Lower limb prosthetic users’ activity classification through real-world inertial sensing
- 1Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- 2School of Health & Society, University of Salford, Salford, United Kingdom
- 3Blatchford Prosthetics, Basingstoke, United Kingdom
Abstract
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 surfaces), with and without a walking aid. Advanced machine learning algorithms were applied to the real-world IMU data, to establish a validated classification model for daily activity outside the laboratory. A random forest classifier achieved a mean leave‑some‑out accuracy of 65.70% (SD 6.6%; 95% CI 47.3–84.1%) and cross‑validation accuracy of 85.65% (SD 0.79%; 95% CI 83.4–87.9%). Per‑class sensitivity ranged from approximately 44–78% in leave‑some‑out analyses, with lower performance for ramp ascending and descending. The difference between cross‑validation and leave‑some‑out accuracies indicates participant‑specific overfitting, so the leave‑some‑out accuracy is considered the current conservative estimate of performance on unseen users and the cross-validation accuracy an estimate of potential accuracy with a more robust dataset. The research subsequently incorporated a preliminary clinical evaluation of the approach on a subset of 3 amputees using a data collection protocol for unsupervised, longitudinal real-world monitoring, with preliminary insight into perceived clinical significance of outcomes ascertained through exploratory interviews with 4 prescribing health care professionals. The findings suggest potential benefits of real-world inertial sensing, supporting a move from primarily subjective clinical assessment toward more objective, data-driven activity classification. This research demonstrates that an evidence-based tool for enhancing K-level prescription could in future provide more appropriate component selection and improve long-term mobility for prosthetic users, but current accuracies and sample sizes indicate that the system should be viewed as a promising proof‑of‑concept rather than a definitive tool for changing K‑level prescription.
Related
- Pantoea ananatis- triggered systemic resistance requires root sensing through the LORE receptor kinase in Arabidopsis
- “Drug-Delivering-Drug” nanocomplexes reinforce cytosolic nucleic acid sensing for broad-spectrum antiviral activity in fish
- Central lipid sensing tunes accumbal D2R-neuron activity in appetitive behaviours
- IoT-enabled smart home activity classification using BiLSTM and attention mechanisms
- Detection of Stress in Naturalistic Settings Through Passive Mobile Sensing
BibTeX
@article{wassall2026lower,
title = {{Lower limb prosthetic users’ activity classification through real-world inertial sensing}},
author = {Matthew Wassall and Malcolm Granat and Saeed Zahedi and Sibylle Thies},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0358882},
url = {https://doi.org/10.1371/journal.pone.0358882},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Lower limb prosthetic users’ activity classification through real-world inertial sensing
AU - Matthew Wassall
AU - Malcolm Granat
AU - Saeed Zahedi
AU - Sibylle Thies
PY - 2026
DA - 2026/10/09
JO - PLOS One
DO - 10.1371/journal.pone.0358882
UR - https://doi.org/10.1371/journal.pone.0358882
AB - 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 surfaces), with and without a walking aid. Advanced machine learning algorithms were applied to the real-world IMU data, to establish a validated classification model for daily activity outside the laboratory. A random forest classifier achieved a mean leave‑some‑out accuracy of 65.70% (SD 6.6%; 95% CI 47.3–84.1%) and cross‑validation accuracy of 85.65% (SD 0.79%; 95% CI 83.4–87.9%). Per‑class sensitivity ranged from approximately 44–78% in leave‑some‑out analyses, with lower performance for ramp ascending and descending. The difference between cross‑validation and leave‑some‑out accuracies indicates participant‑specific overfitting, so the leave‑some‑out accuracy is considered the current conservative estimate of performance on unseen users and the cross-validation accuracy an estimate of potential accuracy with a more robust dataset. The research subsequently incorporated a preliminary clinical evaluation of the approach on a subset of 3 amputees using a data collection protocol for unsupervised, longitudinal real-world monitoring, with preliminary insight into perceived clinical significance of outcomes ascertained through exploratory interviews with 4 prescribing health care professionals. The findings suggest potential benefits of real-world inertial sensing, supporting a move from primarily subjective clinical assessment toward more objective, data-driven activity classification. This research demonstrates that an evidence-based tool for enhancing K-level prescription could in future provide more appropriate component selection and improve long-term mobility for prosthetic users, but current accuracies and sample sizes indicate that the system should be viewed as a promising proof‑of‑concept rather than a definitive tool for changing K‑level prescription.
SN - 1932-6203
ER -