Quantification of Arm Swing during Walking in Healthy Adults and Parkinson’s Disease Patients: Wearable Sensor-Based Algorithm Development and Validation

Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson’s disease (PwP)...

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Published in:Sensors (Basel, Switzerland) Vol. 20; no. 20; p. 5963
Main Authors: Warmerdam, Elke, Romijnders, Robbin, Welzel, Julius, Hansen, Clint, Schmidt, Gerhard, Maetzler, Walter
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 21.10.2020
MDPI
Subjects:
Adult
Aged
Algorithms
Arm
Asymmetry
Fitness equipment
Gait
gyroscope
Humans
inertial measurement unit
Letter
Male
Middle Aged
Parkinson Disease - diagnosis
Parkinson's disease
Principal components analysis
Sensors
Velocity
Walking
Wearable Electronic Devices
United States > US
inertial measurement unit
gait
Parkinson’s disease
gyroscope
ISSN:1424-8220, 1424-8220
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Abstract Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson’s disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from −0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson’s disease.
AbstractList Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson's disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from -0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson's disease.Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson's disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from -0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson's disease.
Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson’s disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from −0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson’s disease.
Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson's disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from -0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson's disease.
Author Warmerdam, Elke
Welzel, Julius
Schmidt, Gerhard
Maetzler, Walter
Hansen, Clint
Romijnders, Robbin
AuthorAffiliation 2 Faculty of Engineering, Kiel University, Kaiserstraße 2, 24143 Kiel, Germany; gus@tf.uni-kiel.de
1 Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany; r.romijnders@neurologie.uni-kiel.de (R.R.); j.welzel@neurologie.uni-kiel.de (J.W.); c.hansen@neurologie.uni-kiel.de (C.H.); w.maetzler@neurologie.uni-kiel.de (W.M.)
AuthorAffiliation_xml – name: 2 Faculty of Engineering, Kiel University, Kaiserstraße 2, 24143 Kiel, Germany; gus@tf.uni-kiel.de
– name: 1 Department of Neurology, Kiel University, Arnold-Heller-Straße 3, 24105 Kiel, Germany; r.romijnders@neurologie.uni-kiel.de (R.R.); j.welzel@neurologie.uni-kiel.de (J.W.); c.hansen@neurologie.uni-kiel.de (C.H.); w.maetzler@neurologie.uni-kiel.de (W.M.)
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  givenname: Walter
  surname: Maetzler
  fullname: Maetzler, Walter
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33096899$$D View this record in MEDLINE/PubMed
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Keywords inertial measurement unit
gait
Parkinson’s disease
gyroscope
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Snippet Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance....
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StartPage 5963
SubjectTerms Adult
Aged
Algorithms
Arm
Asymmetry
Fitness equipment
Gait
gyroscope
Humans
inertial measurement unit
Letter
Male
Middle Aged
Parkinson Disease - diagnosis
Parkinson's disease
Principal components analysis
Sensors
Velocity
Walking
Wearable Electronic Devices
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Title Quantification of Arm Swing during Walking in Healthy Adults and Parkinson’s Disease Patients: Wearable Sensor-Based Algorithm Development and Validation
URI https://www.ncbi.nlm.nih.gov/pubmed/33096899
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