A Novel Tool for Gait Analysis: Validation Study of the Smart Insole PODOSmart®

Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.

Validation of the Short Physical Performance Battery via Plantar Pressure Analysis Using Commercial Smart Insoles

This cross-sectional study, conducted at a tertiary care hospital's rehabilitation clinic, aimed to validate Short Physical Performance Battery (SPPB) results obtained through plantar pressure analysis using commercial smart insoles (SPPB-SI) and to compare these results to manually acquired results by an experienced examiner (SPPB-M). This study included 40 independent-walking inpatients and outpatients aged 50 or older. SPPB-SI and SPPB-M were administered concurrently, with the smart insoles providing plantar pressure data that were converted into time-pressure curves. Two interpreters assessed the curves, determining component completion times for the SPPB-SI scores. Among the 40 participants (mean age: 72.98, SD: 9.27), the mean total SPPB-SI score was 7.72 ± 2.50, and the mean total SPPB-M score was 7.95 ± 2.63. The time recordings and measured scores of each SPPB-SI component exhibited high reliability with inter- and intra-interpreter correlation coefficients of 0.9 and 0.8 or higher, respectively. The intraclass correlation coefficient between the total SPPB-SI and SPPB-M scores was 0.831 (p < 0.001), and that between the component scores of the two measurements ranged from 0.837 to 0.901 (p < 0.001). Consistent correlations with geriatric functional parameters were observed for both SPPB-SI and SPPB-M. This study underscores the potential of commercial smart insoles as reliable tools for conducting SPPB assessments.

The Use of Embedded IMU Insoles to Assess Gait Parameters: A Validation and Test-Retest Reliability Study

Wireless wearable insoles are interesting tools to collect gait parameters during daily life activities. However, studies have to be performed specifically for each type of insoles on a big data set to validate the measurement in ecological situations. This study aims to assess the criterion validity and test-retest reliability of gait parameters from wearable insoles compared to motion capture system. Gait of 30 healthy participants was recorded using DSPro® insoles and a motion capture system during overground and treadmill walking at three different speeds. Criterion validity and test-retest reliability of spatio-temporal parameters were estimated with an intraclass correlation coefficient (ICC). For both systems, reliability was found higher than 0.70 for all variables (p < 0.001) except for minimum toe clearance (ICC < 0.50) with motion capture system during overground walking. Regardless of speed and condition of walking, Speed, Cadence, Stride Length, Stride Time and Stance Time variables were validated (ICC > 0.90; p < 0.001). During walking on treadmill, loading time was not validated during slow speed (ICC < 0.70). This study highlights good criterion validity and test-retest reliability of spatiotemporal gait parameters measurement using wearable insoles and opens a new possibility to improve care management of patients using clinical gait analysis in daily life activities.

Conventional physical therapy combined with extracorporeal shock wave leads to positive effects on spasticity in stroke survivors: a prospective observational study

The study aimed to evaluate the effectiveness of radial extracorporeal shock wave therapy (rESWT) and conventional physical therapy (CPT) protocol on the gait pattern in stroke survivors through a new gait analysis technology. Fifteen (n=15) stroke survivors took part in this prospective, observational study and were assessed clinically and through an instrumented treadmill before and after rESWT and CPT. Spasticity grade 95% CI 0.93 (0.79 +/- 1.08), pain intensity 95% CI 1.60 (1.19 +/- 2.01), and clonus score decreased significantly 95% CI 1.13 (0.72 +/- 1.54). The sensorimotor function 95% CI -2.53 (-3.42 +/- 1.65), balance 95% CI -5.67 (-6.64 +/- - 4.69), and gait parameters were enhanced at the end of the program. Step length 95% CI -3.47 (-6.48 +/- 0.46) and step cycle were improved 95% CI -0.09 (-0.17 +/- -0.01), and hip 95% CI -3.90 (-6.92 +/- -0.88), knee 95% CI -2.08 (-3.84 +/- -0.32) and ankle flexion-extension 95% CI -2.08 (-6.64 +/- -4.69) were augmented. Adding the quantitative analysis to the clinical assessment, we gained easy access to track progress and obtained an individualized therapeutic approach for stroke survivors.

Modeling and Calibration of Pressure-Sensing Insoles via a New Plenum-Based Chamber

This paper proposes a novel method to reliably calibrate a pair of sensorized insoles utilizing an array of capacitive tactile pixels (taxels). A new calibration setup is introduced that is scalable and suitable for multiple kinds of wearable sensors and a procedure for the simultaneous calibration of each of the sensors in the insoles is presented. The calibration relies on a two-step optimization algorithm that, firstly, enables determination of a relevant set of mathematical models based on the instantaneous measurement of the taxels alone, and, then, expands these models to include the relevant portion of the time history of the system. By comparing the resulting models with our previous work on the same hardware, we demonstrate the effectiveness of the novel method both in terms of increased ability to cope with the non-linear characteristics of the sensors and increased pressure ranges achieved during the experiments performed.

Responsive fungal insoles for pressure detection

Mycelium bound composites are promising materials for a diverse range of applications including wearables and building elements. Their functionality surpasses some of the capabilities of traditionally passive materials, such as synthetic fibres, reconstituted cellulose fibres and natural fibres. Thereby, creating novel propositions including augmented functionality (sensory) and aesthetic (personal fashion). Biomaterials can offer multiple modal sensing capability such as mechanical loading (compressive and tensile) and moisture content. To assess the sensing potential of fungal insoles we undertook laboratory experiments on electrical response of bespoke insoles made from capillary matting colonised with oyster fungi Pleurotus ostreatus to compressive stress which mimics human loading when standing and walking. We have shown changes in electrical activity with compressive loading. The results advance the development of intelligent sensing insoles which are a building block towards more generic reactive fungal wearables. Using FitzHugh-Nagumo model we numerically illustrated how excitation wave-fronts behave in a mycelium network colonising an insole and shown that it may be possible to discern pressure points from the mycelium electrical activity.

Accuracy of the fully integrated Insole3's estimates of spatiotemporal parameters during walking

This study investigated the accuracy of the Insole3 wireless shoe device in estimating several clinically useful spatiotemporal parameters (STPs). Eleven subjects walked at slow (0.8-1.0 m/s) and moderate-paced (1.2-1.4 m/s) speeds. Data were simultaneously recorded using the Insole3 and an industry-standard, three-dimensional motion capture (MOCAP) system. An error analysis compared the resulting STP data from the two systems. The mean bias error (MBE) was generally lower for temporal variables, and somewhat higher, but acceptable, for spatial variables. The MBE for temporally-related cadence and cycle time were the lowest (less than ±0.45%), with 100% (110/110) of slow-paced walking trial values and 99.1% (109/110) of moderate-paced walking trial values within 5% of the MOCAP estimates. The MBE was highest for speed (3.23-4.91%) and stride length (3.68-4.63%), with between 52.7 and 69.1% of trial values falling within the 5% error range. Stance time and swing time ranged between -0.98 and 4.38% error for both walking conditions. The results of this study suggest that the Insole3 is a potential alternative to MOCAP for estimating several STPs, namely cadence, stance time, and cycle time, particularly for use outside of the laboratory setting.

Feasibility and efficacy of an activity-monitoring approach using pedometer in patients undergoing subacute rehabilitation: A pilot study

Wearable devices for the quantification of walking have recently been adopted for gait rehabilitation. To apply this method in subacute rehabilitation settings, this approach must be effective in these populations and implemented as a feasible method in terms of adherence and safety, especially the risk of falling. This study aimed to investigate the feasibility and efficacy of an activity monitoring approach in subacute rehabilitation using a commercially available pedometer validated with slow walking. This randomized controlled study with blinded assessors recruited 29 patients admitted to a rehabilitation ward. The participants were randomly assigned to either the feedback (intervention) or the no-feedback (control) group. Participants in both groups received at least 120 min of therapy sessions every day for 6 or 7 days per week while wearing pedometers on their unaffected ankles from the day they were permitted to walk independently till discharge. Only participants in the feedback group received weekly encouragement and the next goals. The primary outcome was the change in the 6-minute walking distance (Δ6MD). Feasibility (percentage of pedometer data acquisition days in the total observational period and the number of falls) and other efficacy outcomes (step counts, gait speed, 30-seconds chair stand test, Berg Balance Scale, and Timed Up and Go Test) were also evaluated. Regarding feasibility outcomes, the data acquisition rate was 94.1% and the number of falls during the observation period was one in the feedback group. Regarding efficacy outcomes, Δ6MD was not significantly greater in the feedback group [mean (standard deviation): 79.1 (51.7) m] than in the no-feedback group [86.1 (65.4) m] (p = 0.774) and the other five secondary outcomes showed no between-group difference. Considering the large number of steps per day in both groups [6,912 (4,751) and 5,600 (5,108) steps in the feedback and no-feedback group, respectively], the effect of the intended intervention might have been masked by the effect of simply wearing pedometers in the control group. This study revealed that the activity monitoring approach using an ankle-worn pedometer was practical in terms of adherence and safety. Further clinical trials are required to elucidate ways to effectively use wearable devices in subacute rehabilitation.

A 3D-Printed Capacitive Smart Insole for Plantar Pressure Monitoring

Gait analysis refers to the systematic study of human locomotion and finds numerous applications in the fields of clinical monitoring, rehabilitation, sports science and robotics. Wearable sensors for real-time gait monitoring have emerged as an attractive alternative to the traditional clinical-based techniques, owing to their low cost and portability. In addition, 3D printing technology has recently drawn increased interest for the manufacturing of sensors, considering the advantages of diminished fabrication cost and time. In this study, we report the development of a 3D-printed capacitive smart insole for the measurement of plantar pressure. Initially, a novel 3D-printed capacitive pressure sensor was fabricated and its sensing performance was evaluated. The sensor exhibited a sensitivity of 1.19 MPa−1, a wide working pressure range (<872.4 kPa), excellent stability and durability (at least 2.280 cycles), great linearity (R2=0.993), fast response/recovery time (142−160 ms), low hysteresis (DH<10%) and the ability to support a broad spectrum of gait speeds (30−70 steps/min). Subsequently, 16 pressure sensors were integrated into a 3D-printed smart insole that was successfully applied for dynamic plantar pressure mapping and proven able to distinguish the various gait phases. We consider that the smart insole presented here is a simple, easy to manufacture and cost-effective solution with the potential for real-world applications.

Gait Improvement by Alerted Push-Off via Heating of Insole Tip

This study investigated the change in the joint angles of the lower limb during gait by heating the tip of the insole to make a conscious push-off with the warm part. Fifteen healthy males performed treadmill walking under three different conditions: CONTROL walked as usual, INST was instructed to extend the stride with a push-off from the ball of foot to the toe, and HEAT was asked to walk while attempting to push off the warm area, which was attached to the disposable warmer to the area from the ball of foot to the toe of the insole. A 3D-motion capture system with infrared cameras was used to analyze the gait. The hip joint angle increased significantly under the INST and HEAT. Although the ankle dorsi-flexion at heel strike did not differ significantly for these conditions, ankle plantar-flexion significantly increased at toe-off under the INST and HEAT. Especially, effect size (d) in increased plantar-flexion was large in HEAT (=1.50), whereas it was moderate in INST (=0.68). These results suggest that a heated stimulus during gait enhanced the consciousness of push-off and increased leg swing and ankle plantar-flexion during the terminal stance phase, which may increase the stride length.

Impact of Kinesiotherapy and Hydrokinetic Therapy on the Rehabilitation of Balance, Gait and Functional Capacity in Patients with Lower Limb Amputation: A Pilot Study

The purpose of this pilot study was to identify impact differences in the rehabilitation of balance, gait and functional capacity in patients with lower limb amputation performing hydrokinetic therapy and kinesiotherapy programs during the pre-prosthetic and prosthetic phases. The study included 16 male patients aged 40–60 years with amputated lower limbs for 6 to 12 months, which involved transfemoral amputation (TFA), transtibial amputation (TTA), traumatic and vascular amputation, who were divided into the following two groups: the hydrokinetic therapy (HKT) group and the kinesiotherapy (KT) group, named after the content of the rehabilitation programs that were implemented for 2 weeks in the pre-prosthetic and prosthetic periods. The initial and final evaluation of the participants included the following tests: the Berg Scale and the four square test for the evaluation of the balance; the PodoSmart device for gait assessment; through the walking test over 6 min, we evaluated the functional capacity. The results were processed in SPSS 24. Analysis of the results on balance rehabilitation through the Berg Scale highlighted that the progress related to the mean of the total score was 7.62 points, p = 0.00 for the HKT group and 7.50 points, p = 0.00 for the KT group, while in the four square step test, the mean of progress was 6.125 s, p = 0.00 for the HKT group and 6 s, p = 0.000 for the KT group. The PodoSmart gait analysis revealed that the HKT group showed a progress mean of 4.875%, p = 0.00, for the foot symmetry parameter, which was 1.875% less than the score achieved by the KT group whose symmetry progress mean was 6.75%, p = 0.00, while the average progress mean for the cadence parameter was 2.75 steps/min higher for the KT group than the HKT group. The comparative analysis of the impact of these two programs on the patients’ functional capacity indicated that the score recorded by the KT group was a progress mean of 15.12 m, p = 0.00 better than the HKT group for the travelled distance parameter; the implementation of the hydrokinetic therapy program led to better exercise adaptation for the HKT group compared to the KT group at an average HR (HRavg) with 0.50 BPM, p = 0.00. After analyzing the results, it has been found that hydrokinetic therapy programs have a greater impact on balance rehabilitation and exercise adaptation, while kinesiotherapy programs have a greater impact on gait rehabilitation and functional capacity optimization for the travelled distance parameter.

Detection of Human Gait Phases Using Textile Pressure Sensors: A Low Cost and Pervasive Approach

Human gait analysis is a standard method used for detecting and diagnosing diseases associated with gait disorders. Wearable technologies, due to their low costs and high portability, are increasingly being used in gait and other medical analyses. This paper evaluates the use of low-cost homemade textile pressure sensors to recognize gait phases. Ten sensors were integrated into stretch pants, achieving an inexpensive and pervasive solution. Nevertheless, such a simple fabrication process leads to significant sensitivity variability among sensors, hindering their adoption in precision-demanding medical applications. To tackle this issue, we evaluated the textile sensors for the classification of gait phases over three machine learning algorithms for time-series signals, namely, random forest (RF), time series forest (TSF), and multi-representation sequence learner (Mr-SEQL). Training and testing signals were generated from participants wearing the sensing pants in a test run under laboratory conditions and from an inertial sensor attached to the same pants for comparison purposes. Moreover, a new annotation method to facilitate the creation of such datasets using an ordinary webcam and a pose detection model is presented, which uses predefined rules for label generation. The results show that textile sensors successfully detect the gait phases with an average precision of 91.2% and 90.5% for RF and TSF, respectively, only 0.8% and 2.3% lower than the same values obtained from the IMU. This situation changes for Mr-SEQL, which achieved a precision of 79% for the textile sensors and 36.8% for the IMU. The overall results show the feasibility of using textile pressure sensors for human gait recognition.

Test-Retest Reliability of PODOSmart® Gait Analysis Insoles

It is recognized that gait analysis is a powerful tool used to capture human locomotion and quantify the related parameters. PODOSmart^® insoles have been designed to provide accurate measurements for gait analysis. PODOSmart^® insoles are lightweight, slim and cost-effective. A recent publication presented the characteristics and data concerning the validity of PODOSmart^® insoles in gait analysis. In literature, there is still no evidence about the repeatability of PODOSmart^® gait analysis system. Such evidence is essential in order to use this device in both research and clinical settings. The aim of the present study was to assess the repeatability of PODOSmart^® system. In this context, it was hypothesized that the parameters of gait analysis captured by PODOSmart^® would be repeatable. In a sample consisting of 22 healthy male adults, participants performed two walking trials on a six-meter walkway. The ICC values for 28 gait variables provided by PODOSmart^® indicated good to excellent test-retest reliability, ranging from 0.802 to 0.997. The present findings confirm that PODOSmart^® gait analysis insoles present excellent repeatability in gait analysis parameters. These results offer additional evidence regarding the reliability of this gait analysis tool.