New instrumental approaches for quantitative assessment of biomechanical risk in occupational field

Abstract

This PhD project deals with the study of new instrumental approaches for quantitative assessment of biomechanical risk in occupational field. Worker health is an issue of fundamental importance in the ergonomic field: it is a duty to protect and guarantee the health of workers, in particular those exposed to occupational risk factors. In this context, of particular interest are the work-related musculoskeletal disorders (WMSDs) which represent the most common disorder in the occupational field and the main cause of absence from work in the industrialized world. During my PhD work I focused on two main groups of WMSDs: work-related low-back disorders (WLBDs), mainly caused by manual lifting tasks, and work-related neck and upper limb disorders (WRNULDs), mainly caused by computer use and use of touch screen devices that require static neck and shoulder posture or forward head posture. Previously, in the attempt to reduce the risk of WMSDs several methods have been developed, accepted by the international literature and used in the workplace. Specifically, regarding WLBDs, the National Institute for Occupational Safety and Health (NIOSH) published the Revised NIOSH Lifting Equation (RNLE), an approach widely used throughout the world to assess two-handed manual lifting demands but cannot be used in all work conditions. Regarding WRNULDs, there are few quantitative studies for work activities associated with use of computer and touch screen devices. Indeed, the majority of studies is founded on the questionnaire to evaluate self- reported pain, discomfort at the neck, at the shoulder and at the upper extremity and muscle fatigue due to the daily use of computer and touch screen devices. Therefore, the risk assessment methods currently used for WLBDs and WRNULDs have different limitations that inhibit their applicability to all work activities; hence the idea of using an instrumental and quantitative approach to evaluate the biomechanical risk in any work environment improving the risk assessment, adapting it to all the work conditions and overcoming the limits of the current standardized methods. Particularly, it is useful to introduce quantitative indices related to biomechanical risk because when the risk factors are analysed by means of quantitative methods, the possibility of identifying the relationship between pathologies and risk increases significantly. Furthermore, thanks to the technological advances that have allowed the development of miniaturized and non-invasive devices, such as the sEMG and wearable sensors, it will be possible to provide a method of assessing the biomechanical commitment applicable directly in the field without interfering with the work activity. Indeed, these devices can be performed both in the laboratory and in the workplace allowing the estimation of biomechanical risk in real-time providing a direct feedback to the end-user who would be constantly monitored directly while at work. That quantitative approach could allow to prevent and reduce the onset of these disorders but also the reintegration of workers affected by these and other disorders. Therefore, I tried to introduce quantitative approaches to evaluate biomechanical risk of certain work activities estimating the biomechanical commitment required by these activities with a computerized multifactorial motion analysis system (kinematics, kinetics and surface electromyography) and with new methodologic approaches to analyse muscle activity of weak and noisy myoelectric signals. Thesis’ results show that these instrumental approaches could be used to classify the risk. Particularly, regarding WLBDs, this thesis dealt with biomechanical risk assessment during lifting tasks, showing that kinematic features (i.e. lifitng energy consumption or jerk) and time and frequency sEMG features (max, average rectified value, mean and median frequency) have been seen significantly change in relation to the risk levels during these activities and they also correlate with spinal load variables (force and moment) in the L5-S1 region. Furthermore, the erector spinae longissimus was identified as the most sensitive trunk muscle with respect to changes in the lifting conditions. Additionally, these kinematic and sEMG features have been used as input variables of artificial neural networks for the prediction of WLBDs during lifting tasks. This approach has been proved to be able to improve the biomechanical risk estimation suggesting that an IMU/Inertial sensor or sEMG based lifting recognition tool using these features and designed according to the revised RNLE lends itself to the estimation of biomechanical risk. These instrumental methods could be integrated with methods already used for biomechanical risk assessment (i.e. NIOSH protocol) or used when the standardized methods cannot be used due to the equation and parameters restrictions. Moreover, my thesis’ work also dealt with weak and noisy signals allowing to quantify the muscle activity during some typical work activities that cause WRNULDs (i.e. use of computer and mobile touch screen devices by office workers). Indeed, the muscles involved in these work activities are often difficult to analyse being the signals produced by these muscles weak and noisy myoelectric signals which are characterized by low amplitudes, low firing rate, low number of recruited motor units and low signal to noise ratio. Two methods tested on synthetic and semisynthetic signals were developed so highlighting the possibility to identify the muscle activation also in these conditions evaluating biomechanically some work activities that aren’t evaluable with classical methods. Therefore, the use of new innovative technologies for biomechanical risk assessment is only at its initial stage, but this process seems to be unstoppable, as it is happening in all the other areas of medicine and beyond. Obviously, it will be necessary for any validation to follow evidence-based medicine/policy/legislation multistep scientific approaches by designing rigorous laboratory and epidemiologic studies, by replicating them by independent research groups and by systematically evaluating them through transparent review processes. I am however convinced that, even if such use should fail in ergonomic practice, the huge knowledge that will derive from its experimentation will allow the optimization of the current standardized methods or the developments of the new ones.