| EPSRC Reference: |
EP/T027746/1 |
| Title: |
Automatic Posture and Balance Support for Supernumerary Robotic Limbs |
| Principal Investigator: |
Farkhatdinov, Dr II |
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| Department: |
Sch of Electronic Eng & Computer Science |
| Organisation: |
Queen Mary University of London |
| Scheme: |
New Investigator Award |
| Starts: |
01 January 2021 |
Ends: |
31 December 2024 |
Value (£): |
373,313
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| EPSRC Research Topic Classifications: |
| Control Engineering |
Robotics & Autonomy |
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| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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It is well-known from the biomechanics and ergonomics research that material handling tasks in industry can often cause harmful working postures, potentially leading to musculoskeletal disorders and occupational injuries. Wearable robotic systems like supernumerary (additional) robotic limbs augment human bodies with extra mobility and manipulation capabilities, and they can increase the efficiency when conducting bulky material handling tasks and allow older workers to maintain their jobs. This project aims to create novel techniques to address ergonomics and safety of supernumerary robotic limbs. A novel posture and balance support wearable robotic system will be created and its control will be integrated with the supernumerary robotic limbs for material handling. The scope of the project is to study how the ergonomics of the supernumerary limbs for material handling can be improved through additional back and balance support. The implementation will be based on creating and using innovative mechatronic technologies (soft robotic actuation and sensing; light-weight cable-driven active mechanisms; haptic feedback; human-centred interactive control) and posture assessment and data processing methods (distributed wireless sensing; Cloud data storage; personalised machine-learning based data analysis and decision-making). The outcomes of the projects will have direct impacts on the UK manufacturing, logistics and agriculture industries (>15% of GDP, employing more than 10 million people), through development and evaluation of efficient and safe material handling robotic assistive technologies.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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