The project investigates the potential of transforming traditional safety shoes into smart safety shoes to prevent occupational incidents in the construction and logistics sectors. By incorporating smart technology, these shoes aim to provide continuous, real-time, and personalised risk assessments, addressing the shortcomings of current safety programs. The project includes an extensive literature review and practical insights from semi-structured interviews and case studies. The design concept integrates sensors and machine learning to detect leading and lagging indicators of manual handling incidents. The smart shoes then communicate these insights to employees, supervisors, and training providers, fostering a hybrid system of reactive and proactive incident prevention. This innovative approach promises to enhance workplace safety significantly by supporting and improving existing safety measures.
Incorrect manual handling techniques in construction and logistics can lead to severe physical injuries, especially back disorders, significantly affecting employees' health and increasing costs for employers. Current safety programs, which rely on lagging indicators and periodic assessments, are insufficient for proactive incident prevention. There is a pressing need for a solution that can provide real-time, personalised feedback to workers to improve manual handling practices and reduce incidents.
The project uses advanced smart technology embedded in safety shoes aimed at the proactive prevention of manual handling incidents. Data is gathered through extensive literature reviews and series of semi-structured interviews. This phase is crucial for collecting insights on manual handling, workplace safety, behavioural patterns, and the potential integration of smart technologies in safety protocols.
The equipment development focuses on smart safety shoes equipped with advanced sensors. These sensors are crucial for real-time data gathering, particularly in monitoring manual handling activities that could lead to workplace injuries. The integration of data analysis tools, including machine learning algorithms, is essential in interpreting the collected data for predicting and thereby preventing potential incidents.
The analysis phase uses the data gathered to evaluate the current safety measures and their effectiveness in preventing incidents. This selection process is critical in identifying the most relevant data points that correlate directly with workplace safety outcomes. The aim is to refine the selection of data used to bolster the efficacy of existing safety protocols and suggest feasible improvements.
This subtopic involves a detailed comparison of traditional safety measures against the innovative approach of using smart technology embedded in safety footwear. The comparative analysis aims to highlight the benefits and potential improvements in incident prevention. By evaluating the impact of smart safety shoes, the project demonstrates the tangible benefits of adopting technology-driven solutions in improving overall workplace safety.
The research concludes that smart safety shoes are a promising solution to the inadequacies of current workplace safety programs. The integration of sensors and machine learning in safety shoes can provide real-time detection and feedback on manual handling techniques, addressing both leading and lagging indicators of incidents. The project proposes a concept design capable of significantly enhancing incident prevention through continuous, personalised risk assessments. This design supports current safety methods by providing actionable insights to various stakeholders, including employees, supervisors, and training providers. The implementation of such technology could mark a substantial step forward in occupational safety, reducing the incidence of manual handling injuries. However, further research and development are needed to refine the technology and explore its broader applications in different industries. The smart safety shoes not only present a viable immediate solution but also pave the way for future advancements in ubiquitous occupational safety systems.
