Background Accurately measuring hypokinetic arm swing during free-living gait in Parkinson’s disease (PD) is challenging due to other concurrent arm activities. We developed a method to isolate gait segments without these arm activities. Methods Wrist accelerometer and gyroscope data were collected from 25 individuals with PD and 25 age-matched controls while performing unscripted activities in their home environment. This was done after overnight withdrawal of dopaminergic medication (‘pre-medication’) and approximately one hour after intake (‘post-medication’). Using video annotations as ground truth, we trained and evaluated two classifiers: one for detecting gait and one for detecting gait segments without other arm activities. Based on the filtered gait segments, arm swing was quantified using the median and 95th percentile range of motion (RoM). These arm swing parameters were evaluated in three ways: (1) the agreement between predicted and video-annotated gait segments without other arm activities, (2) the sensitivity to differences between PD and controls, and (3) the sensitivity to the effects of dopaminergic medication. Results On the most affected side, the mean (SD) balanced accuracy for detecting gait without other arm activities was 0.84 (0.10) pre-medication and 0.88 (0.09) post-medication. The agreement between arm swing parameters of predicted and video-annotated gait segments without other arm activities was high irrespective of medication state (intra-class correlation coefficients: median RoM: 0.99; 95th percentile RoM: 0.97). Both the median and 95th percentile RoM were smaller in PD pre-medication compared to controls (median: <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1578_Article_IEq1.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="94" /> </InlineMediaObject> <EquationSource Format="TEX">$\Delta = -18.80^{\circ }$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="normal">Δ</mi> <mo>=</mo> <mo>-</mo> <mn>18</mn> <mo>.</mo> <msup> <mn>80</mn> <mo>∘</mo> </msup> </mrow> </math></EquationSource> </InlineEquation>, 95% CI [<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1578_Article_IEq2.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$-$</EquationSource> <EquationSource Format="MATHML"><math> <mo>-</mo> </math></EquationSource> </InlineEquation>30.63, <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1578_Article_IEq3.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$-$</EquationSource> <EquationSource Format="MATHML"><math> <mo>-</mo> </math></EquationSource> </InlineEquation>10.60], p < 0.001; 95th percentile: <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1578_Article_IEq4.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="94" /> </InlineMediaObject> <EquationSource Format="TEX">$\Delta = -28.34^{\circ }$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="normal">Δ</mi> <mo>=</mo> <mo>-</mo> <mn>28</mn> <mo>.</mo> <msup> <mn>34</mn> <mo>∘</mo> </msup> </mrow> </math></EquationSource> </InlineEquation>, 95% CI [<InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1578_Article_IEq5.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$-$</EquationSource> <EquationSource Format="MATHML"><math> <mo>-</mo> </math></EquationSource> </InlineEquation>38.26, <InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1578_Article_IEq6.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$-$</EquationSource> <EquationSource Format="MATHML"><math> <mo>-</mo> </math></EquationSource> </InlineEquation>18.18], p < 0.001), and smaller in pre- compared to post-medication (median: <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1578_Article_IEq7.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="94" /> </InlineMediaObject> <EquationSource Format="TEX">$\Delta = -12.31 ^{\circ }$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="normal">Δ</mi> <mo>=</mo> <mo>-</mo> <mn>12</mn> <mo>.</mo> <msup> <mn>31</mn> <mo>∘</mo> </msup> </mrow> </math></EquationSource> </InlineEquation>, 95% CI [<InlineEquation ID="IEq8"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1578_Article_IEq8.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$-$</EquationSource> <EquationSource Format="MATHML"><math> <mo>-</mo> </math></EquationSource> </InlineEquation>21.35, <InlineEquation ID="IEq9"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1578_Article_IEq9.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$-$</EquationSource> <EquationSource Format="MATHML"><math> <mo>-</mo> </math></EquationSource> </InlineEquation>5.59], p < 0.001; 95th percentile: <InlineEquation ID="IEq10"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1578_Article_IEq10.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="94" /> </InlineMediaObject> <EquationSource Format="TEX">$\Delta = -19.04 ^{\circ }$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="normal">Δ</mi> <mo>=</mo> <mo>-</mo> <mn>19</mn> <mo>.</mo> <msup> <mn>04</mn> <mo>∘</mo> </msup> </mrow> </math></EquationSource> </InlineEquation>, 95% CI [<InlineEquation ID="IEq11"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1578_Article_IEq11.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$-$</EquationSource> <EquationSource Format="MATHML"><math> <mo>-</mo> </math></EquationSource> </InlineEquation>28.48, <InlineEquation ID="IEq12"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1578_Article_IEq12.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$-$</EquationSource> <EquationSource Format="MATHML"><math> <mo>-</mo> </math></EquationSource> </InlineEquation>11.14], p < 0.001). The differences in RoM between pre- and post-medication were larger after filtering gait for the median (p < 0.01) and 95th percentile RoM (p = 0.01). Conclusions Filtering out gait segments with other concurrent arm activities is feasible and increases the change in arm swing parameters following dopaminergic medication in free-living conditions. This approach may be used to monitor treatment effect and disease progression in daily life.

Quantifying arm swing in Parkinson’s disease: a method accounting for arm activities during free-living gait

期刊论文
出版模式：

开放获取
发表日期：

2025年2月26日

查看全文

Erik Post,
Twan van Laarhoven,
Yordan P. Raykov,
Max A. Little,
Jorik Nonnekes,
Tom M. Heskes,
Bastiaan R. Bloem,
Luc J. W. Evers

摘要

Background

Accurately measuring hypokinetic arm swing during free-living gait in Parkinson’s disease (PD) is challenging due to other concurrent arm activities. We developed a method to isolate gait segments without these arm activities.

Methods

Wrist accelerometer and gyroscope data were collected from 25 individuals with PD and 25 age-matched controls while performing unscripted activities in their home environment. This was done after overnight withdrawal of dopaminergic medication (‘pre-medication’) and approximately one hour after intake (‘post-medication’). Using video annotations as ground truth, we trained and evaluated two classifiers: one for detecting gait and one for detecting gait segments without other arm activities. Based on the filtered gait segments, arm swing was quantified using the median and 95th percentile range of motion (RoM). These arm swing parameters were evaluated in three ways: (1) the agreement between predicted and video-annotated gait segments without other arm activities, (2) the sensitivity to differences between PD and controls, and (3) the sensitivity to the effects of dopaminergic medication.

Results

On the most affected side, the mean (SD) balanced accuracy for detecting gait without other arm activities was 0.84 (0.10) pre-medication and 0.88 (0.09) post-medication. The agreement between arm swing parameters of predicted and video-annotated gait segments without other arm activities was high irrespective of medication state (intra-class correlation coefficients: median RoM: 0.99; 95th percentile RoM: 0.97). Both the median and 95th percentile RoM were smaller in PD pre-medication compared to controls (median: $\Delta = -18.80^{\circ }$ $Δ = - 18 . 80^{\circ}$ , 95% CI [ $-$ $-$ 30.63, $-$ $-$ 10.60], p < 0.001; 95th percentile: $\Delta = -28.34^{\circ }$ $Δ = - 28 . 34^{\circ}$ , 95% CI [ $-$ $-$ 38.26, $-$ $-$ 18.18], p < 0.001), and smaller in pre- compared to post-medication (median: $\Delta = -12.31 ^{\circ }$ $Δ = - 12 . 31^{\circ}$ , 95% CI [ $-$ $-$ 21.35, $-$ $-$ 5.59], p < 0.001; 95th percentile: $\Delta = -19.04 ^{\circ }$ $Δ = - 19 . 04^{\circ}$ , 95% CI [ $-$ $-$ 28.48, $-$ $-$ 11.14], p < 0.001). The differences in RoM between pre- and post-medication were larger after filtering gait for the median (p < 0.01) and 95th percentile RoM (p = 0.01).

Conclusions

Filtering out gait segments with other concurrent arm activities is feasible and increases the change in arm swing parameters following dopaminergic medication in free-living conditions. This approach may be used to monitor treatment effect and disease progression in daily life.