Work

A semantic digital health pipeline for interpretable and longitudinal gait monitoring using wearable sensors

Year: 2026

Type: article

Abstract: Background: Wearable gait monitoring can support neurological follow-up, but raw inertial time-series are difficult to interpret clinically. Objective: To evaluate a semantic pipeline that converts raw wearable streams into structured gait events for free-living monitoring. Methods: Instrumented smart socks captured tri-axial accelerometer and gyroscope signals from a bilateral inertial measurement unit (IMU) configuration. Data were stored as raw time-series. A deterministic rule-based pipeline derived time-bounded events (per-limb sensor-reception intervals, concurrent bilateral candidate intervals, per-limb effective-movement intervals, and bilateral gait episodes) in a relational database. The semantic layer was published as a Web Ontology Language (OWL) ontology with an Ontop ontology-based data access (OBDA) mapping and reusable SPARQL Protocol and RDF Query Language (SPARQL) queries. Feasibility was evaluated in 44 participants (31 people with multiple sclerosis (PwMS) and 13 controls) across 257 monitoring sessions. Results: The pipeline identified 6,224 gait episodes (∼60.8 hours walking). Episode counts were right-skewed (mean 141.5 episodes/participant, 95% CI 109-174). Most participants (80%) showed low left-right asymmetry (mean index 0.069, 95% CI 0.040-0.107), while elevated imbalance was concentrated in low-data participants. Against standardised in-clinic tests, positive-interval detection sensitivity was 85% (50/59 intervals with bilateral data) after per-session clock alignment using the same annotations; this potentially optimistic estimate does not establish specificity or free-living false-positive performance. In the frozen analysis snapshot, semantic-consistency checking confirmed that 100% of the 6,224 reported gait episodes were temporally contained within concurrent bilateral candidate intervals (activity_all). Conclusions: Hierarchical semantic temporal events enable transparent, scalable, and interpretable real-world gait monitoring. By releasing the ontology, OBDA mapping, and queries, the pipeline supports longitudinal assessment, limb balance analysis, and interoperable health-system integration. The same approach is applicable beyond gait to other wearable-derived digital biomarkers and rehabilitation-monitoring tasks. This work does not deliver a validated clinical gait assessment tool, but demonstrates reusable digital health representations.

Source: Digital Health

DOI: 10.1177/20552076261488885

Authors: Amir Farmanesh, Joaquín Ordieres-Meré, Raúl G. Sanchis, Yolanda Aladro-Benito

Institutions: Universidad Politécnica de Madrid, Hospital Universitario La Paz, Hospital La Paz Institute for Health Research, Hospital Universitario de Getafe, Universidad Europea de Madrid

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,000