AI Article Synopsis
- A study aimed to create an unobtrusive freezing of gait (FOG) detection system for patients with Parkinson's disease to help reduce fall risks.
- A sole-mounted sensor was tested on twenty patients, utilizing a new time-domain method that was significantly less computationally intensive than traditional methods, while still delivering comparable detection performance.
- The findings suggest that this new system, optimized with 3D sensor data and specific time windows, could be beneficial for practical clinical use.
Article Abstract
Purpose: Freezing of gait (FOG), increasing the fall risk and limiting the quality of life, is common at the advanced stage of Parkinson's disease, typically in old ages. A simple and unobtrusive FOG detection system with a small calculation load would make a fast presentation of on-demand cueing possible. The purpose of this study was to find a practical FOG detection system.
Patients And Methods: A sole-mounted sensor system was developed for an unobtrusive measurement of acceleration during gait. Twenty patients with Parkinson's disease participated in this study. A simple and fast time-domain method for the FOG detection was suggested and compared with the conventional frequency-domain method. The parameters used in the FOG detection were optimized for each patient.
Results: The calculation load was 1,154 times less in the time-domain method than the conventional method, and the FOG detection performance was comparable between the two domains (P=0.79) and depended on the window length (P<0.01) and dimension of sensor information (P=0.03).
Conclusion: A minimally constraining sole-mounted sensor system was developed, and the suggested time-domain method showed comparable FOG detection performance to that of the conventional frequency-domain method. Three-dimensional sensor information and 3-4-second window length were desirable. The suggested system is expected to have more practical clinical applications.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4199977 | PMC |
| http://dx.doi.org/10.2147/CIA.S69773 | DOI Listing |
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