Occupationally-related low back disorders (LBDs) continue to be the leading cause of lost work days and the most costly occupational safety and health problem facing industry today. It has been well established that most occupationally-related LBD risk is associated with manual materials handling activities as well as psychosocial influences in the workplace (National Academy of Sciences, 2001). In addition, individual factors can influence risk. However, our ability to characterize risk associated with these various dimensions of LBD risk has been rather poorly understood. Recent literature indicates that a common link within each of these risk dimensions involves increases in trunk muscle coactivation that can lead to increased spine loading and subsequent LBD. Electromyographic (EMG)-assisted models provide the only means to accurately assess and quantify the effect of changes in trunk muscle coactivation upon spinal loading. However, the collection of EMG under most industrial conditions is impractical. The objective of this work is to develop a Spine Loading Assessment System (SLAS) that has the capacity to assess trunk muscle coactivation patterns and subsequent spine loading in response to multiple risk dimensions. This system would permit one to accurately estimate spine loading as a result of physical workplace factors, psychosocial factors, and individual factors but would not require the use of EMG. This objective will be achieved through the development of a Hybrid Neuro-Fuzzy Engine (HNFE). This engine would act as a system artificial """"""""brain"""""""" able to synthesize information about the workplace and assess how the trunk musculature would behave. The engine will interface with a well-developed biologically-driven dynamic biomechanical model of the trunk. In this manner, we will be able to accurately predict spine loading in the workplace in response to various risk factor dimensions without the need to collect EMG data in the workplace. Collectively, the SLAS will have several benefits. First, it can be used to assess the risk of spine structure damage believed to contribute to low back pain as a function of work dimensions commonly associated with the workplace. Hence, this model will have immediate applications to workplace designs. Second, the system will provide insights as to how the various dimensions of risk synergistically impact the musculoskeletal system. Finally, it will facilitate further investigations regarding stability and coactivity.
