# Lifting Activity Assessment Using Kinematic Features and Neural Networks
## 1 Introduction  
Manual lifting tasks are very common in a variety of workplaces [1] and have been demonstrated to influence the occurrence of musculoskeletal problems such as work-related low-back disorders (WLBDs) [2; 3; 4]. Over the past three decades, a growing effort has been made to evaluate the effectiveness of ergonomic interventions in preventing and reducing the risk of developing WLBDs. Among the proposed quantitative methods, the revised National Institute for Occupational Safety and Health (NIOSH) lifting equation (RNLE) [3; 5; 6; 7; 8; 9] is an established means to assess risk of low back pain (LBP) due to manual lifting. More recently, in order to overcome equation and parameter restrictions [10; 11; 12; 13; 14; 15; 16; 17], to increase the accuracy and minimize job misidentification [10; 18] and to improve the identification of the relationship between WLBDs and risk factors [19], wearable monitoring devices have been proposed for biomechanical risk assessment [20; 21]. Among these, inertial measurement units (IMUs), instrumented gloves, surface electromyography (sEMG) sensors, smart footwear-based wearable systems [22; 23; 24; 25] and vision-based tracking systems [26] can monitor workers' motor behavior if individually placed on the body segments or embedded in elastic suits. The use of these instrumental techniques has been facilitated by the enormous technological advances that led to increased measurement accuracy, device miniaturization, and more efficient connections via wireless protocols (i.e., WiFi and Bluetooth). Among the instrumental and quantitative approaches proposed to evaluate different tasks and classify the biomechanical risk, those based on artificial neural networks (ANNs) are particularly promising [27; 28; 29; 30]. An ANN is a model, constituted by elementary processing units (neurons) interconnected in different topologies, mimicking the behavior of real neuronal networks. In layered ANNs, the neurons are arranged in several layers through which information is transmitted in a single direction. The characteristics of each neuron behavior are modified based on the presentation of a number of examples as inputs, through different learning methods. This makes thus ANNs able to respond effectively to generalization, classification and clustering problems, if a sufficient number of examples is provided. For this reason, ANNs' impact spread over many fields of research, including robotics, biology, psychology, computer science, and ergonomics. For the WLBD problem the predictive capacity of this model has been proven to be greater than that achieved by statistical methods [31]. ANNs with different topologies have been tested to predict the risk of developing WLBDs: [PERSON] et al. [27] and [PERSON] et al. [28; 29] used ANNs to classify the risk (low-risk and high-risk) according to the associated probability of causing WLBDs, by using as input signals some mechanical parameters associated with risk factors (e.g., sagittal angle, lift rate, peak, peak twist velocity average, peak lateral velocity maximum, peak moment). Not only mechanical features, but also surface electromyography (sEMG) signals recorded from trunk muscles during the performance of lifting tasks can provide risk-related information. Amplitude and coordination parameters calculated from sEMG data (maximum, average rectified value, root mean square, muscle coactivation, [32; 33] have been shown to be correlated to the compression and shear forces and moments at L5-S1 joint, and able to detect the risk levels. Recently, [PERSON] and colleagues [30] used ANNs for mapping surface sEMG features on lifting index (LI) levels for biomechanical risk estimation, defined based on the RNLE. They have shown that LI levels during lifting tasks can be reliably estimated through multi-layered ANNs by using a set of combined sEMG time and frequency features obtained only from the erector spinae muscle.