Periodic Reporting for period 2 - SocketSense (Advanced sensor-based design and development of wearable prosthetic socket for amputees)
Reporting period: 2021-01-01 to 2022-12-31
The H2020 project SocketSense aims at developing an innovative prosthetic socket system with integration of advanced sensing, data analysis, AI methods, embedded edge and cloud computing. It is motivated by the fact that limb amputations often cause serious physical disabilities and thereby compromise the quality of life of many people around the globe. The World Health Organization estimates that there are ~40 million amputees in the world. One common reason for limb loss is diabetes. One undesired current situation is that many amputees do not wear their prostheses because of discomfort or residual limb pain. Consequently, their overall quality of life is often negatively affected. For example, people with disabilities are at a higher risk of unemployment as they may be more vulnerable and usually meet more barriers in finding new jobs. The lack of employment significantly affects the perceived social exclusion index. The levels of social exclusion are the highest among the long-term unemployed and those unable to work for reasons of poor health or disability.
The key cutting-edge technology of SocketSense is given by the support for effective monitoring of dynamic operational conditions within prosthetic sockets that are traditionally not directly observable. The measurement values are evaluated against biomechanical models based on the residual limb tissue properties of individual prosthetic users. The result of that comparison may then indicate that the socket fit is non-optimal and that fabricating a better fitting socket may be necessary. See the Figure 1 for the overall system architecture of the SocketSense system, and Figure 2 visual of a QTSSTM single multi-point pressure sensing strip and a shear/loss of friction sensor. With SocketSense, the prosthetists will be able to achieve an optimized socket and the technique applies to both above knee and below knee amputees.
The key cutting-edge technology of SocketSense is given by the support for effective monitoring of dynamic operational conditions within prosthetic sockets that are traditionally not directly observable. The measurement values are evaluated against biomechanical models based on the residual limb tissue properties of individual prosthetic users. The result of that comparison may then indicate that the socket fit is non-optimal and that fabricating a better fitting socket may be necessary. See the Figure 1 for the overall system architecture of the SocketSense system, and Figure 2 visual of a QTSSTM single multi-point pressure sensing strip and a shear/loss of friction sensor. With SocketSense, the prosthetists will be able to achieve an optimized socket and the technique applies to both above knee and below knee amputees.
The work included problem definition, investigation of candidate methods and technologies, system architecture design, sensor design and optimization, the establishment of an embedded system platform for data processing and communication, and finally, the clinical investigation trials, which entailed data collection and analysis, system validation and dissemination and exploitation.
The successful results achieved include the design of intra-socket light-weight, printed, flexible, wearable multi-point pressure and shear/loss of friction sensors for the measurement of dynamic load conditions, and the accomplishment of the prototype system integrating the sensors, sensor electronics, firmware, and application software, as described below.
- On the sensor system development: The final choice for optimal pressure sensor strip and shear/loss of friction sensor design were evaluated by extensive lab testing and performance evaluation. Meanwhile, the electronic system for sensor data acquisition, processing and communication was optimized following pilot tests under real-use conditions. This resulted in a flexible, retrofittable, wearable sensor system that is untethered and capable of global mapping of intra-socket pressures and other relevant data measurements.
- On the data analysis: The data acquisition system was developed to collect QTSS pressure and shear sensor information via Analog-to-Digital Converter as voltage outputs. The work on data analysis emphasized sensor behaviour modelling, characterization and utilizations of data features and identification of models and algorithms for data treatment. The initial design for smart sensor data analysis includes MLP (Multi-Layer Perceptron) and HMM (Hidden Markov Model) for sensor and biomechanical modelling. It creates basic understanding of intra-socket dynamic operational conditions. To allow (re)generating the intra-socket pressure conditions without direct clinical trials, the OpenSim tool was used to capture the overall operational conditions of amputee behaviour. AnsysNSYS was then employed to elicit the pressure distribution inside socket using Finite Element Analysis (FEA) in simulated static and dynamic test conditions. To support the FEA model, the same test conditions and the sensors developed were deployed in a model socket and tested in a Stewart Manipulator.
- On the comfort assessment and socket optimization: A protocol for soft tissue measurement and comparison with a simulation-based method was established in Abaqus to evaluate the interaction between the residual limb and the socket. See Figure 3, below. The work on virtual socket generation was finished. A Decision Support System (DSS) based on a fuzzy-logic inference engine (IE) to support prosthetists in socket rectification decisions was achieved.
- On the overall system integration: A reference scheme for sensor deployment inside prosthetic sockets was developed, allowing the mapping of measurement data in both 2D and 3D representations. The design of a new data management platform for clinicians was also refined. The key features of the SocketSense data management system include a backend for data computation and storage, and a frontend system for the visualization of 3D pressure distributions and data presentation.
On the clinical trial investigation: The clinical investigation was designed, and the protocol to be conducted during the trials was defined. Various other documentation required by the medical devices and health regulatory agencies in the UK and in Spain were also completed. The clinical investigation trials were conducted in the UK and Spain, upon which data analysis and system validation and verification were successfully executed.
Dissemination and exploitation of results was on-going throughout the project. The partners have produced a business plan for the exploitation of the successfully validated TRL5 device, generated numerous publications, attended and presented at a number of conferences and produced significant open access data.
The successful results achieved include the design of intra-socket light-weight, printed, flexible, wearable multi-point pressure and shear/loss of friction sensors for the measurement of dynamic load conditions, and the accomplishment of the prototype system integrating the sensors, sensor electronics, firmware, and application software, as described below.
- On the sensor system development: The final choice for optimal pressure sensor strip and shear/loss of friction sensor design were evaluated by extensive lab testing and performance evaluation. Meanwhile, the electronic system for sensor data acquisition, processing and communication was optimized following pilot tests under real-use conditions. This resulted in a flexible, retrofittable, wearable sensor system that is untethered and capable of global mapping of intra-socket pressures and other relevant data measurements.
- On the data analysis: The data acquisition system was developed to collect QTSS pressure and shear sensor information via Analog-to-Digital Converter as voltage outputs. The work on data analysis emphasized sensor behaviour modelling, characterization and utilizations of data features and identification of models and algorithms for data treatment. The initial design for smart sensor data analysis includes MLP (Multi-Layer Perceptron) and HMM (Hidden Markov Model) for sensor and biomechanical modelling. It creates basic understanding of intra-socket dynamic operational conditions. To allow (re)generating the intra-socket pressure conditions without direct clinical trials, the OpenSim tool was used to capture the overall operational conditions of amputee behaviour. AnsysNSYS was then employed to elicit the pressure distribution inside socket using Finite Element Analysis (FEA) in simulated static and dynamic test conditions. To support the FEA model, the same test conditions and the sensors developed were deployed in a model socket and tested in a Stewart Manipulator.
- On the comfort assessment and socket optimization: A protocol for soft tissue measurement and comparison with a simulation-based method was established in Abaqus to evaluate the interaction between the residual limb and the socket. See Figure 3, below. The work on virtual socket generation was finished. A Decision Support System (DSS) based on a fuzzy-logic inference engine (IE) to support prosthetists in socket rectification decisions was achieved.
- On the overall system integration: A reference scheme for sensor deployment inside prosthetic sockets was developed, allowing the mapping of measurement data in both 2D and 3D representations. The design of a new data management platform for clinicians was also refined. The key features of the SocketSense data management system include a backend for data computation and storage, and a frontend system for the visualization of 3D pressure distributions and data presentation.
On the clinical trial investigation: The clinical investigation was designed, and the protocol to be conducted during the trials was defined. Various other documentation required by the medical devices and health regulatory agencies in the UK and in Spain were also completed. The clinical investigation trials were conducted in the UK and Spain, upon which data analysis and system validation and verification were successfully executed.
Dissemination and exploitation of results was on-going throughout the project. The partners have produced a business plan for the exploitation of the successfully validated TRL5 device, generated numerous publications, attended and presented at a number of conferences and produced significant open access data.
The approach taken by SocketSense is highly multidisciplinary, characterized by the integration of a wide range of topics beyond state-of-the-art, relating to
- Novel, affordable, wearable, safe ultra-low voltage operation, printed, flexible pressure and shear/loss of friction sensors that are retrofittable into prosthetic sockets and suitable for mass manufacture and Large Area Electronic (LAE) fabrication whilst being mindful of recyclability and sustainability.
- Statistical analysis and AI based estimation of intra-socket conditions that are inherently stochastic and only partially observable.
- Advanced biomechanical models for limb diagnostics and prognostics, and socket design optimization.
- Integrated edge - and cloud-based software services for effective data processing and communication.
- Clinical data collection and IoT system validation.
- Novel, affordable, wearable, safe ultra-low voltage operation, printed, flexible pressure and shear/loss of friction sensors that are retrofittable into prosthetic sockets and suitable for mass manufacture and Large Area Electronic (LAE) fabrication whilst being mindful of recyclability and sustainability.
- Statistical analysis and AI based estimation of intra-socket conditions that are inherently stochastic and only partially observable.
- Advanced biomechanical models for limb diagnostics and prognostics, and socket design optimization.
- Integrated edge - and cloud-based software services for effective data processing and communication.
- Clinical data collection and IoT system validation.