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Background <p>Balance dysfunction in older adults is a major risk factor for falls. Conventional subjective scales and single-modality measures may not adequately capture the integrated motor, cortical, and cognitive processes underlying postural control. Using a multimodal feature set, this study aimed to develop an interpretable framework for dysfunction-related classification and for exploring heterogeneity in balance-related functional patterns.</p> Methods <p>A total of 81 community-dwelling older adults (≥ 60 years old) were recruited and divided into balance dysfunction group (<i>n</i> = 38, BBS ≤ 45) and healthy control group (<i>n</i> = 43, BBS &gt; 45) according to the Berg Balance Scale (BBS). During six stance tasks, center of pressure (COP) trajectories, lower-limb surface electromyography (sEMG), and cortical activation measured by functional near-infrared spectroscopy (fNIRS) were synchronously recorded. Group × task effects were tested using two-way repeated-measures ANOVA. Multimodal features were then used for supervised classification (XGBoost) and for clustering-based exploration of heterogeneity within dysfunction-related multimodal profiles using K-means clustering.</p> Results <p>Under more challenging postural conditions, the dysfunction group showed larger anteroposterior COP oscillation range, velocity, and area. Muscle activation patterns showed lower rectus femoris contribution together with higher biceps femoris and gastrocnemius activation under selected task conditions, accompanied by altered muscle synergy organization. fNIRS revealed greater activation in premotor cortex, supplementary motor area, and prefrontal cortex regions in the dysfunction group. In classification, XGBoost achieved the best overall performance among the tested models, with 83.56% accuracy. Clustering analysis further identified three functional patterns with graded differences in BBS and Montreal Cognitive Assessment (MoCA) scores, suggesting heterogeneity in dysfunction-related multimodal profiles.</p> Conclusion <p>Balance dysfunction in older adults is associated with a neuro-muscular-cognitive profile involving impaired postural control, altered lower-limb muscle recruitment, and increased cortical activation under more demanding task conditions. The proposed multimodal framework provides an interpretable approach for classification and for exploring heterogeneity in dysfunction-related functional patterns. Further validation in larger cohorts and simplified sensor configurations will be needed to support broader practical application.</p>

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Multimodal assessment of balance dysfunction in older adults: from classification to clustering-based functional pattern identification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月14日

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  • Jiayi Sun,
  • Chenhao Li,
  • Mianjia Shan,
  • Yujia Gao,
  • Zilin Wang,
  • Fengxian Wu,
  • Yaming Liu,
  • Wenxin Niu

摘要

Background

Balance dysfunction in older adults is a major risk factor for falls. Conventional subjective scales and single-modality measures may not adequately capture the integrated motor, cortical, and cognitive processes underlying postural control. Using a multimodal feature set, this study aimed to develop an interpretable framework for dysfunction-related classification and for exploring heterogeneity in balance-related functional patterns.

Methods

A total of 81 community-dwelling older adults (≥ 60 years old) were recruited and divided into balance dysfunction group (n = 38, BBS ≤ 45) and healthy control group (n = 43, BBS > 45) according to the Berg Balance Scale (BBS). During six stance tasks, center of pressure (COP) trajectories, lower-limb surface electromyography (sEMG), and cortical activation measured by functional near-infrared spectroscopy (fNIRS) were synchronously recorded. Group × task effects were tested using two-way repeated-measures ANOVA. Multimodal features were then used for supervised classification (XGBoost) and for clustering-based exploration of heterogeneity within dysfunction-related multimodal profiles using K-means clustering.

Results

Under more challenging postural conditions, the dysfunction group showed larger anteroposterior COP oscillation range, velocity, and area. Muscle activation patterns showed lower rectus femoris contribution together with higher biceps femoris and gastrocnemius activation under selected task conditions, accompanied by altered muscle synergy organization. fNIRS revealed greater activation in premotor cortex, supplementary motor area, and prefrontal cortex regions in the dysfunction group. In classification, XGBoost achieved the best overall performance among the tested models, with 83.56% accuracy. Clustering analysis further identified three functional patterns with graded differences in BBS and Montreal Cognitive Assessment (MoCA) scores, suggesting heterogeneity in dysfunction-related multimodal profiles.

Conclusion

Balance dysfunction in older adults is associated with a neuro-muscular-cognitive profile involving impaired postural control, altered lower-limb muscle recruitment, and increased cortical activation under more demanding task conditions. The proposed multimodal framework provides an interpretable approach for classification and for exploring heterogeneity in dysfunction-related functional patterns. Further validation in larger cohorts and simplified sensor configurations will be needed to support broader practical application.