Automatic Posture and Balance Support for Supernumerary Robotic Limbs

Lead Research Organisation: Queen Mary University of London
Department Name: Sch of Electronic Eng & Computer Science

Abstract

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.

Planned Impact

The primary goal of this project is to develop and validate novel user-centred devices and control methods to enable safe, comfortable and efficient material handling tasks by workers assisted by wearable supernumerary robotic limbs. The project will have wide impacts across manufacturing and logistics industries, robotics and ergonomics research and development. The assistive robotic system proposed in the project will improve the operator's working conditions with long-term health benefits. The quality of material handling operations will be improved as well, leading to increased productivity and reduced costs. Impact beneficiaries include industry workers performing material handling tasks; industries such as manufacturing and logistics; policymakers in industrial automation; society and healthcare providers; the general public; researchers in assistive robotics and automation.
Workers in industry. The proposed ergonomic support system for supernumerary robotic limbs will monitor and adjust the workers' posture 1) to maintain their physical well-being and 2) to reduce their workload and fatigue.

Businesses. Business in which complete automation is not possible due to the complexity of the tasks and/or high costs of autonomous robots will benefit by introducing the resultant robotic assistance system. The proposed system will improve productivity by helping to handle larger and heavier materials with less effort, time, cost and reduced risk to health and safety.

Healthcare providers. The proposed system will improve the working conditions of workers. It will reduce risks of work-related accidents and provide long term ergonomic support to maintain a healthy posture. Healthcare providers will benefit from the long-term advantages of using the resultant assistive robotics system. The risk of development of medical disorders such as back pain will be reduced.

Local communities and society in general. The proposed ergonomics assistance system will benefit workers of older age. Currently, many older workers in industry risk losing their jobs because it might get more difficult and tiring for their bodies to perform physical tasks. The posture and balance support system created in the project will help workers of older age to perform their tasks efficiently, and therefore it will support their employment and provide stable financial income.

Policymakers. The adoption of assistive robotics and technologies in industry is an important topic for business and economy policymakers in the UK. This project will demonstrate the advantages of such technologies and bring forward a specific application case of assisted material handling.

General public. The interest of the general public in modern technologies including robotics is growing rapidly. This project will serve as an excellent example of how assistive robots can be beneficial to businesses and workers to perform their jobs more efficiently, safer and healthier. Demonstrations of the proposed system will motivate the interest of young people in studying and working with advanced research and engineering.

Researchers in assistive robotics and automation will be able to learn from the project. The project will contribute to several challenging areas of study in human-robot interaction, robot control and design, and ergonomics. A new research topic - ergonomics of supernumerary robotic limbs will be established. Novel techniques and evaluation methods will be created for supporting healthy posture, stability and efficiency in material handling tasks assisted with supernumerary robotic limbs. The results of the project will be made available through open access publications, datasets and media reports.

Publications

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Abeywardena S (2022) Human balance augmentation via a supernumerary robotic tail. in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

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Abeywardena S (2023) Mechanical characterisation of supernumerary robotic tails for human balance augmentation in Journal of Mechanisms and Robotics

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Farkhatdinov I (2022) Evaluation of a Portable fMRI Compatible Robotic Wrist Interface. in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

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Huang HY (2021) Cable-Driven Robotic Interface for Lower Limb Neuromechanics Identification. in IEEE transactions on bio-medical engineering

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Perez NP (2022) Lateralization of Impedance Control in Dynamic Versus Static Bimanual Tasks. in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

 
Description Novel experimental data explaining how humans adjust their posture and balance during material handling demonstrated the importance of effort distribution in the body.
A new type of measuring technique was validated experimentally to estimate human centre of mass movement during material handling.
The proposed measuring technique can be integrated with balancing augmentation devices like exoskeletons or robotic tails.
Mathematical models for different kinematic configurations of a robotic tail were derived and used to analyse the balance augmentation capabilities of the tails
A robotic tail with two revolute joints are more efficient for balance augmentation when compared to tails with combination of revolute and prismatic joints.
Mechanism design prototypes for robotic tails were developed.
The proposed robotic tail can augment human balancing and support posture during material handling tasks.
Exploitation Route Academic route: understanding how human posture and balance is affected during material handling tasks and how a robotic tail can be used to augment human balance. These are novel results useful for academics and researchers in the field of human-robot interaction, gait and posture and wearable systems. The mathematical models of the robotic tails can provide a tool to explore how animals with tails balance their bodies, that is relevant to researchers in biology.

Non-academic route: a hand-held device for measuring centre-of-mass movement during material handling was designed. The device has potential to be used/integrated in industries where people are required to handle heavy/bulky materials, i.e. warehouses, logistics. The device can record load characteristics and estimate potential negative affects on user ergonomics.
Sectors Construction,Healthcare,Manufacturing, including Industrial Biotechology,Transport

 
Description MuMoHi - Multi-modal haptic interface for extended reality and robotics
Amount £10,000 (GBP)
Organisation Queen Mary University of London 
Sector Academic/University
Country United Kingdom
Start 08/2022 
End 03/2023
 
Description Design of a wearable robotic tail for balancing 
Organisation Shadow Robot Company
Country United Kingdom 
Sector Private 
PI Contribution Project meeting has been organised to discuss the design and development progress of the wearable robotic tail.
Collaborator Contribution Shadow robot provides advice on robot design and system integration.
Impact The plans for wearable robotic tail design have been identified.
Start Year 2021
 
Description Design of experimental task to evaluate the wearable robot for material handling 
Organisation Ocado Technology
Country United Kingdom 
Sector Private 
PI Contribution We collaborate on designing experimental task for material handling relevant to realistic industrial environments.
Collaborator Contribution Ocado Technology proposed several possible tasks for material handling operations which were integrated in the experimental design.
Impact Experimental protocol and design.
Start Year 2021
 
Description Experimental investigation of neuromotor control in upper limb with Imperial College London 
Organisation Imperial College London
Department Department of Bioengineering
Country United Kingdom 
Sector Academic/University 
PI Contribution We collaboratively design and conduct an experimental study to explore redundancy in muscle activation of upper limbs in humans during material handling task.
Collaborator Contribution Imperial College London (Human Robotics group) provides support on experimental design.
Impact - experimental protocol for the study
Start Year 2021