ArticlePloS one2026
Classification of daily activities using a wireless instrumented insole (WalkinSense) in a semi free-living setting.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
backgroundAccurate 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.
methodsA 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.
resultsActivity 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%).
conclusionThese 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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