Search Results (80)

Diabetic foot ulcers (DFUs) represent a critical global health issue, necessitating the development of advanced smart, flexible, and wearable sensors for continuous monitoring that are reimbursable within foot orthotics. This study presents the design and characterization of a pressure sensor implemented into a shoe insole to monitor diabetic wound pressures, emphasizing the need for a high sensitivity, durability under cyclic mechanical loading, and a rapid response time. This investigation focuses on the electrical and mechanical properties of carbon nanotube (CNT) composites utilizing Ecoflex and polydimethylsiloxane (PDMS). Morphological characterization was conducted using Transmission Electron Microscopy (TEM), Laser Confocal Microscopy, and Scanning Electron Microscopy (SEM). The electrical and mechanical properties of the CNT/Ecoflex- and the CNT/PDMS-based sensor composites were then investigated. CNT/Ecoflex was then further evaluated due to its lower variability performance between cycles at the same pressure, as well as its consistently higher capacitance values across all trials in comparison to CNT/PDMS. The CNT/Ecoflex composite sensor showed a high sensitivity (2.38 to 3.40 kPa⁻¹) over a pressure sensing range of 0 to 68.95 kPa. The sensor’s stability was further assessed under applied pressures simulating human weight. A custom insole prototype, incorporating 12 CNT/Ecoflex elastomeric matrix-based sensors (as an example) distributed across the metatarsal heads, midfoot, and heel regions, was developed and characterized. Capacitance measurements, ranging from 0.25 pF to 60 pF, were obtained across N = 3 feasibility trials, demonstrating the sensor’s response to varying pressure conditions linked to different body weights. These results highlight the potential of this flexible insole prototype for precise and real-time plantar surface monitoring, offering an approachable avenue for a challenging diabetic orthotics application. Full article

Gait, the fundamental form of human locomotion, occurs across diverse environments. The technology for recognizing environmental changes during walking is crucial for preventing falls and controlling wearable robots. This study collected gait data on level ground (LG), ramps, and stairs using a feed-forward neural network (FFNN) to classify the corresponding gait environments. Gait experiments were performed on five non-disabled participants using an inertial measurement unit, a galvanic skin response sensor, and a smart insole. The collected data were preprocessed through time synchronization and filtering, then labeled according to the gait environment, yielding 47,033 data samples. Gait data were used to train an FFNN model with a single hidden layer, achieving a high accuracy of 98%, with the highest accuracy observed on LG. This study confirms the effectiveness of classifying gait environments based on signals acquired from various wearable sensors during walking. In the future, these research findings may serve as basic data for exoskeleton robot control and gait analysis. Full article

Instrumented insoles have created opportunities for patient monitoring via long-term recordings of ground reaction forces (GRFs). As the GRF curve is altered in patients after lower-extremity fracture, parameters defined on established curve landmarks often cannot be used to monitor the early rehabilitation process. We aimed to screen several new GRF curve-based parameters for suitability and hypothesized an interrelation with days after surgery. In an observational longitudinal study, data were collected from 13 patients with tibial fractures during straight walking at hospital visits using instrumented insoles. Parametrized curves were fitted and regression analyses conducted to determine the best fit, reflected in the highest R²-value and lowest fitting error. A Wald Test with t-distribution was employed for statistical analysis. Strides were classified as regular or non-regular, and changes in this proportion were analyzed. Among the 12 parameters analyzed, those with the highest R²-values were the mean force between inflection points (R² = 0.715, p < 0.001, t₄₂ = 9.89), the absolute time between inflection points (R² = 0.707, p < 0.001, t₄₂ = 9.83), and the highest overall force (R² = 0.722, p < 0.001, t₄₂ = 10.05). There was a significant increase in regular strides on both injured (R² = 0.427, p < 0.001, t₄₂ = 5.83) and healthy (R² = 0.506, p < 0.001, t₄₂ = 6.89) sides. The proposed parameters and assessment of the regular stride ratio enable new options for analyses and monitoring during rehabilitation after tibial shaft fractures. They are robust to pathologic GRF curves, can be determined independently from spatiotemporal coherence, and thus might provide advantages over established methods. Full article

Monitoring plantar foot temperatures is essential for assessing foot health, particularly in individuals with diabetes at increased risk of complications. Traditional thermographic imaging measures foot temperatures in unshod individuals lying down, which may not reflect thermal characteristics of feet in shod, active, real-world conditions. These controlled settings limit understanding of dynamic foot temperatures during daily activities. Recent advancements in wearable technology, such as insole-based sensors, overcome these limitations by enabling continuous temperature monitoring. This study leverages a data-driven clustering approach, independent of pre-selected foot regions or models like the angiosome concept, to explore normative thermal patterns in shod feet with insole-based sensors. Data were collected from 27 healthy participants using insoles embedded with 21 temperature sensors. The data were analysed using clustering algorithms, including k-means, fuzzy c-means, OPTICS, and hierarchical clustering. The clustering algorithms showed a high degree of similarity, with variations primarily influenced by clustering granularity. Six primary thermal patterns were identified, with the “butterfly pattern” (elevated medial arch temperatures) predominant, representing 51.5% of the dataset, aligning with findings in thermographic studies. Other patterns, like the “medial arch + metatarsal area” pattern, were also observed, highlighting diverse yet consistent thermal distributions. This study shows that while normative thermal patterns observed in thermographic imaging are reflected in insole data, the temperature distribution within the shoe may better represent foot behaviour during everyday activities, particularly when enclosed in a shoe. Unlike thermal imaging, the proposed in-shoe system offers the potential to capture dynamic thermal variations during ambulatory activities, enabling richer insights into foot health in real-world conditions. Full article

A variety of 3D volumetric scanners and smart-device applications are currently being used in podiatry for recording virtual foot data. The accuracy and reliability of these devices vary, resulting in a large variation in the quality of foot scans used for orthotic design. While it is widely believed that a higher quality scanner yields a better scan and thus is expected to produce a more accurate orthotic design, the direct impact of scanning quality on orthotic design has not yet been tested. Therefore, in this study, three commonly used industrial 3D scanners with varying output qualities were used to obtain foot scans of three participants in two weight-bearing conditions. A total of 54 foot scans were obtained, out of which 18 were used to design orthotic insoles using commercial software (FitFoot360). We found variation in the quality of foot scans produced by the different scanners (61.75 ± 2.23% similarity of the foot scans showing a deviation of less than ±1 mm). However, there were no significant differences in the designed foot orthoses within the same weight-bearing condition (83.59 ± 1.97% similarity of the orthotic designs showing a deviation of less than ±1 mm). The medial arch height and heel width differed significantly only when the weight-bearing condition was changed. The findings from this study suggest that the industrial design and production of an orthotic insole using current methods does not depend on the scanning quality of the scanner used but is dependent on the extent of weight bearing. Full article

Chronic ankle instability (CAI), due to its chronic nature and biomechanical complexity, is well-suited for continuous monitoring and tele-rehabilitation using wearable sensor technology. This study assessed whether a smart insole system, equipped with 4 force-sensing resistor sensors and an inertial measurement unit, combined with functional tests and biomechanical indices, could distinguish CAI patients from healthy controls. A total of 21 CAI patients (23.8 ± 5.1 years) and 16 controls (22.62 ± 2.60 years) completed a battery of functional performance tests while wearing the smart insole system. The results showed an increased medial-lateral pressure ratio in the CAI during heel raise (p = 0.031, effect size = 0.82) and hop tests, suggesting an everted foot position. Significant deviations in center-of-pressure trajectory during double-leg heel raises (p = 0.005, effect size = 1.10) suggested asymmetric motion coordination, while compensatory fluctuations of the lifted limb during single-leg balance tests (p = 0.011, effect size = 1.03) were greater in CAI patients. These findings facilitated the development of features to characterize CAI-specific movement patterns. Together, this system shows promise as a quantitative assessment tool for CAI, supporting improved treatment outcomes through tele-rehabilitation. Full article

Background: Virtual reality-based training has been widely used for post-stroke patients due to its positive effects on functional aspects by promoting brain plasticity. Objective: This study aimed to investigate the effectiveness of gait training with virtual reality-based real-time feedback on motor function, balance, and spatiotemporal gait parameters in post-stroke patients. Methods: Fifteen patients (n = 15) with chronic stroke were randomly assigned to either the virtual reality-based real-time feedback with treadmill gait training (experimental group n = 8) or the treadmill gait training (control group n = 7). For the experimental group that participated, a treadmill, an Oculus Rift VR device, and smart insoles were used for gait training with VR-based real-time feedback. Regarding gait training with VR-based real-time feedback, the patient wore an Oculus Rift and performed gait training on a treadmill for 30 min a day, three times a week, for 5 weeks. The control group participated in treadmill gait training for 30 min a day, three times a week, for 5 weeks. Motor function was measured using the Fugl-Meyer assessment. Balance was measured using the timed up and go test and Berg balance scale. Gait performance was measured using an Optogait. The normality test was performed using the Shapiro–Wilk test, the Wilcoxon signed-rank test was used for the within-group comparison, and the Mann–Whitney U test was used for the between-group comparison. Results: In the group analyses, both groups showed significant improvements in motor function balance and gait ability. According to the pre- and post-treatment results, greater improvement in the Fugl-Meyer assessment (experimental group: 4.75 vs. control group: 1.57) was observed in the experimental group compared with the control group (p < 0.05). In balance ability, greater improvement in the timed up and go test (experimental group: −3.10 vs. control group: −1.12) and Berg balance scale (experimental group: 3.00 vs. control group: 1.71) (p < 0.05). In the spatiotemporal gait parameters, greater improvement in affected step length (5.35 vs. 2.01), stride length (3.86 vs. 1.75), affected single support (2.61 vs. 1.22), and cadence (0.07 vs. 0.02) was observed in the experimental group compared with the control group (p < 0.05). Conclusions: This study suggested the positive effects of the virtual reality-based real-time feedback with treadmill gait training on motor function, balance, and gait performance. Full article

Temporal parameters are crucial for understanding running performance, especially in elite sports environments. Traditional measurement methods are often labor-intensive and not suitable for field conditions. This study seeks to provide greater clarity in parameter estimation using a single device by comparing it to the gold standard. Specifically, this study aims to investigate how the temporal parameters and vertical stiffness (K_vert) of running stride exerted by IMU sensors are related to the parameters of the smart insole for outdoor acquisition. Ten healthy male subjects performed four 60-meter high-speed runs. Data were collected using the WIMU PRO™ device and smart insoles. Contact time (CT) and flight time (FT) were identified, and K_vert was calculated using Morin’s method. Statistical analyses assessed data normality, correlations, and reliability. WIMU measured longer CT, with differences ranging from 26.3% to 38.5%, and shorter FT, with differences ranging from 27.3% to 54.5%, compared to smart insoles, across different running speeds. K_vert values were lower with WIMU, with differences ranging from 23.96% to 45.01% depending on the running activity, indicating significant differences (p < 0.001). Using these results, a multiple linear regression model was developed for the correction of WIMU’s K_vert values, improving the accuracy. The improved accuracy of K_vert measurements has significant implications for athletic performance. It provides sports scientists with a more reliable metric to estimate player fatigue, potentially leading to more effective training regimens and injury prevention strategies. This advancement is particularly valuable in team sports settings, where easy-to-use and accurate biomechanical assessments of multiple athletes are essential. Full article

Background: Flatfoot deformity is a common condition in children and teenagers that may increase the risk of knee, hip, and back pain. Most of the insoles suggested to treat flatfoot symptoms are not designed to adapt to foot temperature during walking, and they are either too soft to provide support or hard enough to be uncomfortable. Purpose: This study aims to develop an advanced solution to diagnose and treat flexible flatfoot (FFT) using infrared thermography measurements and a hybrid insole reinforced by nitinol (NiTiCu) smart-memory-alloy wires (SMAWs), this super-elastic alloy can return back to its pre-deformed shape when heated, which helps to reduce the local high-temperature points caused by the uneven pressure of FFT. This approach achieves a more uniform thermal distribution across the foot, which makes the hybrid insole more comfortable. Methods: The study involved 16 subjects, divided into two groups of eight flat-footed and eight normal. The procedure includes two parts, namely, designing a prototype insole with SMAW properties based on thermography measurement by using SolidWorks, and evaluating this design using Ansys. Second, a hybrid insole reinforced with SMAWs is customized for flatfoot subjects. The thermography measurement differences between the medial and lateral sides of the metatarsophalangeal line are compared for the normal and flatfoot groups before and after wearing the suggested design. Results: The results show that our approach safely diagnosed FFT and significantly improved the thermal distribution in FFT subjects by more than 80% after wearing the suggested design. A paired t-test reported significant (p-value > 0.001) thermal decreases in the high-temperature points after using the SMAW insole, which was closely approximated to the normal subjects. Conclusions: the SMAW-reinforced insole is comfortable and suitable for treating FFT deformity, and infrared thermography is an effective tool to evaluate FFT deformity. Full article

Although diabetic polyneuropathy (DPN) has a very high prevalence among people with diabetes, gait analysis using cyclograms is very limited, and cyclogram research, in general, is limited to standard measures available in software packages. In this study, cyclograms (movements of the centre of pressure, COP, on and between the plantar surfaces) of diabetics and healthy individuals recorded with a smart insole were compared in terms of geometry and balance index, BI. The latter was calculated as the summed product of standard deviations of cyclogram markers, i.e., start/end points, turning points, and intersection points of the COP. The geometry was assessed by the positions of, and distances between, these points, and the distance ratios (14 parameters in total). The BI of healthy and diabetic individuals differed significantly. Of the fifteen parameters (including the BI), three were suitable as classifiers to predict DPN, namely two distances and their ratio, with false negatives ranging from 1.8 to 12.5%, and false positives ranging from 2.9 to 7.1%. The standard metric of the cyclogram provided by the software packages failed as a classifier. While the BI captures both DPN-related balance and other balance disorders, the changing geometry of the cyclogram in diabetics appears to be DPN-specific. Full article

Diabetes is a significant global health issue impacting millions. Approximately 26 million diabetics experience foot ulcers, with 20% ending up with amputations, resulting in high morbidity, mortality, and costs. Plantar pressure screening shows potential for early detection of Diabetic Foot Ulcers (DFUs). Although foot ulcers often occur due to excessive pressure on the soles during dynamic activities, most studies focus on static pressure measurements. This study’s primary objective is to apply wireless plantar pressure sensor-embedded insoles to classify and detect diabetic feet from healthy ones based on dynamic plantar pressure. The secondary objective is to compare statistical-based and Machine Learning (ML) classification methods. Data from 150 subjects were collected from the insoles during walking, revealing that diabetic feet have higher plantar pressure than healthy feet, which is consistent with prior research. The Adaptive Boosting (AdaBoost) ML model achieved the highest accuracy of 0.85, outperforming the statistical method, which had an accuracy of 0.67. These findings suggest that ML models, combined with pressure sensor-embedded insoles, can effectively classify healthy and diabetic feet using plantar pressure features. Future research will focus on using these insoles with ML to classify various stages of diabetic neuropathy, aiming for early prediction of foot ulcers in home settings. Full article

This study presents an IoT-based gait analysis system employing insole pressure sensors to assess gait kinetics. The system integrates piezoresistive sensors within a left foot insole, with data acquisition managed using an ESP32 board that communicates via Wi-Fi through an MQTT IoT framework. In this initial protocol study, we conducted a comparative analysis using the Zeno system, supported by PKMAS as the gold standard, to explore the correlation and agreement of data obtained from the insole system. Four volunteers (two males and two females, aged 24–28, without gait disorders) participated by walking along a 10 m Zeno system path, equipped with pressure sensors, while wearing the insole system. Vertical ground reaction force (vGRF) data were collected over four gait cycles. The preliminary results indicated a strong positive correlation (r = 0.87) between the insole and the reference system measurements. A Bland–Altman analysis further demonstrated a mean difference of approximately (0.011) between the two systems, suggesting a minimal yet significant bias. These findings suggest that piezoresistive sensors may offer a promising and cost-effective solution for gait disorder assessment and monitoring. However, operational factors such as high temperatures and sensor placement within the footwear can introduce noise or unwanted signal activation. The communication framework proved functional and reliable during this protocol, with plans for future expansion to multi-device applications. It is important to note that additional validation studies with larger sample sizes are required to confirm the system’s reliability and robustness for clinical and research applications. Full article

This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer’s gait and diagnose balance issues. This approach can be utilized to improve a user’s rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques. Full article

Background: Integrating diverse sensor technologies into smart insoles offers significant potential for monitoring biomechanical metrics; enhancing sports performance; and managing therapeutic interventions, diseases, disorders, and other health-related issues. The variation in sensor types and applications requires a systematic review to synthesize existing evidence and guide future innovations. Objectives: This review aims to identify, categorize, and critically evaluate the various sensors used in smart insoles, focusing on their technical specifications, application scopes, and validity. Methods: Following the PRISMA guidelines, a search was conducted in three major electronic databases, namely, PubMed, Scopus, and Web of Science, for relevant literature published from 2014 to 2024. Other works not located in the mentioned databases were added manually by parallel searches on related themes and suggestions from the website of the databases. To be eligible, studies were required to describe sensor implementation in insoles, specify the sensor types, and report on either validation experiments or practical outcomes. Results: The search identified 33 qualifying studies. Proper analysis revealed a dominance of pressure sensors, with accelerometers and gyroscopes also being widely used. Critical applications included gait analysis, posture correction, and real-time athletic and rehabilitation feedback. The review also examined the relative effectiveness of different sensor configurations. Conclusions: This systematic review comprehensively classifies sensor technologies within smart insoles and highlights their broad application potential across various fields. Future research should aim to standardize measurement protocols, enhance sensor integration, and advance data processing techniques to boost functionality and clinical applicability. Full article