Classification of daily activities using a wireless instrumented insole (WalkinSense) in a semi free-living setting

Background Accurate monitoring of activities of daily living (ADLs) in real‑world environments is essential for preventive care and rehabilitation, yet it remains difficult to achieve outside controlled laboratory settings. Instrumented insoles provide a promising, unobtrusive solution for continuous monitoring. However, the use of multimodal systems integrating plantar pressure and inertial data in naturalistic conditions and larger cohorts is still limited. This study therefore aims to evaluate the accuracy of a wireless instrumented insole (WalkinSense) that fuses pressure and inertial sensor data to classify ADLs within a semi free-living setting. Methods A total of 99 participants performed a broad set of indoor and outdoor activities. Frame-by-frame performance was compared to ground truth (direct observation) using overall accuracy, Cohen’s Kappa, precision, recall, F1-scores and a normalised confusion matrix. Agreement on total activity duration was assessed using mean absolute percentage error (MAPE) scores and Bland-Altman plots. Results Activity classification showed almost perfect agreement (mean overall accuracy 0.87, mean Cohen’s Kappa 0.84). Excellent performance (F1 > 0.90) was achieved for sitting, walking with crutches and cycling, while standing, level and non-level walking showed good performance (F1 > 0.80). Most misclassifications occurred between level walking, hill walking and stairs. Duration-based analysis confirmed high accuracy for sitting, walking with crutches and cycling (MAPE ≤ 10%). Bland-Altman plots indicated overestimation of level walking and underestimation of hill and stair walking. Step count was highly accurate (MAPE < 5%), whereas stair count showed only reasonable accuracy (MAPE ≈ 26%). Conclusion These findings demonstrate the system’s strong potential for real-world monitoring and classification of ADLs while also highlighting the need for improved detection of non-level walking activities.

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PLoS ONE
Published
2026-09-16
DOI
https://doi.org/10.1371/journal.pone.0357961
Primary Topic
Balance, Gait, and Falls Prevention
Type
article
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article

Classification of daily activities using a wireless instrumented insole (WalkinSense) in a semi free-living setting

Jennifer Fayad, Romain Seil, Melanie Eckelt, Thomas Solignac et al.
PLoS ONE
Balance, Gait, and Falls Prevention
article

Classification of daily activities using a wireless instrumented insole (WalkinSense) in a semi free-living setting

Jennifer Fayad, Romain Seil, Melanie Eckelt, Thomas Solignac, Anne Backes, Laurent Malisoux, Valeria Serchi, Tobias Meyer, Caroline Mouton, Bernd Grimm
article en

Abstract

Background Accurate monitoring of activities of daily living (ADLs) in real‑world environments is essential for preventive care and rehabilitation, yet it remains difficult to achieve outside controlled laboratory settings. Instrumented insoles provide a promising, unobtrusive solution for continuous monitoring. However, the use of multimodal systems integrating plantar pressure and inertial data in naturalistic conditions and larger cohorts is still limited. This study therefore aims to evaluate the accuracy of a wireless instrumented insole (WalkinSense) that fuses pressure and inertial sensor data to classify ADLs within a semi free-living setting. Methods A total of 99 participants performed a broad set of indoor and outdoor activities. Frame-by-frame performance was compared to ground truth (direct observation) using overall accuracy, Cohen’s Kappa, precision, recall, F1-scores and a normalised confusion matrix. Agreement on total activity duration was assessed using mean absolute percentage error (MAPE) scores and Bland-Altman plots. Results Activity classification showed almost perfect agreement (mean overall accuracy 0.87, mean Cohen’s Kappa 0.84). Excellent performance (F1 > 0.90) was achieved for sitting, walking with crutches and cycling, while standing, level and non-level walking showed good performance (F1 > 0.80). Most misclassifications occurred between level walking, hill walking and stairs. Duration-based analysis confirmed high accuracy for sitting, walking with crutches and cycling (MAPE ≤ 10%). Bland-Altman plots indicated overestimation of level walking and underestimation of hill and stair walking. Step count was highly accurate (MAPE < 5%), whereas stair count showed only reasonable accuracy (MAPE ≈ 26%). Conclusion These findings demonstrate the system’s strong potential for real-world monitoring and classification of ADLs while also highlighting the need for improved detection of non-level walking activities.

PLoS ONEVol. 21(9)
Centre Hospitalier de Luxembourg (LU), Luxembourg Institute of Health (LU), Institute of Sports Medicine and Science (JP)
Openalex Percentile: Top 5%
Balance, Gait, and Falls Prevention
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