# Applying Wearable Technology and a Deep Learning Model to Predict Occupational Physical Activities
## 4 Discussion
Future research should include more variations of how each OPA is performed during the training-test dataset before it is applied to simulated work tasks. Fifteen subjects participated in the current study. Including more people in the training-test dataset would also be helpful in capturing variations in how OPAs are performed. Another reason for poor prediction accuracy was that subjects often performed multiple OPAs at the same moment while performing a task which the single model prediction approach could not resolve. For example, subjects may reach while kneeling, sitting, or standing. Standing was misclassified as lifting since subjects usually lift items when standing. This presented challenges for the CNN model in predicting the predominant activity. Despite the extensive training, evaluation, and discussion about how to classify each frame in MVTA, human judgement was used to identify the predominant activity. Figure 7: Activities with similar trunk flexion have low prediction accuracies: (**a**) one-handed lifting, an asymmetrical activity (83%); (**b**) crouching (88%). As described above when OPAs were performed in isolation, similar postures across OPAs were a reason for poor accuracy during the MMH tasks, especially since there was no information on loads being handled. Reaching was frequently predicted as lifting, overhead work, and carrying likely because those activities include shoulder flexion with an increased horizontal distance between the body and wrists. Kneeling was commonly misclassified as crawling or crouching, also likely due to the similarities in hip flexion and trunk angles, particularly when kneeling while sitting on one's heels. Crouching was misclassified as lifting or sitting, again due to similar hip flexion angles. Surprisingly, walking had low prediction accuracies and was most commonly misclassified as one-handed pulling, carrying, or pushing, all of which include walking while handling a load. Having additional information from lower extremity IMUs and/or about loads being handled may improve the OPA classification across these otherwise similar activities that all included some amount of reach. Another common reason for misclassification was when one OPA was part of or embedded within another OPA. For example, when categorizing OPAs in MVTA, the visual cues made it obvious when someone was reaching forward to lift something. In this case, the lift started at the beginning of the reach to lift an item and ended when the item was brought back to their body. In other words, if someone was reaching forward to lift something, the frames including the reach forward required to make contact with the object, were classified as part of the lift. Despite this consistency in how the models were trained, the CNN model could not differentiate these activities during the simulated activities, based on intent of the movement. This may explain why reaching was incorrectly predicted as a carry and why standing was frequently misclassified as lifting. Thus, a different approach to OPA classification will be needed in the future to help differentiate OPAs. It may be beneficial to start the classification of a lift when the load is actually being lifted versus the moment someone reaches forward to initiate a lift. To mitigate the misclassification discussed above and improve the prediction accuracy, additional IMUs and/or pressure insoles could be added to the system in future studies. For example, pressure insole information may help differentiate similar activities that primarily differ based on loads being handled such walking versus carrying, pushing or pulling and crouching versus lifting. Information from pressure insoles has been shown to distinguish such activities [36] and may help distinguish kneeling, crouching, and stooping from lifting. Pressure insole information may also help distinguish crawling from kneeling and crouching since the total force would be significantly lower since the weight is supported through the knees when crawling.