aceLower limb prosthetic users' activity classification through real-world inertial sensing
The prescription of lower limb prosthetics relies heavily on the assigned K-level. K-level assessment has been criticised for subjectivity and variability, potentially leading to inappropriate component selection and poorer functional outcomes. This study presents an original methodology for objectively determining activity levels from inertial sensor data, developed through rigorous data collection on 20 amputees who walked on various types of outdoor terrain (flat ground, stairs, ramp, grass/uneven/unstable su…
A random forest classifier trained on real-world sensor data from 20 amputees classified outdoor activities with 85.65% cross-validation accuracy, pointing toward more objective activity assessment for prosthetic prescription.
Accuracy on unseen users was only about 66%, with overfitting to individual participants, and the sample was small, so the authors call it a proof of concept rather than a definitive prescribing tool.
Evidence
- Peer-reviewedPLOS One2026-10-09
How should this claim be treated?
Truvace Impact Record TRV-2026-1671, v1: “Lower limb prosthetic users' activity classification through real-world inertial sensing.” Truvace, 2026-10-17. /record/TRV-2026-1671 (accessed at citation time). sha256 512df3b966b5ba55…
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