Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 16004-16009 |
| Seitenumfang | 6 |
| Fachzeitschrift | IFAC-PapersOnLine |
| Jahrgang | 53 |
| Ausgabenummer | 2 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Extern publiziert | Ja |
| Veranstaltung | 21st IFAC World Congress 2020 - Berlin, Deutschland Dauer: 12 Juli 2020 → 17 Juli 2020 |
Abstract
Freezing of Gait (FoG) is one of the cardinal symptoms of Parkinson's disease, which arises in the late stages of the disease. It affects the gait cycle and increases the risk of falling. FoG leads to heterogeneous gait cycles, which makes the detection of gait phases and events difficult. In this article, we introduce a new inertial measurement unit-based approach for detecting Parkinsonian gait phases based on the acceleration, velocity, rate of turn and orientation of the foot. Furthermore, we introduce a new gait evaluation measurement, the so-called GaitScore, for distinguishing between normal and FoG-affected motion phases and thus for detecting FoG episodes. Preliminary results show that the extreme values of the pitch angle during a motion phase provide valuable information for the detection of FoG. The proposed method can detect FoG episodes with a sensitivity of 97% and specificity of 87%. The reference data were generated by clinical experts who annotated FoG episodes in video data synchronized with the measurements of the inertial sensors. The detection of FoG in real-time enables on-demand cueing.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
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in: IFAC-PapersOnLine, Jahrgang 53, Nr. 2, 2020, S. 16004-16009.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Inertial Sensor Based Detection of Freezing of Gait for On-Demand Cueing in Parkinson's Disease
AU - Dvorani, A.
AU - Jochner, M. C.E.
AU - Seel, T.
AU - Salchow-Hömmen, C.
AU - Meyer-Ohle, J.
AU - Wiesener, C.
AU - Voigt, H.
AU - Kühn, A.
AU - Wenger, N.
AU - Schauer, T.
PY - 2020
Y1 - 2020
N2 - Freezing of Gait (FoG) is one of the cardinal symptoms of Parkinson's disease, which arises in the late stages of the disease. It affects the gait cycle and increases the risk of falling. FoG leads to heterogeneous gait cycles, which makes the detection of gait phases and events difficult. In this article, we introduce a new inertial measurement unit-based approach for detecting Parkinsonian gait phases based on the acceleration, velocity, rate of turn and orientation of the foot. Furthermore, we introduce a new gait evaluation measurement, the so-called GaitScore, for distinguishing between normal and FoG-affected motion phases and thus for detecting FoG episodes. Preliminary results show that the extreme values of the pitch angle during a motion phase provide valuable information for the detection of FoG. The proposed method can detect FoG episodes with a sensitivity of 97% and specificity of 87%. The reference data were generated by clinical experts who annotated FoG episodes in video data synchronized with the measurements of the inertial sensors. The detection of FoG in real-time enables on-demand cueing.
AB - Freezing of Gait (FoG) is one of the cardinal symptoms of Parkinson's disease, which arises in the late stages of the disease. It affects the gait cycle and increases the risk of falling. FoG leads to heterogeneous gait cycles, which makes the detection of gait phases and events difficult. In this article, we introduce a new inertial measurement unit-based approach for detecting Parkinsonian gait phases based on the acceleration, velocity, rate of turn and orientation of the foot. Furthermore, we introduce a new gait evaluation measurement, the so-called GaitScore, for distinguishing between normal and FoG-affected motion phases and thus for detecting FoG episodes. Preliminary results show that the extreme values of the pitch angle during a motion phase provide valuable information for the detection of FoG. The proposed method can detect FoG episodes with a sensitivity of 97% and specificity of 87%. The reference data were generated by clinical experts who annotated FoG episodes in video data synchronized with the measurements of the inertial sensors. The detection of FoG in real-time enables on-demand cueing.
KW - Biomedical Systems
KW - Detection Algorithms
KW - Freezing of Gait
KW - Gait Analysis
KW - Inertial Measurement Unit
KW - On-Demand Cueing
KW - Parkinson's Disease
KW - Rehabilitation
UR - http://www.scopus.com/inward/record.url?scp=85119953441&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2020.12.400
DO - 10.1016/j.ifacol.2020.12.400
M3 - Conference article
AN - SCOPUS:85119953441
VL - 53
SP - 16004
EP - 16009
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 2
T2 - 21st IFAC World Congress 2020
Y2 - 12 July 2020 through 17 July 2020
ER -