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

Drăgulinescu, Andrei; Zinca, Gabriela; Bucur, Doina; Feies, Valentin; Neagu, Dumitru-Marius

doi:10.3390/s20154316

Cited by 50 publications

(46 citation statements)

References 106 publications

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“…The experimental setup included three parts: a Zebris FDM-THM Treadmill (Zebris Medical GmbH, Germany) for measuring the plantar pressure distribution, wedge- Platform systems embedded in a treadmill are considered the gold standard in plantar pressure measurement and have high measurement precision [24]. We utilized the Zebris FDM-THM Treadmill (Figure 1) to obtain the gait features.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…IoT technologies play a fundamental role in developing these devices, enabling capillary, accurate, and continuous tracking of user conditions [ 22 ]. To detect the pressure distribution, piezoresistive force sensors (FSRs) are used instead of the piezoelectric, capacitive, and optical sensors [ 23 ]. The FSRs are usually featured by a linear trend of conductivity with the applied force [ 24 ].…”

Section: An Overview Of Smart Insoles For Plantar Pressure Detection and Gait Analysismentioning

confidence: 99%

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

Fazio

Perrone

Velázquez

et al. 2021

Sensors

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The evolution of low power electronics and the availability of new smart materials are opening new frontiers to develop wearable systems for medical applications, lifestyle monitoring, and performance detection. This paper presents the development and realization of a novel smart insole for monitoring the plantar pressure distribution and gait parameters; indeed, it includes a piezoresistive sensing matrix based on a Velostat layer for transducing applied pressure into an electric signal. At first, an accurate and complete characterization of Velostat-based pressure sensors is reported as a function of sizes, support material, and pressure trend. The realization and testing of a low-cost and reliable piezoresistive sensing matrix based on a sandwich structure are discussed. This last is interfaced with a low power conditioning and processing section based on an Arduino Lilypad board and an analog multiplexer for acquiring the pressure data. The insole includes a 3-axis capacitive accelerometer for detecting the gait parameters (swing time and stance phase time) featuring the walking. A Bluetooth Low Energy (BLE) 5.0 module is included for transmitting in real-time the acquired data toward a PC, tablet or smartphone, for displaying and processing them using a custom Processing® application. Moreover, the smart insole is equipped with a piezoelectric harvesting section for scavenging energy from walking. The onfield tests indicate that for a walking speed higher than 1 ms−1, the device’s power requirements (i.e., ) was fulfilled. However, more than 9 days of autonomy are guaranteed by the integrated 380-mAh Lipo battery in the total absence of energy contributions from the harvesting section.

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“…Multiple technology options exist for kinetic data measurement including low-cost smart insoles, Nintendo Wii Balance Board, and highend gold standard force plates for kinetic data, that can be used to derive ground reaction forces (GRFs), rate of force development (RFD) and centre of pressure (CoP) parameters. In sports performance laboratories, the force plates are routinely used [526,759], but recently low-cost insoles [195,250,378,612,795,864] and Nintendo R Wii Balance Board TM [543] have been investigated as alternatives as the cost of force plates can be prohibitive in some cases. Recently, nonlearning mathematical [863], and deep learning models have been developed to approximate the forces generated by the athlete, solely from nonkinetic measurements, such as from kinematic data [323,375,724].…”

Section: B Kinetic: Force Measurementmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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

Cited by 50 publications

References 106 publications

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

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

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

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