# Lifting Activity Assessment Using Kinematic Features and Neural Networks
## 4 Discussion  
In this study, we have used ANNs and kinematic data to classify the biomechanical risk associated with freely performed dynamic lifting tasks with an increasing LI values (LI = 1, LI = 2, and LI = 3) designed using the RNLE [7, 8]. The used feed-forward ANNs were trained with different training sets, on different network topologies (numbers of neurons in each hidden layer, numbers of hidden layers,), and different combinations of kinematic features as inputs (from lifting energy consumption and jerk), taken by considering different combinations of the body segments necessary to extract them (i.e., only upper body, only upper limb, total body, etc.).  
A proper combination of kinematic features and network architectures can lead to a good classification. Indeed, a machine-learning approach based on ANNs was proven able to predict the biomechanical risk in lifting activities [27, 28, 29, 30] and kinematic data (i.e., mechanical lifting energy consumption) have been proven to be sensitive to the risk level (the higher the risk, the more these parameters increase) and also correlated with forces that determine injuries at the L5-S1 joint [34].  
In particular, the training sets, numbers of hidden layers, and numbers of neurons in each hidden layer influence the ANNs performance: the best performances were obtained by using energy consumption data derived from upper body and load, energy and smoothness data derived from the upper body-load complex, and, as expected, by considering all the energy and smoothness features.  
ANNs trained by using the configuration of the upper body-load complex and energy data alone, show a high performance. However, this configuration takes into account the kinematic behavior of the multi-segment upper body (head, trunk, upper arms, forearms and hands) and load altogether, and it could thus fail in presence of human-robot collaboration (HRC) technologies, such as wearable assistive devices worn by the workers. For instance, the use of a spinal exoskeleton [38] could strongly reduce the muscle effort without modifying the lifting kinematics, thus implying a misclassification.  
This limit linked with the use of HRC technologies could be completely eliminated, or at least greatly reduced, if the other two configurations are used. Both allow the best performance with the advantage of taking into account also information about the smoothness, the only index that could be sensitive to the use of assistive devices. Among these, SET 7 shows a reliable performance also in terms of variability, meaning that biomechanical risk estimation using this specific SET is reliable and repeatable at the same time.  
Furthermore, it is worth highlighting that misclassifications between LI = 1 and LI = 3, that represent the lowest and the highest risk classes respectively, are missing. This is a desired and acceptable behavior of an LI classifier.  
While complex network architectures (i.e., more than 2 hidden layers) were needed in order to reach suitable performances using ANNs to map sEMG features onto LI levels [30], in this study we have shown that increasing the network complexity does not lead to an increased risk estimation. Indeed, the best performance is almost always obtained with only one hidden layer (see Figure 3 and Table 2). This aspect is of importance when dealing with applications in real scenarios using wearable sensors, since the reduced network complexity lowers the overall computational cost needed to train the ANNs.