Deep learning algorithm for plantar pressures

NewsBot · Jan 17, 2022

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Deep learning approach to estimate foot pressure distribution in walking with application for a cost-effective insole system
Frederick Mun, Ahnryul Choi
J Neuroeng Rehabil. 2022 Jan 16;19(1):4

Background: Foot pressure distribution can be used as a quantitative parameter for evaluating anatomical deformity of the foot and for diagnosing and treating pathological gait, falling, and pressure sores in diabetes. The objective of this study was to propose a deep learning model that could predict pressure distribution of the whole foot based on information obtained from a small number of pressure sensors in an insole.

Methods: Twenty young and twenty older adults walked a straight pathway at a preferred speed with a Pedar-X system in anti-skid socks. A long short-term memory (LSTM) model was used to predict foot pressure distribution. Pressure values of nine major sensors and the remaining 90 sensors in a Pedar-X system were used as input and output for the model, respectively. The performance of the proposed LSTM structure was compared with that of a traditionally used adaptive neuro-fuzzy interference system (ANFIS). A low-cost insole system consisting of a small number of pressure sensors was fabricated. A gait experiment was additionally performed with five young and five older adults, excluding subjects who were used to construct models. The Pedar-X system placed parallelly on top of the insole prototype developed in this study was in anti-skid socks. Sensor values from a low-cost insole prototype were used as input of the LSTM model. The accuracy of the model was evaluated by applying a leave-one-out cross-validation.

Results: Correlation coefficient and relative root mean square error (RMSE) of the LSTM model were 0.98 (0.92 ~ 0.99) and 7.9 ± 2.3%, respectively, higher than those of the ANFIS model. Additionally, the usefulness of the proposed LSTM model for fabricating a low-cost insole prototype with a small number of sensors was confirmed, showing a correlation coefficient of 0.63 to 0.97 and a relative RMSE of 12.7 ± 7.4%.

Conclusions: This model can be used as an algorithm to develop a low-cost portable smart insole system to monitor age-related physiological and anatomical alterations in foot. This model has the potential to evaluate clinical rehabilitation status of patients with pathological gait, falling, and various foot pathologies when more data of patients with various diseases are accumulated for training.
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NewsBot · Apr 5, 2022

New Distinct Component Patterns for Plantar Pressure Variables by Using Principal Component Analysis
Başar Öztürk , Yusuf Çelik
J Am Podiatr Med Assoc. 2022 Mar 11;1-30.

Background: It's important to determine the plantar pressure distribution of school children by applying static and dynamic foot analyses using a pedobarography device. However, it's difficult to obtain clear interpretations from results which can be explained by a large number of plantar pressure variables. The aim of this study is to use Principal Component Analysis (PCA) to predict main components for reducing the size of big data sets, provide a practical overview and minimize information loss on the subject of plantar pressure assessment in youths.

Methods: In total, 112 school children were included in the current study (average age 10.58 ± 1.27 years, body mass index 18.86 ± 4.33 kg / m2). During the research, a Sensor Medica Freemed pedobarography device was used to obtain plantar pressure data. Each foot was divided into six anatomical regions and evaluated. Global and regional plantar pressure distribution, load and surface areas, pressure time integrals, weight ratios and geometric foot properties were calculated.

Results: PCA yielded ten principal component (PC) that together account for 81.88% of the variation in the data set and represent new and distinct patterns. Thus, 137 variables affecting the subject were reduced to ten components.

Conclusions: Static and dynamic plantar pressure distribution, which is affected by many variables, can be reduced to ten components by PCA, making the research results more concise and understandable.
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