This project focuses on developing an innovative safety shoe equipped with sensors to monitor and prevent musculoskeletal disorders (MSDs), particularly low back pain (LBP), prevalent in sectors like warehousing and construction. By integrating pressure sensors within the shoe, the project aims to collect plantar pressure distribution (PPD) data, which can be analysed using machine learning to detect risky postures and provide feedback to the user. The initial phase involves extensive literature research on ergonomics and biomechanics, followed by the design and prototyping of a smart insole to validate the concept. This iterative process aims to prove the feasibility of using AI to analyse ergonomic data and develop a functional prototype that can potentially reduce work-related injuries.
In various working sectors, employees suffer from long-term physical problems due to incorrect postures, lifting heavy objects, or prolonged standing. These issues are challenging to track as each individual's circumstances differ, and collecting ergonomic data through observation is time-consuming and often incomplete. This project seeks to design a smart safety shoe that continuously collects data to analyse and prevent ergonomic issues without disrupting the workers' routine tasks.
The project uses pressure sensors integrated into safety shoes to collect plantar pressure distribution (PPD) data from workers. These sensors gathered information about various lifting postures, including stoop lifting, lifting above shoulder height, and asymmetrical lifting. Data collection was conducted through practical testing with prototypes, ensuring that the gathered data accurately represented the conditions under which workers operate. This hands-on approach allowed for real-time data collection, essential for analysing the ergonomic impact of different postures.
To facilitate the innovative data collection methods, the project involved developing a customised pressure insole. This insole, designed using 3D printing technology, housed the pressure sensors in a durable and flexible structure. The design underwent several iterations to ensure accuracy and comfort, with materials like TPU and PLA used to optimise sensor placement and data accuracy. The integration of these sensors into the shoes was crucial for collecting reliable data without hindering the workers' movements or comfort.
The project carefully selected the data necessary for analysing ergonomic risks associated with different lifting postures. This involved advising on using existing ergonomic knowledge and determining the most relevant and feasible data points to collect and analyse. The selection process was guided by the project's objectives to reduce musculoskeletal disorders by identifying high-risk postures.
Collected data was compared using machine learning algorithms to identify patterns and classify different lifting postures. The analysis focused on static postures initially, achieving high accuracy in classifying these postures. However, dynamic data analysis posed more challenges and required additional development. The use of AI facilitated the comparison of data, enabling automated detection of risky postures and providing feedback to users. This comparison was essential for validating the effectiveness of the smart safety shoes in real-world scenarios.
The research concludes that the smart safety shoe prototype successfully demonstrates the potential to detect different lifting postures through PPD data with high accuracy in static conditions. The machine learning model developed can classify static postures effectively, proving the core concept's viability. However, further research and development are needed to refine the hardware and software for dynamic data analysis and real-time feedback. The next steps involve enhancing sensor sensitivity, calibrating the sensors, and building a comprehensive posture database. With these improvements, the smart safety shoe can significantly impact workplace safety by reducing the risk of MSDs and associated costs. The project's success lays a solid foundation for future innovations in ergonomic safety footwear, promising better health outcomes for workers and economic benefits for employers.
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