Detection of Excessive Pronation and Supination for Walking and Running Gait with Smart Socks

Eizentals, Peteris; Katashev, Alexei; Okss, Alexander; Pavare, Zane; Balcuna, Darta

doi:10.1007/978-981-10-9038-7_112

Cited by 8 publications

(10 citation statements)

References 4 publications

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“…A method was also proposed, in which the values given by the sensors are converted into a pressure vector, which can be further used for describing each step [25]. In particular, this method is capable of detecting, for each step, the excessive pronation and supination of the foot.…”

Section: Smart Socks For Gait Monitoring and Partitioning Of Gait Cyclementioning

confidence: 99%

“…The same research group [25] suggested, for improving their method of detecting excessive pronation and supination with smart socks, a larger sample of participants, including not only healthy persons, but also patients, and finally, in order to improve the quality of the results, an increase of the sampling rate, by means of improved hardware solutions.…”

Section: Challenges and Open Issuesmentioning

confidence: 99%

See 1 more Smart Citation

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications

Drăgulinescu

Zinca

Bucur

et al. 2020

Sensors

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The present paper reviews, for the first time, to the best of our knowledge, the most recent advances in research concerning two popular devices used for foot motion analysis and health monitoring: smart socks and in-shoe systems. The first one is representative of textile-based systems, whereas the second one is one of the most used pressure sensitive insole (PSI) systems that is used as an alternative to smart socks. The proposed methods are reviewed for smart sock use in special medical applications, for gait and foot pressure analysis. The Pedar system is also shown, together with studies of validation and repeatability for Pedar and other in-shoe systems. Then, the applications of Pedar are presented, mainly in medicine and sports. Our purpose was to offer the researchers in this field a useful means to overview and select relevant information. Moreover, our review can be a starting point for new, relevant research towards improving the design and functionality of the systems, as well as extending the research towards other areas of applications using sensors in smart textiles and in-shoe systems.

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Section: Smart Socks For Gait Monitoring and Partitioning Of Gait Cyclementioning

confidence: 99%

Section: Challenges and Open Issuesmentioning

confidence: 99%

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications

Drăgulinescu

Zinca

Bucur

et al. 2020

Sensors

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“…Strephenopodia seriously affects the patient's daily life and rehabilitation and may also cause serious injury [2][3][4]. To obtain better rehabilitation, it is necessary for patients to train themselves to walk properly using their own muscles [5].…”

Section: Introductionmentioning

confidence: 99%

“…Since the acquisition of plantar pressure is simple and noninvasive, it has been widely used in detecting and monitoring gait patterns and lower extremity movements [17][18][19]. In a previous report [3], smart textile socks with five integrated pressure sensors were used for pronation and supination detection. Another study [5] utilized a pressuresensitive insole to detect foot pronation in real time, and feedback provided to the user reduced pronation significantly.…”

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of Strephenopodia Based on Plantar Pressure and Machine Learning: A Pilot Study

Nong

Zhou

Xian

et al. 2021

Preprint

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Purpose Stroke patients often suffer from strephenopodia because of high muscle tension or muscle spasms, which seriously affect their walking ability and rehabilitation. During the treatment of strephenopodia, there are practical demands for convenient, automatic, and quantitative assessments of the angle of strephenopodia. However, existing strephenopodia detection methods, including traditional clinical gait analysis, gait video analysis and plantar pressure systems, suffer from object obstruction or require complex setups. In this paper, we proposed a novel methodology for automatically predicting the angles of strephenopodia based on a gait analysis system using machine learning methods.Methods Plantar pressure distribution data from thirty healthy participants were recorded during walking on the Zebris FDM-THM instrumented treadmill and were processed to generate 15 gait features. The right ankle angles on the coronal plane were measured by the Vicon system to provide a detailed description and explanation of strephenopodia walking. Three machine learning methods were implemented to build stochastic function mapping from gait features to strephenopodia angles.Results This study showed good reliability and precision prediction of the angle of strephenopodia [determination coefficient (R2)\(\ge\)0.80]. Gaussian process regression (GPR) exhibited the best regression performance [R2 = 0.93, mean root-mean-square error (RMSE) = 0.67].Conclusion The study results showed that this strephenopodia-detection method is not only convenient to implement but also has high accuracy and outperforms previous reports. Measurements derived from the gait analysis system are proper estimators of the angle of strephenopodia and should be considered to improve diagnosis and assessment of the stroke population.

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“…Some smart socks have been reported in the literature or are on the market. They can measure the pressure on the foot at several positions within the sock, localized on specific areas of the sole, using piezo-resistive yarns [49,50,51,52] patches made from yarn [53] or polymer plate [54,55]. Other proposed methods include socks sensitive on the whole surface with a specific sensor yarn integrated in the socks, such as Alpha-Fit [56], or made from a piezo-resistive complex of three layers of 2D fabrics sewn to obtain a foot shape and adapted only for prosthesis [57], or the forming of a sensor sewn on the sock [58].…”

Section: Introductionmentioning

confidence: 99%

E-Knitted Textile with Polymer Optical Fibers for Friction and Pressure Monitoring in Socks

Guignier

Camillieri

Schmid

et al. 2019

Sensors

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The objective of this paper is to study the ability of polymer optical fiber (POF) to be inserted in a knitted fabric and to measure both pressure and friction when walking. Firstly, POF, marketed and in development, have been compared in terms of the required mechanical properties for the insertion of the fiber directly into a knitted fabric on an industrial scale, i.e. elongation, bending rigidity, and minimum bending radius before plastic deformation. Secondly, the chosen optical fiber was inserted inside several types of knitted fabric and was shown to be sensitive to friction and compression. The knitted structure with the highest sensitivity has been chosen for sock prototype manufacturing. Finally, a feasibility study with an instrumented sock showed that it is possible to detect the different phases of walking in terms of compression and friction.

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Detection of Excessive Pronation and Supination for Walking and Running Gait with Smart Socks

Cited by 8 publications

References 4 publications

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications

Automatic Evaluation of Strephenopodia Based on Plantar Pressure and Machine Learning: A Pilot Study

E-Knitted Textile with Polymer Optical Fibers for Friction and Pressure Monitoring in Socks

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