Project leader: , ,
Project members: , , , ,
Start date: 1. January 2018
End date: 31. December 2019
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
In cooperation with: UK Erlangen – Department for molecular neurology
The ability to walk defines human nature and limits the mobility, independence, and quality of life. Falls are the leading cause of both fatal and nonfatal injuries among older adults, causing severe injuries such as hip fractures, head trauma, and death. Increased fall risk is a key symptom in Parkinson’s disease (PD), limiting the independency and mobility of patients.
So far, no validated technical solutions exist to identify the individual’s rising fall risk before the first fall occurs. Therefore, we will investigate algorithms, that are able to predict the fall risk based on specific gait patterns, captured by shoe integrated inertial sensors. The data for the evaluation of fall risk associated gait patterns will be acquired by means of a continuous long-term monitoring system.
To ensure a successful progress of this project we will combine three strategies in the research and development phase:
- Usage of distinct sensors that enable gait assessment with high biomechanical resolution
- Development and evaluation of machine learning based gait pattern algorithms
- Digital biobanking of clinical distinct gait patterns to individualize fall risk monitoring.
The overall goal of the project is the investigation of novel machine learning based algorithms that enable the determination of PD patients’ fall risk using continuous gait data. Since existing algorithms and test procedures in related clinical research are typically limited to one-time assessments, we will investigate new algorithms for a continuous gait monitoring system, that will identify disease specific changes of gait with a high reliability.
At the same time, we will generate clinical understanding and validated data for individualized applicability that is required for medical product licensing, as well as economic effect sizes of technology application in healthcare strategies.
FallRiskPD: Long-term fall risk classification for Parkinson’s disease via intelligent sensor-based gait analysis in the home environment (Talk)
European Falls Festival 2018 (Manchester, 2. July 2018 - 3. July 2018)
In: European Falls Festival, 2nd and 3rd July 2018, Manchester, United Kingdom, ABSTRACT BOOKLET 2018
URL: http://eufallsfest.eu/documents/Abstract Booklet.pdf
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Unsupervised harmonic frequency-based gait sequence detection for Parkinson’s disease
IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) (Chicago, 19. May 2019 - 22. May 2019)
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Detection of Gait From Continuous Inertial Sensor Data Using Harmonic Frequencies
In: IEEE Journal of Biomedical and Health Informatics 24 (2020), p. 1869 - 1878
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Automatic clinical gait test detection from inertial sensor data
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (Montreal, 20. July 2020 - 24. July 2020)
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Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson’s disease patients
In: Journal of neuroEngineering and rehabilitation 18 (2021), Article No.: 93
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