# A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training
## 5 Review of Sports
### Weight Lifting  
Weight lifting is similar to fitness (gym training), a fairly static discipline where movement is limited. It was the third most researched SST discipline and a total of nine research studies were published as seen in Table 17. The research was focused largely around the use of inertial movement unit sensors in a lot of studies [110; 111; 112]. Interestingly, one [113] of the studies put sensors on the equipment instead of the trainee.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Research SST Methods** & **Focus** & **Results** & & **Training** & \\ \hline
[107] & MC; LR & Improving the team performance of a (specific) volleyball team, through identifying specific skills to improve in training. & Ball blocks were found to be determinable to their team performance, so their use in training was recommended. & Ball blocks were found to their team performance, so their training was recommended. & 2 & 1 & 1 & 3 \\
[109] & LSTM & Arms, hands and wrists, standing posture and timing recognition by the use of wearable (Inertial Measure Unit and EMG sensor) and video cameras and classification of disallowed moves so that feedback is provided in training sessions. & The system use in practice was presented. The system detection process of player jumps (which has a 93\% rate for true positives and 100\% accuracy for true negatives) was described in depth. & 1 & 2 & 2 & 2 \\
[108] & k-NN & SAETA Aml system for professional team sports training presentations. Developing decision-making systems for different aspects of player training, providing automated real time feedback to coaches and athletes. & 1 & 2 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Identified studies in the domain of volleyball.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Research SST Methods** & **Focus** & **Results** & & **Training** & \\ \hline
[114] & ANN & A prediction of a female power-lifter’s performance, based on their applying their bio-metric data to an ANN model. & A prediction model was built that gives an estimation of a best dead-lift performance. & 0 & 0 & 2 & 2 \\
[111] & CNN & A training system was developed for Weight Lifting exercises. It contained fatigue and a posture warning system, based on CNN, to warm users about incorrect postures or high fatigue. & A solution was developed that warns users of incorrect movements and fatigue, based on readings from sensors and cameras. & 1 & 2 & 1 & 0 \\
[112] & SVM & Classification of Weight Lifting exercise by an SVM model currently performed from data worn on a single wrist wearable device. & 0 & 2 & 0 & 0 \\
[110] & Markov Chains, SVM, k-NN & Accurate and real time tracking of select/the use of wearable gyroscope and acceleration meter sensors (based on Inertial Measurement Units). & 0 & 2 & 0 & 0 \\
[115] & CNN & A prototype of a system for extracting poses from weightlifting sport training videos. & 0 & 2 & 2 & 0 \\
[113] & ANN & Artificial Neural Network (ANN) model to automatically evaluate exercises in weight training, by receiving data from sensor placed on Weight Lifting machines. & 0 & 2 & 2 & 0 \\
[116] & Fuzzy & The Fuzzy logic approach to evaluate if Weight Lifting exercises on machines are being executed correctly. & 0 & 0 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Identified studies in the domain of weight lifting.