ArticleJournal of neuroengineering and rehabilitation2025
Digital biomechanical assessment of gait in patients with peripheral neuropathies.
Article in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- Multi-Domain CoP Feature Analysis of Functional Mobility for Parkinson's Disease Detection Using Wearable Pressure Insoles.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
18 authors.
Funding
Abstract
backgroundThe clinical status and treatment response of patients with peripheral neuropathies (PNs) rely on subjective and inaccurate clinical scales. Wearable sensors have been evaluated successfully in other neurological conditions to study gait and balance. Our aim was to explore the ability of biomechanical analysis using wearable technology to monitor disease activity in PN.
methodsWe conducted a single-center, longitudinal study to analyze gait parameters in PN patients and healthy controls using wearable biomechanical sensors. We used a novel technology that registers and integrates data from multiple wearable inertial sensors placed at different locations and plantar insoles. This system allows measuring kinematics, spatio-temporal parameters and plantar pressure. Patients wore the wearable system while performing the 2-min walking test (2MWT).
resultsWe included 37 chronic inflammatory demyelinating polyneuropathy (CIDP) patients, 3 chronic ataxic neuropathy, ophthalmoplegia, immunoglobulin M [IgM] paraprotein (CANOMAD) patients, 21 monoclonal gammopathy patients of undetermined significance associated with IgM (IgM-MGUS) patients, 7 patients with autoimmune nodopathies, 11 patients with hereditary neuropathies, and 50 healthy controls. First, we analyzed the sensor's ability to detect changes in ataxia and steppage gait severity and found significant differences in spatiotemporal and angular variables of the gait cycle. Second, we found correlations between biomechanical features and clinical scales and with the specific gait phenotype they associated with. Finally, we demonstrated that this technology is able to capture clinically significant changes in gait features over time.
conclusionsOur study provides proof-of-concept that wearable technology effectively detects and grades gait impairment, captures clinically relevant changes, and could enhance gait assessment in routine care and clinical research for patients with PN.
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