Multi-modal sensor fusion in smart footwear to capture altered gait dynamics in patients with diabetic peripheral neuropathy: System validation and clinical applications.
Individuals with diabetic peripheral neuropathy (DPN) are confronted with significantly elevated risk of falling and diabetic foot ulcers. There is an increasing need for clinically viable wearable technologies for routine biomechanical assessment of DPN patients.
Can a footwear-based wearable system ("Lab-in-Shoe") achieve gold-standard accuracy and effectively distinguish gait characteristics among patients with type 2 diabetes (T2DM) under various neuropathic conditions?
The spatiotemporal accuracy of the system was first validated against an 8-camera Vicon motion capture system using a single-marker strategy. For clinical evaluation, thirty T2DM patients were classified into G1 (no neuropathy), G2 (subclinical), and G3 (confirmed DPN). All patients performed a 10 m walking test, utilizing the "Lab-in-Shoe" to simultaneously capture spatiotemporal parameters and plantar pressure to evaluate their effectiveness in distinguishing degrees of neuropathy. Additionally, a Diabetic Gait Index (DGI) was formulated based on these fused features.
The system showed excellent agreement with Vicon across all seven evaluated spatiotemporal parameters, yielding mean absolute errors (MAE) between 2% and 8% and intraclass correlation coefficients (ICC) ranging from 0.812 to 0.993. The severe neuropathy group (G3) exhibited a 14% reduction in step length (p < 0.001) and a 14% reduction in step height (p < 0.01). Concurrently, step frequency was significantly increased by 8% (p < 0.001) and swing phase duration was shorter by 9% (p < 0.01). Regarding plantar loading, G3 showed significantly higher load under the second metatarsal and lateral heel compared to G1 (all p < 0.05). Furthermore, the proposed DGI achieved an 86.7% accuracy in classifying the three neuropathy severity groups.
This wearable system effectively detects subtle gait changes associated with the progression of DPN. It is well-suited for routine clinical practice, offering an objective tool for early risk screening for falls and ulceration in DPN patients.
Can a footwear-based wearable system ("Lab-in-Shoe") achieve gold-standard accuracy and effectively distinguish gait characteristics among patients with type 2 diabetes (T2DM) under various neuropathic conditions?
The spatiotemporal accuracy of the system was first validated against an 8-camera Vicon motion capture system using a single-marker strategy. For clinical evaluation, thirty T2DM patients were classified into G1 (no neuropathy), G2 (subclinical), and G3 (confirmed DPN). All patients performed a 10 m walking test, utilizing the "Lab-in-Shoe" to simultaneously capture spatiotemporal parameters and plantar pressure to evaluate their effectiveness in distinguishing degrees of neuropathy. Additionally, a Diabetic Gait Index (DGI) was formulated based on these fused features.
The system showed excellent agreement with Vicon across all seven evaluated spatiotemporal parameters, yielding mean absolute errors (MAE) between 2% and 8% and intraclass correlation coefficients (ICC) ranging from 0.812 to 0.993. The severe neuropathy group (G3) exhibited a 14% reduction in step length (p < 0.001) and a 14% reduction in step height (p < 0.01). Concurrently, step frequency was significantly increased by 8% (p < 0.001) and swing phase duration was shorter by 9% (p < 0.01). Regarding plantar loading, G3 showed significantly higher load under the second metatarsal and lateral heel compared to G1 (all p < 0.05). Furthermore, the proposed DGI achieved an 86.7% accuracy in classifying the three neuropathy severity groups.
This wearable system effectively detects subtle gait changes associated with the progression of DPN. It is well-suited for routine clinical practice, offering an objective tool for early risk screening for falls and ulceration in DPN patients.
Authors
Wang Wang, Xie Xie, Zhang Zhang, Huang Huang, Geng Geng, Zhao Zhao, Yin Yin, Ma Ma, Chen Chen
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