• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.7
• /
• pp.1389-1397
• /
• 2016

This study was to evaluate opportunities for the commercialization of smart insole. smart technology is evolving to Insole. Pressure-sensitive sensor or an acceleration sensor is applied to create a balance of the feet and body, is also evolving for entertainment (sports, entertainment, etc.) and health care. Moreover, smart insole can fix an incorrect walking habit by sending a weight value measured by the sensor on a smartphone and during the movement, smart insole helps to correct body balance by measuring the center of gravity moving condition. However, smart tendency of the insole has yet to create a clear boundary in the entertainment and healthcare markets. This is because the fitness band, smart socks, smart shoes can also replace the benefits of a smart insole. Interestingly, the business opportunities are appearing more frequently in health care solution service of electrocardiogram, body temperature, blood pressure, etc., rather than smart devices.

• Journal of Korean Physical Therapy Science
• /
• v.29 no.1
• /
• pp.30-40
• /
• 2022

Background: This study analyzed the reliability of smart guides for gait analysis in patients with stroke. Design: Cross-sectional study. Methods: The participants of the study were 30 patients with stroke who could walk more than 10 m and had an MMSE-K test score of ≥24. Prior to the experiment, the subjects or their guardians entered their demographic characteristics including gender, age, height, weight into the prepared computer. The experiment was conducted in a quiet, comfortable, and independent location, and the patient was reminded of the equipment description, precautions, and safety rules for walking. A smart insole was inserted into the shoes of the patients and the shoes were put on before the patients walked three times on the 5-m gait analysis system mat installed in the laboratory. Results: The reliability of the equipment was compared with that of the gait analysis system, and the results of this study are as follows: among the gait analysis items, velocity had an ICC=0.982, the cadence had an ICC=0.905, the swing phase on the side of the gait cycle had an ICC=0.893, the swing phase on the side of the gait had an ICC=0.839, that on the non-affected side had an ICC=0.939, single support on the affected side had an ICC=0.812, and support on the non-affected side had an ICC=0.767. Conclusion: The results of this study indicate no statistical difference between the smart insole and the gait analysis system. Therefore, it is believed that real-time gait analysis through smart insole measurement could help patients in rehabilitation.

• International journal of advanced smart convergence
• /
• v.9 no.1
• /
• pp.202-208
• /
• 2020

Body balance necessary for ordinary daily activities can be undermined by diverse causes. In this study, as a way to control such a problem, we have produced smart insole as a wearable device in the form of insole and developed analysis software evaluating body balance, which measures ground reaction force applied to each area of sole and Center of Pressure (COP). The software visualized changes in COP positions while a user was moving and average COP positions, and it is also capable of measuring the COP values in the Anterior-Posterior (AP) and Medial-Lateral (ML) areas of feet. Through gait analysis, it can analyze the time of walking, strides, speed, COP trajectory while walking, etc. In addition, we have developed training contents for body balance improvement designed in consideration of Y-Balance Test and Timed Up and Go (TUG) Test. They were established in virtual reality similar to daily living environment so that people can expect more effective training results regardless of places.

• Physical Therapy Rehabilitation Science
• /
• v.11 no.2
• /
• pp.215-223
• /
• 2022

Objective: This study was to resolve the limitations of the experimental environment and to solve the shortcomings of the method of measuring human gait characteristics using optical measuring instruments. Design: A cross-sectional study. Methods: Fifteen healthy adults without a history of orthopedic surgery on the lower extremities for the past 6 months were participated. They were analyzed gait variables using the smart guide and the 3D image analysis at the same time, and their results were compared. Visual-3D was used to calculate the analysis variables. Results: The reliability and validity of the data according to the two measuring instruments were found to be very high; gait speed(0.85), cycle time(0.99), stride time of both feet(0.98, 0.97) stride legnth of both feet(0.86, 0.88) stride per minute of both feet(0.99, 0.96), foot speed of both feet(0.90, 0.91), step time of both feet(0.77, 0.71), step per minute(0.72, 0.74), stance time of both feet(0.96, 0.97), swing time of both feet(0.93, 0.79), double step time(0.81), initial double step time(0.84) and terminal step time(0.76). Conclusions: In the case of the smart insole, which measures human gait variables using the pressure sensor and inertial sensor inserted in the insole, the reliability and validity of the measured data were found to be very high. It can be used as a device to replace 3D image analysis when measuring pathological gait.

• Composites Research
• /
• v.34 no.6
• /
• pp.400-405
• /
• 2021

In this study, a composite material frame was designed to increase the energy harvesting efficiency of polyvinylidene fluoride (PVDF) ribbon harvesters which are installed inside smart shoes. In order to minimize the amount of deformation in the load direction of the frame, it was designed using carbon continuous fiber composites and its complex shaped structure was manufactured using a 3D printer. In order to calculate the amount of deformation of the insole and midsole of the shoes under the condition of the load generated during walking, the insole and midsole were modeled using the distributed spring elements. Using finite element analysis, the elongation of ribbon-type harvesters mounted on smart shoes was calculated during walking. It is expected that the predicted elongation of the harvester can be utilized to increase the energy harvesting efficiency of smart shoes.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.2
• /
• pp.17-26
• /
• 2018

In this paper, we propose a gait type classification method based on pressure sensor which reflects various terrain and velocity variations. In order to obtain stable gait classification performance, we divide the whole gait data into several steps by detecting the swing phase, and normalize each step. Then, we extract robust features for both topographic variation and speed variation by using the Null-LDA(Null-Space Linear Discriminant Analysis) method. The experimental results show that the proposed method gives a good performance of gait type classification even though there is a change in the gait velocity and the terrain.

• ETRI Journal
• /
• v.42 no.1
• /
• pp.46-53
• /
• 2020

Gait analysis is an effective clinical tool across a wide range of applications. Recently, inertial measurement units have been extensively utilized for gait analysis. Effective gait analyses require good estimates of heel-strike and toe-off events. Previous studies have focused on the effective device position and type of triaxis direction to detect gait events. This study proposes an effective heel-strike and toe-off detection algorithm using a smart insole with inertial measurement units. This method detects heel-strike and toe-off events through a time-frequency analysis by limiting the range. To assess its performance, gait data for seven healthy male subjects during walking and running were acquired. The proposed heel-strike and toe-off detection algorithm yielded the largest error of 0.03 seconds for running toe-off events, and an average of 0-0.01 seconds for other gait tests. Novel gait analyses could be conducted without suffering from space limitations because gait parameters such as the cadence, stance phase time, swing phase time, single-support time, and double-support time can all be estimated using the proposed heel-strike and toe-off detection algorithm.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.47 no.1
• /
• pp.110-122
• /
• 2023

The COVID-19 pandemic increased people's time at home and caused an 80% increase in noise disputes between floors. The purpose of this study is to propose suitable materials for making indoor shoes (insoles) to minimize noise between floors. Subjects without back pain and leg-related disease (e.g. arthritis, etc.) from three different age groups (childhood, adolescence, and adulthood) were recruited for the study. Five polymer insole materials were considered: Chloroprene Rubber (CR foam), Ethylene Propylene Diene Monomer (EPDM foam), Natural Latex foam, Ethylene Vinyl Acetate (EVA foam), and Polyurethane (PU foam). From these materials, 20 combinations were prepared and randomly tested for noise and vibration. The results revealed a significant difference in noise and vibration levels based on the type of material used and the age of the subject. Nevertheless, all materials under consideration successfully reduced noise and vibration; in particular, type A-C greatly decreased. The CR foam material was especially effective at noise and vibration reduction (p<.01). This study suggests that adding insoles into socks that children wear at home could reduce noise vibration and disputes between floors.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.11
• /
• pp.29-38
• /
• 2022

This paper proposes a system for classifying gait types using an ensemble deep learning network for gait data measured by a smart insole equipped with multi-sensors. The gait type classification system consists of a part for normalizing the data measured by the insole, a part for extracting gait features using a deep learning network, and a part for classifying the gait type by inputting the extracted features. Two kinds of gait feature maps were extracted by independently learning networks based on CNNs and LSTMs with different characteristics. The final ensemble network classification results were obtained by combining the classification results. For the seven types of gait for adults in their 20s and 30s: walking, running, fast walking, going up and down stairs, and going up and down hills, multi-sensor data was classified into a proposed ensemble network. As a result, it was confirmed that the classification rate was higher than 90%.

• Journal of The Korean Society of Integrative Medicine
• /
• v.9 no.4
• /
• pp.291-298
• /
• 2021

Purpose: The Pedisole is a newly developed shoe-mounted wearable assessment system for analyzing balance and gait. This study aimed to determine the reliability and validity of the parameters provided by the system for static balance and gait analysis of healthy adults. Methods: This study included 38 healthy adults (22.4±1.9 years) with no history of injury in the lower limbs. All participants were asked to perform balance and gait tasks for undertaking measurements. For analysis of balance, both the smart Pedisole and Pedoscan systems were concurrently used to analyze the path length of the center of pressure (COP) and the weight ratio of the left and right for 10 s. Gait was measured using the smart Pedisole and GaitRite walkway systems simultaneously. The participants walked at a self-selected preferred gait speed. The cadence, stance time, swing time, and step time were used to analyze gait characteristics. Using the paired t-test, the intra-class coefficient correlation (ICC) was calculated for reliability. The Spearman correlation was used to assess the validity of the measurements. In total, data for balance from 36 participants and the gait profiles of 37 participants were evaluated. Results: There were significant differences between the COP path lengths (p<.050) derived from the two systems, and a significant correlation was found for COP path length (r=.382~.523) for static balance. The ICC for COP path length and weight ratio was found to be greater than .687, indicating moderate agreement in balance parameters. The ICC of gait parameters was found to be greater than .697 except for stance time, and there was significant correlation (r=.678~.922) with the GaitRite system. Conclusion: The newly developed smart insole-type Pedisole system and the related application are useful, reliable, and valid tools for balance and gait analysis compared to the gold standard Pedoscan and the GaitRite systems in healthy individuals.