Application of Probabilistic Sensor Networks to Intent Detection in Prosthetics and Other Wearable Technology

Lead Research Organisation: University of Oxford
Department Name: Engineering Science


Project Context and Potential Impact
With over 60,000 patients with an amputation or congenital limb deficiency attending specialist rehabilitation service centres in the UK, prosthetic technology is a significant industry with the potential to impact a great many lives. However, it is clear that there is much room for improvement in prosthetic control. The typical, healthy human does not decide to activate each muscle individually in a sequence to perform an action - they simply "will" it, and the limb obeys automatically. In contrast, operating a prosthetic can be challenging, requiring considerable practice to perform any kind of complex operation, which is a limiting factor in prosthetic design and can contribute towards device abandonment. This project will aim to develop techniques to move away from "manual" prosthetic control to a computer-assisted system more reminiscent of healthy biological limb control.

Project Aims and Objectives
The objective of the research will be to design, develop and test a system for probabilistically combining readings from multiple sensor environments (such as EMG, motion-tracking, kinematic sensing, smart home and smart phone-based sensing) in order to produce a measure of user intent when operating a wearable device such as a prosthetic limb, which can then be used to make a decision as to how the device should respond. The goal is that this will be a "drop in/drop out" network, taking advantage of whatever sensors are available at any given time, focusing on low-cost, unobtrusive sensors. This concept will be proved over a number of "test scenarios" for specific, repeatable actions, and expanded into a set of general principles which can be applied over a wide range of situations.

Proposed Research Methodology
An extensive literature review has been carried out, assessing all existing intent-sensing research and determining the merit of all viable sensor methods that could be used in the network. Probabilistic sensor network techniques have been reviewed, carrying forward and expanding upon the results of my fourth year project to produce a set of theoretical models of probabilistic intent-sensing systems. Using the models, a method has been developed to provide a "sanity-check" for machine learning systems to identify inappropriate methods (published and presented at UEMCON 2019 and EMBC 2020).
These models and methods are being used to develop the intent-sensing algorithm and will be applied to test scenarios such as sitting down, beginning climbing stairs, and picking up a cup, which will initially be artificially simulated. Once the system is proven to work with the simulation, experimental data will be gathered from practical sensor networks to test it in the real world. Some of this will be gathered from healthy volunteers, and additional data may be provided by Blatchford (a leading UK prosthetics company) from patients, which can be used to verify the system and test how its success may vary from user to user. With the concept proven to work in multiple test scenarios, the procedure will be formalised into a general set of techniques which can be used for a wide range of applications by inputting various data parameters, employing machine learning techniques to train the system to associate sensor data with intent, regardless of the situation. This framework will be designed to make industrial application straightforward, and cooperation will take place with Blatchford to ensure the principles are viable in a product-focused environment.

Alignment to EPSRC's Strategies and Research Areas
This project falls within the EPSRC Engineering and Healthcare Technologies research areas, with potential applications not only in the fields of prosthetic control and patient rehabilitation, but also in the wider fields of wearable technology and human-machine interfacing as a whole.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R513295/1 01/10/2018 30/09/2023
2284234 Studentship EP/R513295/1 01/10/2019 31/03/2023 Joseph Russell