# Applying Wearable Technology and a Deep Learning Model to Predict Occupational Physical Activities
## 3 Results  
A total of 15 healthy participants with an average age of 31 \(\pm\) 13.6 years (9 males, 6 females, Table 3) participated in this study. The average height and weight of individuals were 169.4 \(\pm\) 14.8 cm and 68.2 \(\pm\) 15.6 kg, respectively. None of them reported any recent injuries or chronic diseases. The CNN achieved an overall accuracy of 95% in test data but differed by OPA with one handed lifting/lowering having the lowest accuracy (83%) and six OPAs having an accuracy of 100% (Table 4). Figure 5: This diagram shows the approach for assessing the accuracy of the CNN model. Data collected from the OPAs performed in isolation were divided into 60%, 20% and 20% for training, validation, and testing, respectively. The trained model was applied to testing data from the OPAs performed in isolations, then the simulated work tasks to predict OPAs. Multimedia video task analysis was performed on the simulated work tasks and compared with model predictions to estimate the accuracy of the predictive model. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Gender**} & \multirow{2}{*}{**N**} & **Age** & **Height** & **Weight** & **BMI** & **Lower Leg** & **UPPER LEG** & **Lower Arm** & **Upper Arm** \\ \cline{3-10}  & & **(yrs)** & **(cm)** & **(kg)** & **(kg/m\({}^{2}\))** & **(cm)** & **(cm)** & **(cm)** & **(cm)** & **(cm)** \\ \hline Male & 9 & Mean & 31 & 177 & 76.10 & 24.30 & 57.44 & 92.00 & 37.67 & 38.89 \\  & & SD & 15.43 & 12.13 & 15.43 & 4.30 & 3.68 & 6.54 & 2.87 & 3.26 \\ Female & 7 & Mean & 33 & 165 & 61.16 & 22.45 & 52.17 & 85.67 & 33.33 & 34.33 \\  & & SD & 15.79 & 4.40 & 7.11 & 1.75 & 2.64 & 2.42 & 1.97 & 2.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Demographics and anthropometric data of subjects. The data collected from bottle packaging tasks, carpet laying tasks and drilling tasks were combined and processed by the trained CNN model. The top CNN OPA prediction was compared with MVTA results for each frame to calculate the prediction accuracy (Table 5). Based on the 1503 frames, the average accuracy was 22% with accuracies varying by OPA. The lowest accuracy was 0% (one-handed lifting) and the highest accuracy was 95% (overhead work). The analysis of the three best CNN predictions was higher; the average prediction accuracy was 45% (Table 6). Some OPA prediction accuracies were high; accuracy of one-handed pulling and overhead work exceeded 95%, while the accuracy for pushing reached 88%. However, prediction accuracy of other OPAs was very low. For example, kneeling had an accuracy of 7%, being commonly mistaken for crouching (Figure 6). None of the one-handed lifts were correctly predicted. Many frames of stooping were predicted as reaching. \begin{table}
\begin{tabular}{c c c} \hline \hline
**OPA Category** & **N of Correct Samples (Windows)** & **Accuracy** \\ \hline Overhead work & 194/194 & 100\% \\ Sitting & 97/97 & 100\% \\ Crawling & 78/79 & 98\% \\ Standing & 103/103 & 100\% \\ Carrying & 271/271 & 100\% \\ Walking & 101/106 & 95\% \\ Pushing & 170/180 & 94\% \\ Reaching & 279/279 & 100\% \\ Static stooping & 84/84 & 100\% \\ Kneeling & 84/85 & 98\% \\ Crouching & 85/96 & 88\% \\ Lifting/Lowering & 1028/1064 & 96\% \\ One-handed lifting/lowering & 273/327 & 83\% \\ Pulling & 80/94 & 85\% \\ One-handed pulling & 194/207 & 93\% \\ \hline Overall & 3121/3266 & 95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CNN model accuracy of different OPA categories (\(N\) = number of correct predictions over total samples for each OPA. Accuracy is the percentile of correct predictions divided by total samples).