# Applying Wearable Technology and a Deep Learning Model to Predict Occupational Physical Activities
###### Abstract  
Many workers who engage in manual material handling (MMH) jobs experience high physical demands that are associated with work-related musculoskeletal disorders (WMSDs). Quantifying the physical demands of a job is important for identifying high risk jobs and is a legal requirement in the United States for hiring and return to work following injury. Currently, most physical demand analyses (PDAs) are performed by experts using observational and semi-quantitative methods. The lack of accuracy and reliability of these methods can be problematic, particularly when identifying restrictions during the return-to-work process. Further, when a worker does return-to-work on modified duty, there is no way to track compliance to work restrictions conflating the effectiveness of the work restrictions versus adherence to them. To address this, we applied a deep learning model to data from eight inertial measurement units (IMUs) to predict 15 occupational physical activities. Overall, a 95% accuracy was reached for predicting isolated occupational physical activities. However, when applied to more complex tasks that combined occupational physical activities (OPAs), accuracy varied widely (0-95%). More work is needed to accurately predict OPAs when combined into simulated work tasks. occupational physical demands; deep learning; wearable devices; musculoskeletal disorders +
Footnote †: journal: Appl. Sci. written in compliance with the American Disabilities Act. The PDA includes information on the approximate duration (% of day) and magnitude (load) that different occupational physical activities (OPAs), such as lifting/lowering, carrying, kneeling, reaching, walking, and standing, take place. The PDA informs workers about the physical demands of a job before they are hired and it provides occupational health practitioners critical information for facilitating effective return to work programs should an injury occur. Various methods for quantifying OPAs include self-report, observational, and direct measurement [8; 9]. Though both self-report and observational approaches are low-cost and convenient, results can be inaccurate and unreliable, as well as time consuming [5]. Although observational methods are the most common approach for estimating OPAs [8], some have used more detailed video-based techniques to quantify the duration of OPAs with higher accuracy. Essentially, workers are recorded in real-time and the video is analyzed using computer software that aggregates the amount of time spent performing different OPAs. Direct measurements of force supplement the video analysis to provide a more valid and reliable PDA; however, this approach is extremely time consuming and costly [8]. Accurate and reliable quantification of OPAs required for a job description is critical for physically demanding jobs. Prospective workers rely on them to determine if they would like to pursue a job and clinicians rely on them to facilitate appropriate return to work plans. Further, accurate and reliable quantification of the duration, frequency and magnitude of physical demands can be useful when assessing interventions designed to reduce physical exposures associated with WMSDs. To overcome the limitations of self-report, observational, and direct measurement methods, kinematic data could be an effective approach to quantifying OPAs in the workplace. Kinematic data yield the position, velocity, and acceleration of body segments and have been used to predict the patterns and quality of movement [10]. Conventional methods of capturing kinematic data rely principally on video analysis or an optoelectronic system to distinguish movement patterns of body segments [11; 12; 13; 14]. However, these laboratory methods have limitations in real work scenarios both in cost and feasibility. Wearable technology, such as inertial measurement units (IMUs), have been used to capture human body motion for animation, optimizing athletic performance, and even optimizing patient treatment [15]. For example, [PERSON] et al.