# Applying Wearable Technology and a Deep Learning Model to Predict Occupational Physical Activities
## 4 Discussion  
The present study used a deep learning method to predict 15 OPA activities common in MMH jobs. A convolution neural network model was applied to data from 8 IMUs to predict which of the 15 OPAs were being performed during each second of analysis. Overall, the model had an average accuracy of 95% when each OPA was performed in isolation. However, model accuracy decreased when applied to simulated work tasks that contained multiple OPAs (e.g., bottle packaging, carpet lying and drilling). The CNN model was first applied to predict occupational physical activities performed in isolation. The study indicated that the CNN model provided a reliable prediction of OPAs performed in isolation with the highest accuracy of prediction reaching 100%, and the lowest accuracy being 83%. However, even when performed in isolation, some activities had better accuracy than others; overhead work, sitting, standing, carrying, reaching and static stoop had 100% accuracy while one-handed lifting, pulling and crouching were lower (83%, 85% and 88%, respectively). One possible reason for the lower accuracy in one-handed lifting (83%) was the asymmetrical movement that varied with each lift (Figure 7a). Pulling and one-handed pulling also had lower prediction accuracy (85% and 93%, respectively) which may have also been due to activity being asymmetrical and varied. The model accuracy for crouching may have been lower (88%) due to the trunk angle being similar to other activities, such as lifting and kneeling. Future studies should include IMU data from the lower legs to evaluate whether additional IMU data improves model prediction for all OPAs by differentiating activities with similar upper body postures (Figure 7b). Figure 6: This stacked bar chart shows the top 5 other OPAs that each activity was misclassified as. The \(X\)-axis represents the OPAs performed during the simulated work tasks and the legend represents OPAs predicted by CNN. To test the robustness of the trained CNN model, it was applied to data collected during three MMH tasks. The overall model accuracy fell dramatically to 22%. Each task contained multiple OPAs and the model was unable to differentiate the OPAs with high accuracy when they were performed as part of a simulated task. To understand the inaccurate predictions, a post hoc analysis analyzed the best three CNN predictions for each second of simulated work. The overall accuracy of OPAs increased from 22% to 45%. The results of some activities improved greatly; prediction for lifting and pushing increased from 13% to 51% and 39% to 88%, respectively. This indicates that the model may have had an incorrect best prediction, but the 2 nd or 3 rd prediction was correct, at least for some OPAs. However, some activities had minimal changes in accuracy. The incorrect predictions for each OPA were graphed to visually depict erroneous predictions for each OPA. This was primarily done to develop improved models for future research, particularly for the OPAs with a low prediction accuracy. Upon further analysis, the erroneous predictions appeared to be primarily due to four circumstances including (1) variation in how the OPA was performed; (2) OPAs being performed concurrently; (3) posture similarities between OPAs; (4) an OPA being embedded in another OPA, thus confusing the model. The amount of variation in how activities were performed impacted the model prediction accuracy. For example, the CNN model was trained with people lifting using a squat technique, yet during the work simulated tasks, some people used the stop lift posture which was mistakenly predicted as crouching. Kneeling only had a 7% accuracy despite using the best three predictions. Kneeling can also be performed with much variation; there was kneeling while sitting on the heels, kneeling upright (no hip flexion) and single knee kneeling. Since the model was not trained for these variations in posture, it predicted other similar activities such as crouching or crawling.