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The aim of the study was to determine if body symmetry influences establishing stand balance after drop jump.  Thirty-two healthy sports students participated in this study, with an average age 19.8 ± 1.4 years, height of 182.9 ± 6.8 cm and weight of 79.1 ± 8.1 kg. Morphological characteristics were assessed by measuring the differences between the left and right side of forearm girth, upper arm girth, calf girth, thigh girth, long shoulder height, lean mass of legs and lean mass of arms. The standing balance result was calculated based on factor scores obtained from nine measurements taken for 30 seconds after jumping from a 25 cm height platform. These measurements included three for normal stand, three for blind stand, and three for deaf stand. The data was collected using a pressure insoles system and by measuring the difference in ground reaction force between the left and right leg. Regression analysis revealed that 27% of the differences in leg load could be explained by differences in morphological characteristics between the left and right side. Two significant predictors were identified: the difference in long shoulder height (explaining 16% of the variance) and the differences in arm lean mass (explaining 11% of the variance). Both variables showed a negative relationship with the factor jump standing. It was observed that imbalances in body symmetry could increase the long-term risk of acute or chronic injuries. Namen raziskave je bil ugotoviti, ali skladnost telesa vpliva na vzpostavitev ravnotežja po skoku v globino v stoji na nogah. V raziskavi je sodelovalo 32 zdravih športnikov z izkušnjami v orodni telovadbi. Udeleženci so bili povprečno stari 19,8 ± 1,4 leta, visoki 182,9 ± 6,8 cm in težki 79,1 ± 8,1 kg. Telesne značilnosti so bile ocenjene z merjenjem razlik med levo in desno stranjo v obsegu podlakti, obsegu nadlahti, obsegu meč, obsegu stegen, višini ramen, pusti masi nog in pusti masi rok. Rezultat stoječega ravnotežja je bil izračunan na podlagi faktorskih rezultatov, pridobljenih iz devetih meritev, izvedenih v 30 sekundah po skoku s 25 cm visoke podlage. Te meritve so vključevale tri za normalno stojo, tri za stojo brez vidne zaznave in tri za stojo brez slušne zaznave. Podatki so bili zbrani s pomočjo sistema pritiskovnih stopalnih vložkov in z merjenjem razlike v reakcijski sili na tla med levo in desno nogo. Stopenjska regresijska analiza je pokazala, da je 27 % razlik v obremenitvi nog mogoče razložiti z razlikami v telesnih značilnostih med levo in desno stranjo z dvema pomembnima napovedovalcema: razliko v višini ramen, ki pojasnjuje 16 % spremenljivosti, in razlike v pusti masi rok, ki pojasnjuje 11 %. Obe spremenljivki sta pokazali negativno povezavo z izvedbo skoka iz stoje. Neravnovesja v telesni skladnosti povečajo dolgoročno tveganje za enkratne ali dolgotrajne poškodbe.

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

Wearable sensors combined with machine learning (ML) offer a promising approach for estimating joint kinetics in real-world settings, with potential applications in athlete monitoring and injury prevention. However, the variety of sensor configurations in previous studies complicates comparisons and optimal configuration selection. This study compared different wearable sensor configurations, comprising inertial measurement units (IMUs) and pressure insoles (PIs), to determine their influence on the accuracy of ML – based predictions of 3D knee moments during running. Sensor configurations ranged from one to four IMUs, with and without PIs. The dataset consisted of wearable and ground truth knee moment data from 19 recreational runners during treadmill running. Model performance of the convolutional neural networks was evaluated on an independent test set. Hyperparameter optimization (HPO) was applied to refine model architectures and training parameters. Performance gains by PIs and a greater number of IMUs were small but significant. The results after HPO confirmed similar performances between single- and multi-sensor configurations, suggesting only small benefits from additional sensors. Our findings highlight that both sensor configuration and model optimization play critical roles in achieving optimal performance. We provide practical recommendations for sensor selection, balancing accuracy and feasibility, to enable biomechanical assessments in real-world environments.

HighlightsCreate a hierarchical honeycomb-inspired triboelectric nanogenerator (TENG) with excellent transparency, compactness, lightweight and deformability.Amplify capacitance variation by dividing large hollow space into numerous energy generation units with porous honeycomb architecture.Demonstrate self-powered insole plantar pressure mapping applications by the self-sustained elastic nature of the h-TENG device.Integrate the h-TENG into the morphing wing of small-unmanned aerial vehicles for converting flapping motions into electricity for the first time.Flexible, compact, lightweight and sustainable power sources are indispensable for modern wearable and personal electronics and small-unmanned aerial vehicles (UAVs). Hierarchical honeycomb has the unique merits of compact mesostructures, excellent energy absorption properties and considerable weight to strength ratios. Herein, a honeycomb-inspired triboelectric nanogenerator (h-TENG) is proposed for biomechanical and UAV morphing wing energy harvesting based on contact triboelectrification wavy surface of cellular honeycomb structure. The wavy surface comprises a multilayered thin film structure (combining polyethylene terephthalate, silver nanowires and fluorinated ethylene propylene) fabricated through high-temperature thermoplastic molding and wafer-level bonding process. With superior synchronization of large amounts of energy generation units with honeycomb cells, the manufactured h-TENG prototype produces the maximum instantaneous open-circuit voltage, short-circuit current and output power of 1207 V, 68.5 μA and 12.4 mW, respectively, corresponding to a remarkable peak power density of 0.275 mW cm−3 (or 2.48 mW g−1) under hand pressing excitations. Attributed to the excellent elastic property of self-rebounding honeycomb structure, the flexible and transparent h-TENG can be easily pressed, bent and integrated into shoes for real-time insole plantar pressure mapping. The lightweight and compact h-TENG is further installed into a morphing wing of small UAVs for efficiently converting the flapping energy of ailerons into electricity for the first time. This research demonstrates this new conceptualizing single h-TENG device's versatility and viability for broad-range real-world application scenarios.

The accurate identification of initial and final foot contacts is a crucial prerequisite for obtaining a reliable estimation of spatio-temporal parameters of gait. Well-accepted gold standard techniques in this field are force platforms and instrumented walkways, which provide a direct measure of the foot–ground reaction forces. Nonetheless, these tools are expensive, non-portable and restrict the analysis to laboratory settings. Instrumented insoles with a reduced number of pressure sensing elements might overcome these limitations, but a suitable method for gait events identification has not been adopted yet. The aim of this paper was to present and validate a method aiming at filling such void, as applied to a system including two insoles with 16 pressure sensing elements (element area = 310 mm2), sampling at 100 Hz. Gait events were identified exploiting the sensor redundancy and a cluster-based strategy. The method was tested in the laboratory against force platforms on nine healthy subjects for a total of 801 initial and final contacts. Initial and final contacts were detected with low average errors of (about 20 ms and 10 ms, respectively). Similarly, the errors in estimating stance duration and step duration averaged 20 ms and <10 ms, respectively. By selecting appropriate thresholds, the method may be easily applied to other pressure insoles featuring similar requirements.

Measuring the foot plantar pressure has the potential to be an important tool in many areas such as enhancing sports performance, diagnosing diseases, and rehabilitation. In general, the plantar pressure sensor should have robustness, durability, and high repeatability, as it should measure the pressure due to body weight. Here, we present a novel insole foot plantar pressure sensor using a highly sensitive crack-based strain sensor. The sensor is made of elastomer, stainless steel, a crack-based sensor, and a 3D-printed frame. Insoles are made of elastomer with Shore A 40, which is used as part of the sensor, to distribute the load to the sensor. The 3D-printed frame and stainless steel prevent breakage of the crack-based sensor and enable elastic behavior. The sensor response is highly repeatable and shows excellent durability even after 20,000 cycles. We show that the insole pressure sensor can be used as a real-time monitoring system using the pressure visualization program.

Pressure-detecting insoles such as the Insole3 have potential as a portable alternative for assessing vertical ground reaction force (vGRF) outside of specialized laboratories. This study evaluated whether the Insole3 is a valid and reliable alternative to force plates for measuring vGRF. Eleven healthy participants walked overground at slow and moderately paced speeds and ran at a moderate pace while collecting vGRF simultaneously from a force plate (3000 Hz) and Insole3 (100 Hz). Intraclass correlation coefficients (ICC) demonstrated excellent vGRF agreement between systems during both walking speeds for Peak 1, Peak 2, the valley between peaks, and the vGRF impulse (ICC > 0.941). There was excellent agreement during running for the single vGRF peak (ICC = 0.942) and impulse (ICC = 0.940). The insoles slightly underestimated vGRF peaks (−3.7% to 0.9% bias) and valleys (−2.2% to −1.8% bias), and slightly overestimated impulses (4.2% to 5.6% bias). Reliability between visits for all three activities was excellent (ICC > 0.970). The Insole3 is a valid and reliable alternative to traditional force plates for assessing vGRF during walking and running in healthy adults. The excellent ICC values during slow walking suggests that the Insole3 may be particularly suitable for older adults in clinical and home settings.

The three Ground Reaction Force (GRF) components can be estimated using pressure insole sensors. In this paper, we compare the accuracy of estimating GRF components for both feet using six methods: three Deep Learning (DL) methods (Artificial Neural Network, Long Short-Term Memory, and Convolutional Neural Network) and three Supervised Machine Learning (SML) methods (Least Squares, Support Vector Regression, and Random Forest (RF)). Data were collected from nine subjects across six activities: normal and slow walking, static with and without carrying a load, and two Manual Material Handling activities. This study has two main contributions: first, the estimation of GRF components (Fx, Fy, and Fz) during the six activities, two of which have never been studied; second, the comparison of the accuracy of GRF component estimation between the six methods for each activity. RF provided the most accurate estimation for static situations, with mean RMSE values of RMSE_Fx = 1.65 N, RMSE_Fy = 1.35 N, and RMSE_Fz = 7.97 N for the mean absolute values measured by the force plate (reference) RMSE_Fx = 14.10 N, RMSE_Fy = 3.83 N, and RMSE_Fz = 397.45 N. In our study, we found that RF, an SML method, surpassed the experimented DL methods.

Compared to force-plates, pressure-detecting insoles have the advantage that vertical ground reaction force (vGRF) can be estimated under field rather than laboratory conditions. However, the question arises whether insoles also provide valid and reliable results compared to a force-plate (i.e., the gold standard). The study aimed to investigate the concurrent validity and test–retest reliability of pressure-detecting insoles during static and dynamic movements. Twenty-two healthy young adults (12 females) performed standing, walking, running, and jumping movements while simultaneously collecting pressure (GP MobilData WiFi, GeBioM mbH, Münster, Germany) and force (Kistler®) data twice, 10 days apart. Concerning validity, ICC values showed excellent agreement (ICC > 0.75), irrespective of the test condition. Further, the insoles underestimated (mean bias: −4.41 to −37.15%) most of the vGRF variables. Concerning reliability, ICC values for nearly all test conditions also showed excellent agreement, and the SEM was rather low. Lastly, most of the MDC95% values were low (≤5%). The predominantly excellent ICC values for between-devices (i.e., concurrent validity) and between-visits (i.e., test–retest reliability) comparisons suggest that the tested pressure-detecting insoles can be used under field-based conditions for a valid and reliable estimation of relevant vGRF variables during standing, walking, running, and jumping.

Insole pressure sensors capture the different forces exercised over the different parts of the sole when performing tasks standing up such as walking. Using data analysis and machine learning techniques, common patterns and strategies from different users to achieve different tasks can be automatically extracted. In this paper, we present the results obtained for the automatic detection of different strategies used by stroke survivors when walking as integrated into an Information Communication Technology (ICT) enhanced Personalised Self-Management Rehabilitation System (PSMrS) for stroke rehabilitation. Fourteen stroke survivors and 10 healthy controls have participated in the experiment by walking six times a distance from chair to chair of approximately 10 m long. The Rivermead Mobility Index was used to assess the functional ability of each individual in the stroke survivor group. Several walking strategies are studied based on data gathered from insole pressure sensors and patterns found in stroke survivor patients are compared with average patterns found in healthy control users. A mechanism to automatically estimate a mobility index based on the similarity of the pressure patterns to a stereotyped stride is also used. Both data gathered from stroke survivors and healthy controls are used to evaluate the proposed mechanisms. The output of trained algorithms is applied to the PSMrS system to provide feedback on gait quality enabling stroke survivors to self-manage their rehabilitation.