Open Cut » Health and Safety
This project has developed wearable sensors that obtain musculoskeletal data from workers, analyse tasks and generate scored manual handling risk assessments. The sensors deliver data to a simple-to-use App with the only task for the user being to start and stop analysing the activity. The App works on phones and tablets using a developed machine learning system to analyse data and present it on intuitive screens in simple report formats.
The sensors measure acceleration and orientation, and gather data that the App uses to compute the risk assessments. The risk assessments can be shown while the task is being performed when the app is connected to the sensors, allowing real time education and training based on the assessment. The sensors can also record data on their own, without the mobile device being present, allowing automated assessments to be performed after the task has ﬁnished.
Testing in the coal production environment has proved the system and feedback has been received from wearers of the sensors who report no interference with work and no discomfort. Post hoc replaying of video and risk assessment data has demonstrated the value of the system in worker engagement and manual handling solution development.
The risk assessment method employed by the application is based on and closely aligned to the Australian Model Code of Practice - Hazardous Manual Tasks 2016 and its predecessor codes, guides and national standard. This method has been extended to address a factor that limits application by both expert and casual users; the consequence scoring in the risk estimation calculation has been populated using extensive injury clinical and claims data. This standardised method results in the provision of ranked scores based on type of injury risk and enables use alongside other Work Health & Safety risk-scoring.