomes in this application field due to its capabilities in classifying large amounts of data. Resistance exercise, generally performed within gyms, is not essential for athletes to enhance their performance; only strength improvement is possible [171]. It is also a non-pharmaceutical method that promotes a positive association in preventing age-related pathologies, such as sarcopenia and osteoporosis [172], representing a sports pillar and a clinical aid. Due to the insufficient number of research in this field of exercise, probably caused by low funding compared to other exercise applications such as in endurance, several team sports, and clinical areas, not many devices have been developed to help practitioners of physical activity within the gym until now. Some authors tried to create sources to enhance gym training. For instance, ref. [173] developed an application to use during gym training, designed for a commercial smartwatch, to recognize the gym activity through which the repetitions performed with both bodyweight and additional weights exercises could be counted. Data collected from this app could record and diversify active (performing exercises) and passive (drinking, resting, walking) training time. The authors used a six-DOF IMU included within the smartwatch. A specific axis (for accelerometer and gyroscope) for each exercise has been selected and filtered, and a proper algorithm has been implemented to count every repetition for each of the performed activities. Results from this proposal have shown high accuracy, specificity, and sensitivity. The authors in [169] attempted to develop a similar framework, which can recognize physical activity in a gym, utilizing an ECG and an accelerometer. They used a machine learning framework based on two hidden layers. Outcomes from this experiment have shown that the developed framework could classify nineteen different gym physical activities, calculate repetitions, and sets of every free weight exercise performed, and assess its intensity through the changes in heart rate and the calculations based on the repetition maximum principle. A further study investigated the potential of sensor-based gym physical exercise recognition [174]. The authors' experiment model was based on a long short-term memory neural network with 100 Hz. In [175], the acquired data was classified using an artificial neural network approach. The ANN algorithms are widely employed in classification problems because they can efficiently obtain a valuable outcome after exploiting proper training procedures over a large amount of data, even when handling large amounts of raw outputs from different sensors. In this case, three layers of a particular feed-forward ANN (i.e., Convolutional Neural Network--CNN) were used to classify data obtained from a forearm band sensor (Push) and a device utilized in several professional sports teams. Data used was already present in the dataset given by Push Inc. Authors have considered only the fifty most frequently performed exercises from such a dataset. Measurements were obtained from the accelerometer and the gyroscope within the sensor, with a sampling frequency of 200 Hz.