A knee rehabilitation device called Slider will soon track patients’ movements on any surface—bed, carpet, or floor—without needing a special mat. The problem is practical. Slider currently uses an optical tracker that only works accurately on a dedicated mat. This limits where patients can use it, especially at home. The project, led by AI Rehab with the National Physics Laboratory and the Newton Gateway to Mathematics, will swap the optical sensor for a combined accelerometer and optical system, then write new algorithms to fuse the data. The goal is accurate, surface-agnostic tracking. If successful, the device becomes cheaper to ship (no bulky mat), simpler for elderly or mobility-impaired patients to set up, and usable in more healthcare settings—hospital bedsides, living rooms, care homes. That matters for remote rehabilitation after knee surgery or for managing chronic knee conditions. The project does not break new fundamental science; it is an engineering refinement of an existing product. But the collaboration with NPL on precision measurement and with mathematicians on data analysis should produce a robust, clinically reliable system that could make home-based physiotherapy more accessible and effective.
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AI Rehab, in collaboration with the National Physics Laboratory (NPL) and the Newton Gateway to Mathematics, is developing a solution to enhance the surface-agnostic movement tracking capabilities of Slider, a rehabilitation device for knee patients. The project aims to overcome the current limitation of Slider's optical tracker, which requires a specific surface for accurate tracking in the horizontal plane. The proposed solution involves improving the sensors of the Slider and developing an algorithm to optimise sensor performance. This change will enable accurate movement tracking on any surface, such as a patient's bed or floor, without the need for a specialised mat. Project objectives: 1\. Changing Slider's sensors 2\. Developing new algorithms. An algorithm is a set of steps that are followed one by one to get the answer to a problem. It is important to develop good algorithms to let the computer chips know exactly what to do. 3\. Improving accuracy 4\. Optimising the system for long-term use. This enhancement will significantly improve Slider's versatility, user experience, and cost-effectiveness. The project will reduce production and shipping costs, simplify the user experience (especially for elderly or mobility-impaired individuals), and expand Slider's applicability in diverse healthcare settings. The collaboration leverages NPL's expertise in precision measurement and the Newton Gateway to Mathematics' proficiency in data analysis and mathematical modelling. This interdisciplinary approach ensures the development of a robust, accurate, and reliable solution that meets the high standards required for medical rehabilitation devices. Successful implementation will not only enhance Slider's competitiveness in the rehabilitation market but also contribute to advancing remote patient monitoring and home-based rehabilitation technologies, ultimately leading to better outcomes for patients recovering from knee surgeries or managing chronic knee conditions.
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