Multiple sclerosis (MS) is the most common cause of neurological disability in young and middle-aged people. MS has a physical, psychological and financial impact on patients and their families. Up to 85 % of patients with MS identify gait disorders as a major problem. The ability to monitor the development of the disorder over time is highly valued diagnostic measure. Falling because of old age, neurological disorders, movement disorders and injuries can be predicted by the assessment of change in gait quality.
A set of wearable sensors and the first computing unit – assessing the overall gait quality in real time while walking, providing immediate feedback to the user or the physician. Subsequently, the second unit identifies several gait disorders, their extent and probable cause. In both cases, the evaluation is performed using machine learning modules. Both approaches show relatively great robustness of the approach used and the relative simplicity of computer performance, especially in the near future. For general use the first step processing can warn patients or elderly people on the probability of falling.
Worldwide increasing incidence of MS was estimated to be 2.8 million people in 2020, in developed countries is double to triple incidence ‑ it might be due to lack of qualified diagnostics available in the developing countries ‑ the numbers are therefore supposedly undervalued. Our device can be helpful for many other neurological or movement diseases and in elderly in general.
Possible uses:
1. Non-MD/Diag. device – wearable “fall prediction”
2. Diagnostic – wearable – good/bad gait indicator
3. Diagnostic – wearable + mobile/tablet - gait disorder type and severity analyser and classifier.
The development was supported by the Technology Agency of the Czech Republic within the programme for applied research, experimental development and innovationGAMA 2 –Project TP01010034 – Centre for Transfer of Biomedical Technologies – PoC 2.
Technology is owned by University Hospital Hradec Králové. Czech patent application PV 2021-331 and PCTapplication PCT/CZ2022/050062 (priority 30. 06. 2022) was submitted. Main goal is to launch the technology to the market as medical device. The ability of the device to objectively classify gait disorders and then follow their progress in time might significantly chapen and objectify clinical studies. For the initial market entry, non-medical device that would for example warn (via mobile phone) seniors about rapid deterioration of gait quality and thus increased risk of fall, might seem to be the best option. Technology went through Proof of Concept phase and Centre for Transfer of Biomedical Technologies is now currently seeking for CEO of new med tech startup.
Technology was awarded with Czech Digi@Med Award (Roche) and supported by Caelestinus, hybrid nine-month digital healt interoperability and FHIR incubator based in Prague.
Intellectual property status
Patent already applied for
Patent application number : PV 2021-331 & PCT/CZ2022/050062
Where : Czech republic & International patent application
Current development status
Laboratory prototypes
Desired business relationship
Patent licensing - searching for CEO of new med tech startup
University Hospital Hradec Králové is now one of the largest medical facilities not only in Eastern Bohemia but also throughout the Czech Republic. It is a state-of-the-art facility spanning a wide range of medical fields and serves as a catchment center for approximately 1,000,000 inhabitants from all over the Czech Republic.
More than 41,000 patients are hospitalized every year at the hospital’s 39 workplaces, which include 24 departments and over 1370 beds, with approximately 715,000 outpatients treated annually. The most complex surgical procedures are performed and modern diagnostic and treatment technologies are used. This makes University Hospital Hradec Králové comparable with similar hospitals in Europe.
University Hospital is an important research and educational institution closely connected with the Faculty of Medicine in Hradec Králové.
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