Factor Loading Matrix for Spasticity Indicators from a Pilot Wearable Sensor Study

Nine individuals with upper-limb spasticity from cerebral palsy or stroke were assessed using a wearable system capturing muscle activation, joint kinematics, and force. A factor loading matrix derived from six area-under-the-curve indicators reveals three latent components accounting for 83.86% of variance. This 5.5 KB Excel file, authored by Juan Manuel Rosero Ñañez and updated in May 2026, contains the weights used to construct a Composite Index for objective spasticity quantification.

Use Cases

• Replicate the Composite Index for spasticity stratification based on the weighted linear aggregation method described.
• Analyze the relative contribution of sEMG, force, and angular velocity indicators to latent components.
• Validate the proposed multimodal instrument's factor structure in new patient cohorts.
• Benchmark new spasticity quantification methods against the derived factor loadings.

Strengths

• Data is derived from a multimodal wearable system integrating surface electromyography, inertial measurement units, and force sensing resistors.
• Three latent components from principal component analysis explain 83.86% of the total variance observed.
• The Composite Index demonstrated differentiation between spasticity severity levels (F=6.38, p=0.0327, η²=0.68) in the pilot study.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (5.5 KB), reflecting a pilot study with only nine participants.

Provenance

Source

Juan Manuel Rosero Ñañez via figshare.

Collection Method

Data captured during standardized passive mobilizations of nine individuals with upper-limb spasticity using a custom wearable sensor system.

Time Range

null

Freshness

Last updated 2026-05-04 17:41:15; freshness should be verified.

Geography

null