<p>Duchenne muscular dystrophy (DMD) and spinal muscular atrophy (SMA) are neuromuscular diseases that lead to progressive muscle degeneration and weakness. Recent therapeutic advances for DMD and SMA highlight the need for accurate clinical evaluation. Traditionally, motor function of the upper limbs is assessed using motor function scales. However, these scales are influenced by clinician’s interpretation and may lack accuracy. For this reason, clinicians are becoming interested in finding alternative solutions. In this context, Inertial Measurement Units (IMUs) have gained popularity, offering the possibility to quantitatively and objectively analyze motor function of patients to support clinicians’ assessments. We analyzed upper limb kinematics of two groups of children with neuromuscular diseases, seventeen DMD patients and fifteen SMA patients, while performing the corresponding clinical assessment. These two groups were further subdivided into two categories (Category A and Category B), according to disease severity (Brooke scores <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1602_Article_IEq1.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="29" /> </InlineMediaObject> <EquationSource Format="TEX">$\le 2$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>≤</mo> <mn>2</mn> </mrow> </math></EquationSource> </InlineEquation> and Brooke scores <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1602_Article_IEq2.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="28" /> </InlineMediaObject> <EquationSource Format="TEX">$>2$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>></mo> <mn>2</mn> </mrow> </math></EquationSource> </InlineEquation>, respectively). The results were compared against a group of ten healthy children. The metrics showing the strongest correlation with the clinical score were the workspace area in the frontal and transverse plane (DMD: <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1602_Article_IEq3.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">$\rho$</EquationSource> <EquationSource Format="MATHML"><math> <mi>ρ</mi> </math></EquationSource> </InlineEquation> = 0.94 and <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1602_Article_IEq4.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">$\rho$</EquationSource> <EquationSource Format="MATHML"><math> <mi>ρ</mi> </math></EquationSource> </InlineEquation> = 0.90; SMA: <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1602_Article_IEq5.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">$\rho$</EquationSource> <EquationSource Format="MATHML"><math> <mi>ρ</mi> </math></EquationSource> </InlineEquation> = 0.78 and <InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1602_Article_IEq6.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">$\rho$</EquationSource> <EquationSource Format="MATHML"><math> <mi>ρ</mi> </math></EquationSource> </InlineEquation> = 0.81) and the workspace volume (DMD: <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1602_Article_IEq7.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">$\rho$</EquationSource> <EquationSource Format="MATHML"><math> <mi>ρ</mi> </math></EquationSource> </InlineEquation> = 0.92; SMA <InlineEquation ID="IEq8"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12984_2025_1602_Article_IEq8.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">$\rho$</EquationSource> <EquationSource Format="MATHML"><math> <mi>ρ</mi> </math></EquationSource> </InlineEquation> = 0.81). Additionally, statistically significant differences were found not only between healthy children and those with neuromuscular disease, but also across severity levels within the patient group. These results represent a first step toward validating IMU-based systems to helping clinicians to accurately quantify the motor status of children with neuromuscular diseases. Furthermore, data collected with inertial sensors can provide clinicians with additional information not available through subjective observation.</p>

Imu-based kinematic analysis to enhance upper limb motor function assessment in neuromuscular diseases

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2025年3月18日

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Alessandra Favata,
Roger Gallart-Agut,
Luc van Noort,
Jesica Exposito-Escudero,
Julita Medina-Cantillo,
Carme Torras,
Daniel Natera-de Benito,
Josep M. Font-Llagunes,
Rosa Pàmies-Vilà

摘要

Duchenne muscular dystrophy (DMD) and spinal muscular atrophy (SMA) are neuromuscular diseases that lead to progressive muscle degeneration and weakness. Recent therapeutic advances for DMD and SMA highlight the need for accurate clinical evaluation. Traditionally, motor function of the upper limbs is assessed using motor function scales. However, these scales are influenced by clinician’s interpretation and may lack accuracy. For this reason, clinicians are becoming interested in finding alternative solutions. In this context, Inertial Measurement Units (IMUs) have gained popularity, offering the possibility to quantitatively and objectively analyze motor function of patients to support clinicians’ assessments. We analyzed upper limb kinematics of two groups of children with neuromuscular diseases, seventeen DMD patients and fifteen SMA patients, while performing the corresponding clinical assessment. These two groups were further subdivided into two categories (Category A and Category B), according to disease severity (Brooke scores $\le 2$ $\leq 2$ and Brooke scores $>2$ $> 2$ , respectively). The results were compared against a group of ten healthy children. The metrics showing the strongest correlation with the clinical score were the workspace area in the frontal and transverse plane (DMD: $\rho$ $ρ$ = 0.94 and $\rho$ $ρ$ = 0.90; SMA: $\rho$ $ρ$ = 0.78 and $\rho$ $ρ$ = 0.81) and the workspace volume (DMD: $\rho$ $ρ$ = 0.92; SMA $\rho$ $ρ$ = 0.81). Additionally, statistically significant differences were found not only between healthy children and those with neuromuscular disease, but also across severity levels within the patient group. These results represent a first step toward validating IMU-based systems to helping clinicians to accurately quantify the motor status of children with neuromuscular diseases. Furthermore, data collected with inertial sensors can provide clinicians with additional information not available through subjective observation.