Enhancing safety monitoring in post-stroke rehabilitation through wearable technologies
Kátia Rech1,2, Maira Jaqueline da Cunha1,2, Ana Paula Salazar1,2
1Movement Analysis and Rehabilitation Laboratory, Universidade Federal de Ciências da Saude de Porto Alegre (UFCSPA), Porto Alegre, Brazil.
Abstract:
ObjectiveCurrent clinical practice guidelines support structured, progressive protocols for improving walking after stroke. Technology enables monitoring of exercise and therapy intensity, but safety concerns could also be addressed. This study explores functional mobility in post-stroke individuals using wearable technology to quantify movement smoothness-an indicator of safe mobility.DesignObservational cohort study.SettingA movement analysis and rehabilitation laboratory.ParticipantsA total of 56 chronic post-stroke individuals and 51 healthy controls.InterventionParticipants performed the mobility test while wearing an inertial measurement unit attached to their waist. Thirty-two healthy participants also engaged in a steady-state walking task.Main measuresFunctional mobility smoothness by examining angular velocities in the yaw, pitch, and roll axes, employing the spectral arc length metrics.ResultsOur findings reveal that post-stroke individuals extend the duration of the timed-up-and-go test (≈9 s and 23 s longer compared to the controls) to ensure safe mobility-greater mobility smoothness (p < 0.001). Notably, for mild and severe impairments, post-stroke mobility demonstrated ≈8% and ≈11% greater smoothness in pitch movements, respectively (p = 0.025 and p = 0.002). In the roll direction, mobility was ≈12% smoother in cases of severe strokes (p = 0.006).ConclusionThis study addresses a crucial gap in the understanding of mobility smoothness in chronic stroke survivors using wearable technology. Our study suggests the potential utility of spectral arc length to predict challenging mobility situations in real-world situations. We highlight the potential for automated monitoring of safety offering promising avenues for real-time, real-life monitoring.
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