Instrumented Insoles Research Articles

The FeetMe® Insoles System: Repeatability, Standard Error of Measure, and Responsiveness.

Three-dimensional motion analysis using optoelectronic cameras and force platforms is typically used to quantify gait disorders. However, these systems have various limitations, particularly when assessing patients in an ecological environment. To address these limitations, several wearable devices have been developed. However, few studies have reported metrological information regarding their repeatability and sensitivity to change. A healthy adult performed 6 min walking tests with FeetMe® system insoles under different walking conditions overground and on a treadmill. The standard error of measurement (SEM), the minimum detectable differences (MDDs), and the effect size (ES) were calculated for spatio-temporal parameters, and the ground reaction force was calculated from the 16,000 steps recorded. SEM values were below 3.9% for the ground reaction force and below 6.8% for spatio-temporal parameters. ES values were predominantly high, with 72.9% of cases between overground and treadmill conditions with induced asymmetry, and 64.5% of cases between treadmill conditions with and without induced asymmetry exhibiting an ES greater than 1.2. The minimum detectable differences ranged from 4.5% to 10.7% for ground reaction forces and 2.1% to 18.9% for spatio-temporal parameters. Our study demonstrated that the FeetMe® system is a reliable solution. The sensitivity to change showed that these instrumented insoles can effectively reflect patient asymmetry and progress.

Improving Biological Joint Moment Estimation During Real-World Tasks With EMG and Instrumented Insoles.

Real-time measurement of biological joint moment could enhance clinical assessments and generalize exoskeleton control. Accessing joint moments outside clinical and laboratory settings requires harnessing non-invasive wearable sensor data for indirect estimation. Previous approaches have been primarily validated during cyclic tasks, such as walking, but these methods are likely limited when translating to non-cyclic tasks where the mapping from kinematics to moments is not unique. We trained deep learning models to estimate hip and knee joint moments from kinematic sensors, electromyography (EMG), and simulated pressure insoles from a dataset including 10 cyclic and 18 non-cyclic activities. We assessed estimation error on combinations of sensor modalities during both activity types. Compared to the kinematics-only baseline, adding EMG reduced RMSE by 16.9% at the hip and 30.4% at the knee (p < 0.05) and adding insoles reduced RMSE by 21.7% at the hip and 33.9% at the knee (p < 0.05). Adding both modalities reduced RMSE by 32.5% at the hip and 41.2% at the knee (p < 0.05) which was significantly higher than either modality individually (p < 0.05). All sensor additions improved model performance on non-cyclic tasks more than cyclic tasks (p < 0.05). These results demonstrate that adding kinetic sensor information through EMG or insoles improves joint moment estimation both individually and jointly. These additional modalities are most important during non-cyclic tasks, tasks that reflect the variable and sporadic nature of the real-world. Improved joint moment estimation and task generalization is pivotal to developing wearable robotic systems capable of enhancing mobility in everyday life.

Predicting Musculoskeletal Loading at Common Running Injury Locations Using Machine Learning and Instrumented Insoles.

Wearables have the potential to provide accurate estimates of tissue loads at common running injury locations. Here we investigate the accuracy by which commercially available instrumented insoles (ARION; ATO-GEAR, Eindhoven, The Netherlands) can predict musculoskeletal loading at common running injury locations. Nineteen runners (10 males) ran at five different speeds, four slopes, with different step frequencies, and forward trunk lean on an instrumented treadmill while wearing instrumented insoles. The insole data were used as input to an artificial neural network that was trained to predict the Achilles tendon strain, and tibia and patellofemoral stress impulses and weighted impulses (damage proxy) as determined with musculoskeletal modeling. Accuracy was investigated using leave-one-out cross-validation and correlations. The effect of different input metrics was also assessed. The neural network predicted tissue loading with overall relative percentage errors of 1.95 ± 8.40%, -7.37 ± 6.41%, and -12.8 ± 9.44% for the patellofemoral joint, tibia, and Achilles tendon impulse, respectively. The accuracy significantly changed with altered running speed, slope, or step frequency. Mean (95% confidence interval) within-individual correlations between modeled and predicted impulses across conditions were generally nearly perfect, being 0.92 (0.89 to 0.94), 0.95 (0.93 to 0.96), and 0.95 (0.94 to 0.96) for the patellofemoral, tibial, and Achilles tendon stress/strain impulses, respectively. This study shows that commercially available instrumented insoles can predict loading at common running injury locations with variable absolute but (very) high relative accuracy. The absolute error was lower than the methods that measure only the step count or assume a constant load per speed or slope. This developed model may allow for quantification of in-field tissue loading and real-time tissue loading-based feedback to reduce injury risk.

In-shoe plantar pressure measurement technologies for the diabetic foot: A systematic review

IntroductionLoss of cutaneous protective sensation and high plantar pressures increase the risk for diabetic foot patients. Trauma and ulceration are imminent threats, making assessment and monitoring essential. This systematic review aims to identify systems and technologies for measuring in-shoe plantar pressures, focusing on the at-risk diabetic foot population. MethodsA systematic search was conducted across four electronic databases (Scopus, Web of Science, PubMed, Oxford Journals) using PRISMA methodology, covering articles published in English from 1979 to 2024. Only studies addressing systems or sensors exclusively measuring plantar pressures inside the shoe were included. ResultsA total of 87 studies using commercially available devices and 45 articles proposing new systems or sensors were reviewed. The prevailing market offerings consist mainly of instrumented insoles. Emerging technologies under development often feature configurations with four, six or eight resistive sensors strategically placed within removable insoles. Despite some variability due to the inherent heterogeneity of human gait, these devices assess plantar pressure, although they present significant differences between them in measurement results. Individuals with diabetic foot conditions appears exhibit elevated plantar pressures, with reported peak pressures reaching approximately 1000 kPa. The results also showed significant differences between the diabetic and non-diabetic groups. ConclusionInstrumented insoles, particularly those incorporating resistive sensor technology, dominate the field. Systems employing eight sensors at critical locations represent a pragmatic approach, although market options extend to systems with up to 960 sensors. Differences between devices can be a critical factor in measurement and highlights the importance of individualized patient assessment using consistent measurement devices.

Long-term continuous instrumented insole-based gait analyses in daily life have advantages over longitudinal gait analyses in the lab to monitor healing of tibial fractures.

Introduction: Monitoring changes in gait during rehabilitation allows early detection of complications. Laboratory-based gait analyses proved valuable for longitudinal monitoring of lower leg fracture healing. However, continuous gait data recorded in the daily life may be superior due to a higher temporal resolution and differences in behavior. In this study, ground reaction force-based gait data of instrumented insoles from longitudinal intermittent laboratory assessments were compared to monitoring in daily life. Methods: Straight walking data of patients were collected during clinical visits and in between those visits the instrumented insoles recorded all stepping activities of the patients during daily life. Results: Out of 16 patients, due to technical and compliance issues, only six delivered sufficient datasets of about 12weeks. Stance duration was longer (p = 0.004) and gait was more asymmetric during daily life (asymmetry of maximal force p < 0.001, loading slope p = 0.001, unloading slope p < 0.001, stance duration p < 0.001). Discussion: The differences between the laboratory assessments and the daily-life monitoring could be caused by a different and more diverse behavior during daily life. The daily life gait parameters significantly improved over time with union. One of the patients developed an infected non-union and showed worsening of force-related gait parameters, which was earlier detectable in the continuous daily life gait data compared to the lab data. Therefore, continuous gait monitoring in the daily life has potential to detect healing problems early on. Continuous monitoring with instrumented insoles has advantages once technical and compliance problems are solved.

The Effect of Wearable-Based Real-Time Feedback on Running Injuries and Running Performance: A Randomized Controlled Trial

Background: Running technique and running speed are considered important risk factors for running injuries. Real-time feedback on running technique and running speed by wearables may help reduce injury risk. Purpose: To investigate whether real-time feedback on spatiotemporal metrics and relative speed by commercially available pressure-sensitive insoles would reduce running injuries and improve running performance compared with no real-time feedback. Study Design: Randomized controlled trial; Level of evidence, 1. Methods: A total of 220 recreational runners were randomly assigned into the intervention and control groups. Both groups received pressure-sensitive insoles, but only the intervention group received real-time feedback on spatiotemporal metrics and relative speed. The feedback aimed to reduce loading on the joint/segment estimated to exhibit the highest load. Injury rates were compared between the groups using Cox regressions. Secondary outcomes compared included injury severity, the proportion of runners with multiple injuries, changes in self-reported personal best times and motivation (Behavioral Regulation in Exercise Questionnaire–2), and interest in continuing wearable use after study completion. Results: A total of 160 participants (73%) were included in analyses of the primary outcome. Intention-to-treat analysis showed no significant difference in injury rate between the groups (Hazard ratio [HR], 1.11; P = .70). This was expected, as 53 of 160 (33%) participants ended up in the unassigned group because they used incorrect wearable settings, nullifying any interventional effects. As-treated analysis showed a significantly lower injury rate among participants receiving real-time feedback (HR, 0.53; P = .03). Similarly, the first-time injury severity was significantly lower (–0.43; P = .042). Per-protocol analysis showed no significant differences in injury rates, but the direction favored the intervention group (HR, 0.67; P = .30). There were no significant differences in the proportion of patients with multiple injuries (HR, 0.82; P = .40) or changes in running performance (3.07%; P = .26) and motivation. Also, ~60% of the participants who completed the study showed interest in continuing wearable use. Conclusion: Real-time feedback on spatiotemporal metrics and relative speed provided by commercially available instrumented insoles may reduce the rate and severity of injuries in recreational runners. Feedback did not influence running performance and exercise motivation. Registration: NL8472 (Dutch Trial Register).

Estimating vertical ground reaction forces from plantar pressure using interpretable high-dimensional approximation

Several studies have reported methods for capturing ground reaction forces in the field. However, these forces are usually measured indirectly with non-interpretable “black box” models. Here we report an interpretable model with a function approximation algorithm. The model uses time series of plantar pressure from instrumented insoles to estimate vertical ground reaction forces. The study included data from 16 persons moving at different speeds on a two-belt treadmill equipped with force plates. The introduced regression model, based on a high-dimensional approximation, using only low-dimensional variable interactions, demonstrated its ability to estimate vertical ground reaction forces. The normalized mean square error for velocities between 3 and 9kmh-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$9\\,\\hbox {km}\\,\\hbox {h}^{-1}$$\\end{document} ranged from 10.6 to 24.4%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$24.4\\,\\%$$\\end{document}. The accuracy of the presented approach, which can be used to analyze and interpret the learned model, was comparable to that reported in the literature. Furthermore, the evaluation of the learned model is particularly suitable for embedded and portable systems and, after a one-time calibration measurement, allows permanent and laboratory-independent measurement of vertical ground reaction forces.

Foot Sole Contact Forces vs. Ground Contact Forces to Obtain Foot Joint Moments for In-Shoe Gait-A Preliminary Study.

In-shoe models are required to extend the clinical application of current multisegment kinetic models of the bare foot to study the effect of foot orthoses. Work to date has only addressed marker placement for reliable kinematic analyses. The purpose of this study is to address the difficulties of recording contact forces with available sensors. Ten participants walked 5 times wearing two different types of footwear by stepping on a pressure platform (ground contact forces) while wearing in-shoe pressure sensors (foot sole contact forces). Pressure data were segmented by considering contact cells' anteroposterior location, and were used to compute 3D moments at foot joints. The mean values and 95% confidence intervals were plotted for each device per shoe condition. The peak values and times of forces and moments were computed per participant and trial under each condition, and were compared using mixed-effect tests. Test-retest reliability was analyzed by means of intraclass correlation coefficients. The curve profiles from both devices were similar, with higher joint moments for the instrumented insoles at the metatarsophalangeal joint (~26%), which were lower at the ankle (~8%) and midtarsal (~15%) joints, although the differences were nonsignificant. Not considering frictional forces resulted in ~20% lower peaks at the ankle moments compared to previous studies, which employed force plates. The device affected both shoe conditions in the same way, which suggests the interchangeability of measuring joint moments with one or the other device. This hypothesis was reinforced by the intraclass correlation coefficients, which were higher for the peak values, although only moderate-to-good. In short, both considered alternatives have drawbacks. Only the instrumented in-soles provided direct information about foot contact forces, but it was incomplete (evidenced by the difference in ankle moments between devices). However, recording ground reaction forces offers the advantage of enabling the consideration of contact friction forces (using force plates in series, or combining a pressure platform and a force plate to estimate friction forces and torque), which are less invasive than instrumented insoles (which may affect subjects' gait).

The accuracy of commercially available instrumented insoles (ARION) for measuring spatiotemporal running metrics.

Spatiotemporal metrics such as step frequency have been associated with running injuries in some studies. Wearables can measure these metrics and provide real-time feedback in-field, but are often not validated. This study assessed the validity of commercially available wireless instrumented insoles (ARION) for quantifying spatiotemporal metrics during level running at different speeds (2.78-5.0 m s-1 ,) and slopes (3° and 6° up/downhill) to an instrumented treadmill. Mean raw, percentage and absolute percentage error, and limits of agreement (LoA) were calculated. Agreement was statistically quantified using four thresholds: excellent, <5%; good, <10%; acceptable, <15%; and poor, >15% error. Excellent agreement (<5% error) was achieved for stride time across all conditions, and for step frequency across all but one condition with good agreement. Contact time and swing time generally showed at least good agreement. The mean difference across all conditions was -0.95% for contact time, 0.11% for stride time, 0.6% for swing time, -0.11% for step frequency, and -0.09% when averaged across all outcomes and conditions. The accuracy at an individual level was generally good to excellent, being <10% for all but two conditions, with these conditions being <15%. Additional experiments among four runners showed that step length could also be measured with an accuracy of 1.76% across different speeds with an updated version of the insoles. These findings suggests that the ARION wearable may not only be useful for large-scale in-field studies investigating group differences, but also to quantify spatiotemporal metrics with generally good to excellent accuracy for individual runners.

Modified stepping behaviour during outdoor winter walking increases resistance to forward losses of stability

Healthy humans are proficient at maintaining stability when faced with diverse walking conditions, however, the control strategies that lead to this proficiency are unclear. Previous laboratory-based research has predominantly concluded that corrective stepping is the main strategy, but whether this finding holds when facing everyday obstacles outside of the laboratory is uncertain. We investigated changes in gait stability behaviour when walking outdoors in the summer and winter, hypothesizing that as ground conditions worsened in the winter, the stepping strategy would be hindered. Stability would then be maintained through compensatory strategies such as with ankle torques and trunk rotation. Data was collected in the summer and winter using inertial measurement units to collect kinematics and instrumented insoles to collect vertical ground reaction forces. Using the goodness of fit for a multivariate regression between the centre of mass state and foot placement we found that, counter to our hypothesis, stepping was not hindered by winter conditions. Instead, the stepping strategy was modified to increase the anterior-posterior margin of stability, increasing the resistance to a forward loss of stability. With stepping being unhindered, we did not observe any additional compensation from the ankle or trunk strategies.

Monitoring Knee Contact Force with Force-Sensing Insoles

Numerous applications exist for monitoring knee contact force (KCF) throughout activities of daily living. However, the ability to estimate these forces is restricted to a laboratory setting. The purposes of this study are to develop KCF metric estimation models and explore the feasibility of monitoring KCF metrics via surrogate measures derived from force-sensing insole data. Nine healthy subjects (3F, age 27 ± 5 years, mass 74.8 ± 11.8 kg, height 1.7 ± 0.08 m) walked at multiple speeds (0.8–1.6 m/s) on an instrumented treadmill. Thirteen insole force features were calculated as potential predictors of peak KCF and KCF impulse per step, estimated with musculoskeletal modeling. The error was calculated with median symmetric accuracy. Pearson product-moment correlation coefficients defined the relationship between variables. Models develop per-limb demonstrated lower prediction error than those developed per-subject (KCF impulse: 2.2% vs 3.4%; peak KCF: 3.50% vs. 6.5%, respectively). Many insole features are moderately to strongly associated with peak KCF, but not KCF impulse across the group. We present methods to directly estimate and monitor changes in KCF using instrumented insoles. Our results carry promising implications for internal tissue loads monitoring outside of a laboratory with wearable sensors.

Effect of Sensor Size, Number and Position under the Foot to Measure the Center of Pressure (CoP) Displacement and Total Center of Pressure (CoPT) Using an Anatomical Foot Model

Ambulatory instrumented insoles are widely used in real-time monitoring of the plantar pressure in order to calculate balance indicators such as Center of Pressure (CoP) or Pressure Maps. Such insoles include many pressure sensors; the required number and surface area of the sensors used are usually determined experimentally. Additionally, they follow the common plantar pressure zones, and the quality of measurement is usually strongly related to the number of sensors. In this paper, we experimentally investigate the robustness of an anatomical foot model, combined with a specific learning algorithm, to measure the static displacement of the center of pressure (CoP) and the center of total pressure (CoPT), as a function of the number, size, and position of sensors. Application of our algorithm to the pressure maps of nine healthy subjects shows that only three sensors per foot, with an area of about 1.5 × 1.5 cm2, are needed to give a good approximation of the CoP during quiet standing when placed on the main pressure areas.

Evaluating Gait Impairment in Parkinson's Disease from Instrumented Insole and IMU Sensor Data.

Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients' mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment.

Estimation of hike events and temporal parameters with body-attached sensors

The analysis of human gait is of fundamental importance for the monitoring and enhancement of athletes’ performances. The kinematics and kinetics of human gait are mostly investigated with optical motion capture systems and force plates that require specialised laboratories and limit the possible test conditions. On the contrary, body-attached sensor networks provide an opportunity for long-term acquisitions in unsupervised, naturalistic scenarios. In this study, a wearable sensor network consisting of two wireless dataloggers and two instrumented insoles with eight pressure sensors each is used. Custom algorithms for the automatic detection of hike events and the estimation of the related temporal parameters based on sensors data are presented. The proposed algorithms were tested against laboratory measurements performed on an instrumented treadmill and showed relative errors of less than 2.5% in the estimation of stride time, step time and cadence. Higher relative errors were found in the estimation of stance and swing phases. The developed algorithms were also applied in a field study. In this paper data from one subject are considered. The aim of this research work is to provide an effective sensor-based methodology for the evaluation of gait parameters in naturalistic settings.

Biosignals by In-Shoe Plantar Pressure Sensors on Different Hardness Mats during Running: A Cross-Over Study

Although the effects of running on plantar pressures have been detailed on several surfaces with different hardness, there is a lack of studies assessing the mechanical behavior analysis by in-shoe plantar pressure sensors on different hardness mats during running. The aim of the present study was to determine in-shoe maximum forces and peak plantar pressures on mats with different hardness, such as hard, soft and air chamber mats, during running. A cross-over study was carried out including 36 amateur runners from a sport center. The maximum force and peak pressures of the foot plantar region were analyzed on three different mat hardnesses —soft and hard polyurethane foam mats and air chamber mats—by in-shoe instrumented insoles. Running on soft polyurethane foam mats presented reduced maximum forces in the whole plantar region and mainly peak pressures in the anterior part of the foot plantar region, such as the toes and first to fourth metatarsal heads, compared to hard polyurethane foam and air chamber mats. The peak pressure in the fifth metatarsal head was specifically reduced during running on soft compared to hard polyurethane foam mats, and running on these soft mats decreased calcaneus peak pressures compared to running on air chamber mats. Running on air chamber mats increased peak plantar pressures in the first metatarsal head compared to running on hard polyurethane foam mats. The mechanical behavior of mats of different hardness could be used to adjust the degree of impact on plantar pressures to determine the most appropriate materials and hardness for running.

Instrumented insoles for assessment of gait in patients with vestibular schwannoma.

Imbalance and gait disturbances are common in patients with vestibular schwannoma (VS) and can result in significant morbidity. Current methods for quantitative gait analysis are cumbersome and difficult to implement. Here, we use custom-engineered instrumented insoles to evaluate the gait of patients diagnosed with VS. Twenty patients with VS were recruited from otology, neurosurgery, and radiation oncology clinics at a tertiary referral center. Functional gait assessment (FGA), 2-minute walk test (2MWT), and uneven surface walk test (USWT) were performed. Custom-engineered instrumented insoles, equipped with an 8-cell force sensitive resistor (FSR) and a 9-degree-of-freedom inertial measurement unit (IMU), were used to collect stride-by-stride spatiotemporal gait parameters, from which mean values and coefficients of variation (CV) were determined for each patient. FGA scores were significantly correlated with gait metrics obtained from the 2MWT and USWT, including stride length, stride velocity, normalized stride length, normalized stride velocity, stride length CV, and stride velocity CV. Tumor diameter was negatively associated with stride time and swing time on the 2MWT; no such association existed between tumor diameter and FGA or DHI. Instrumented insoles may unveil associations between VS tumor size and gait dysfunction that cannot be captured by standardized clinical assessments and self-reported questionnaires.

A Wearable Biofeedback Device for Monitoring Tibial Load During Partial Weight-Bearing Walking.

Patients with tibial fractures are usually advised to follow a partial weight-bearing gait rehabilitation program after surgery to promote bone healing and lower limb functional recovery. Currently, the biofeedback devices used for gait rehabilitation training in fracture patients use ground reaction force (GRF) as the indicator of tibial load. However, an increasing body of research has shown that monitoring GRF alone cannot objectively reflect the load on the lower limb bones during human movement. In this study, a novel biofeedback system was developed utilizing inertial measurement units and custom instrumented insoles. Based on the data collected from experiments, a hybrid approach combining a physics-based model and neural network architectures was used to predict tibial force. Compared to the traditional physics-based algorithm, the physical guided neural networks method showed better predictive performance. The study also found that regardless of the type of weight-bearing walking, the peak tibial force was significantly higher than the peak tibial GRF, and the time at which the peak tibial compression force occurs may not be consistent with the time at which the peak vertical GRF occurs. This further supports the idea that during gait rehabilitation training for patients with tibial fractures, monitoring and providing feedback on the actual tibial force rather than just the GRF is necessary. The developed device is a non-invasive and reliable portable device that can provide audio feedback, providing a viable solution for gait rehabilitation training outside laboratory and helping to optimize patients' rehabilitation treatment strategies.

Accurate COP Trajectory Estimation in Healthy and Pathological Gait Using Multimodal Instrumented Insoles and Deep Learning Models.

Measuring center-of-pressure (COP) trajectories in out-of-the-lab environments may provide valuable information about changes in gait and balance function related to natural disease progression or treatment in neurological disorders. Traditional equipment to acquire COP trajectories includes stationary force plates, instrumented treadmills, electronic walkways, and insoles featuring high-density force sensing arrays, all of which are expensive and not widely accessible. This study introduces novel deep recurrent neural networks that can accurately estimate dynamic COP trajectories by fusing data from affordable and heterogeneous insole-embedded sensors (namely, an eight-cell array of force sensitive resistors (FSRs) and an inertial measurement unit (IMU)). The method was validated against gold-standard equipment during out-of-the-lab ambulatory tasks that simulated real-world walking. Root-mean-square errors (RMSE) in the mediolateral (ML) and anteroposterior (AP) directions obtained from healthy individuals (ML: 0.51 cm, AP: 1.44 cm) and individuals with neuromuscular conditions (ML: 0.59 cm, AP: 1.53 cm) indicated technical validity. In individuals with neuromuscular conditions, COP-derived metrics showed significant correlations with validated clinical measures of ambulatory function and lower-extremity muscle strength, providing proof-of-concept evidence of the convergent validity of the proposed method for clinical applications.

Tracking Day-To-Day Achilles Tendon Loading Progression During Rupture Recovery: A Case Study

Category: Sports; Basic Sciences/Biologics Introduction/Purpose: Achilles tendon rupture patients often suffer long-term functional deficits. Although progressive tendon loading improves healing, it is difficult to reliably measure in the real world and thus be precisely prescribed in rehabilitation. Modern wearable sensors, including instrumented shoe insoles, provide powerful platforms to measure real-world biomechanics. Using simple algorithms, we showed that these instrumented insoles can accurately quantify Achilles tendon loads. Yet, the feasibility of using wearables to enhance targeted Achilles tendon therapeutics remain unknown, partly due to the challenges of obtaining data on many patients over the long recovery process. Therefore, we leveraged a case study to verify whether instrumented insoles are able to track the longitudinal progression of Achilles tendon loading in daily life, and relate to clinically relevant events of tendon health. Methods: A patient (F, 30y/o) who underwent a right Achilles tendon repair was enrolled after consent in this IRB-approved study. During a standard rehabilitation protocol, an immobilizing boot fit with a 3-sensor instrumented insole (Loadsol) was worn starting from week 4 post-surgery. The insole remained in footwear after switching from boot to shoe, and data collection continued until week 22. The patient recorded &gt;10 seconds of insole data during gait each day, logged daily step count, and documented any event that may be related to tendon health, including pain and notable daily living activities. For each step identified by insole, we estimated Achilles tendon loads using our established algorithm, including load peak, impulse over a step, and load rates. We compared the daily average of these variables to self-reported events to explore their inter-relationships. We also correlated the load variables to determine whether they each provide unique insights into tendon loading. Results: The insole recorded up to 70 steps each day on a total of 116 days. Peak Achilles tendon load increased gradually yet non-linearly along the recovery process, with large variations between week 12-19 (Figure). 'Sharp' loading changes often corresponded to tendon health-related events, such as days of pain and swelling following rapid load increases in late week 12. A day of high step count (20k+) in week 18 was followed by a large decrease of the peak tendon load at the end of the same day. The insole also identified tendon load changes according to patient instructions, including a 65% increase in peak load by intentionally 'trying to push off' during gait. Finally, tendon load rates and impulse strongly correlated with peak load (R2 &gt;0.85). Conclusion: Longitudinally monitoring Achilles tendon loading is challenging because 1) it is limited by the scarce frequency of clinical visits, and 2) lab-based gait measurements do not faithfully reflect real-world biomechanics. Our first-of-kind case study shows the feasibility of using instrumented insoles to track day-to-day Achilles tendon loading in the real world. The results suggest Achilles tendon loading can be sufficiently tracked using peak load, and modified instantly and interactively via instructions or biofeedback. Our innovative paradigm empowers future studies to leverage accessible tools and deliver personalized rehabilitation according to quantitative guidance, thereby optimizing long-term Achilles tendon healing and functional recovery.

Ecological Validation of Machine Learning Models for Spatiotemporal Gait Analysis in Free-Living Environments Using Instrumented Insoles

Continuous gait monitoring may aid the early diagnosis of neurological and musculoskeletal conditions and help validate the effectiveness of new treatments. However, consumer-grade activity trackers can only capture summary gait metrics, whereas most of the research-grade devices capable of estimating fine-grained gait parameters are too cumbersome for extended-time use in real-life environments. Instrumented footwear may offer a promising alternative tool owing to their good accuracy and relatively small form factor, but their ability to detect stride-by-stride spatial gait parameters in free-living conditions has not been well explored to date. This work describes machine learning (ML) inference models for an insole system capable of accurately estimating stride time (ST), length (SL), and velocity (SV) in real-life environments. Functional validity was assessed through unstructured tests including straight-line walking, curve walking, and turns. Ecological validity was examined in free-living conditions. The ML models demonstrated better accuracy than conventional data processing methods (mean absolute errors in unstructured conditions were 3.55% for SL and 3.59% for SV). Real-life gait parameters estimated with the ML models showed stronger associations with a standardized walking test compared with the same parameters obtained with conventional methods. These results indicate proof-of-concept feasibility of using instrumented insoles and ML inference models for free-living spatiotemporal gait analysis.