Darius Nahavandi

Ergonomic effects of using Lift Augmentation Devices in mining activities

The mining industry has previously been regarded as a high risk job with safety being the primary concern. Over the years, procedures have been enforced in order to reduce these risks however muscular injuries are still occurring at a significant rate. An assistive technology known as Lift Augmentation Device(LAD) has been in use to reduce the impact on a workers body. This paper provides a musculoskeletal analysis on shoulder and core muscles. Results indicate key differences between manual procedure and LAD-assisted procedures. The LAD-assisted procedure lessened the stretching force on the right shoulder and back muscles at the price of more oscillations in the force applied, while the left shoulder and core muscles suffered more stretching forces and more oscillations in the force applied. Improvements, to be made within the system, are provided.

Passive muscle force analysis during vehicle access: A gender comparison

Ensuring customer satisfaction within the automotive industry is a top priority. Primary concerns of satisfaction revolve around perceived comfort of entering and exiting vehicles. The ease of this task is attributed mostly to the design of the vehicles door frame however these are not tailored towards a specific gender. In this paper we present a biomechanical analysis-based gender assessment during entering and exiting a vehicle. The proposed method of analysis provides an assessment that can be used to predict differences between genders. The trials conducted in this study used ten subjects entering a common family vehicle. The discomfort measure based on the normalised muscle forces relies on biomechanical analysis of posture sequences entering and exiting the vehicles.

Skeleton-free task-specific rapid upper limb ergonomie assessment using depth imaging sensors

Rapid upper body assessment (RULA) is, undoubtedly, one of the most frequently used ergonomic scoring sheets in different industries. The scoring is based on measuring joint angles during assembly tasks. However, the process is labour intensive and requires ergonomists to study hundreds of hours of videos to identify musculoskeletal disorders associated with different assembly tasks. In this paper we propose automating the process using depth imaging sensors such as Kinect and a random decision forest (RDF). The main contribution in this paper is relaxing the need to construct a skeleton and train the RDF on the whole posture at once at the price of training different RDFs for different tasks. The accuracy of the proposed method converges to 95%.