Wearable Insole Research Articles

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Stroke is a health problem experienced by many elderly people around the world. Stroke has a devastating effect on quality of life, causing death or disability. Hemiplegia is clearly an early sign of a stroke and can be detected through patterns of body balance and gait. The goal of this study was to determine various feature vectors of foot pressure and gait parameters of patients with stroke through the use of a wearable sensor and to compare the gait parameters with those of healthy elderly people. To monitor the participants at all times, we used a simple measuring device rather than a medical device. We measured gait data of 220 healthy people older than 65 years of age and of 63 elderly patients who had experienced stroke less than 6 months earlier. The center of pressure and the acceleration during standing and gait-related tasks were recorded by a wearable insole sensor worn by the participants. Both the average acceleration and the maximum acceleration were significantly higher in the healthy participants (p < .01) than in the patients with stroke. Thus gait parameters are helpful for determining whether they are patients with stroke or normal elderly people.

Are wearable insoles a validated tool for quantifying transfemoral amputee gait asymmetry?

Amputee gait is known to be asymmetrical, especially during loading of the lower limb. Monitoring asymmetry could be useful in quantifying patient performance during rehabilitation. Wearable insoles can provide normal ground reaction force asymmetry in real-life conditions. To characterize the validity of Loadsol® insoles versus force plates in quantifying normal ground reaction force and gait asymmetry. To determine the influence walking speed has on loading asymmetry in transfemoral amputees. This is a prospective study. Six transfemoral amputees, wearing Loadsol® insoles, walked at three self-selected speeds on force plates. Validity was assessed by comparing normal ground reaction force data from the insoles and force plates. The Absolute Symmetry Index was used to calculate gait loading asymmetry at each speed. Normalized root mean square errors for the normal ground reaction forces were 6.6% (standard deviation = 2.3%) and 8.9% (standard deviation = 3.8%); correlation coefficients were 0.91 and 0.95 for the prosthetic and intact limb, respectively. The mean error for Absolute Symmetry Index parameters ranged from -2.67% to 4.35%. Loading asymmetry increased with walking speed. This study quantified the validity of Loadsol® insoles in assessing loading asymmetry during gait in transfemoral amputees. The calibration protocol could be improved to better integrate it into a clinical setting. However, our results support the relevance of using such insoles during the clinical follow-up of transfemoral amputees. This is the first study to validate Loadsol® insoles versus force plates and report on loading asymmetry during gait at three different speeds in transfemoral amputees. Loadsol® insoles, which provide visual and audio feedback, are clinically easy to use and could have beneficial application in the amputee's rehabilitation and follow-up.

Mediolateral stability index as a biomarker for Parkinson's disease progression: A graph connectivity based approach.

This paper presents a novel metric to serve as a bio-marker for understanding and detecting progression of Parkinson's disease (PD). Proposed metric, termed as `Mediolateral Stability (MLS) index' has been derived from processing insole gait data using graph connectivity analysis. The proposed metric focuses on variability of mediolateral pressure in foot during gait progression. Vertical Ground Reaction Force (VGRF) and stride time information derived from a wearable insole for PD subjects as well as healthy controls are processed to create a connectivity graph. The insole contains eight pressure sensitive sensors for each foot and these sensors serve as the nodes of the connectivity graph. The proposed MLS index shows significant difference (p <; 0.05) in between control and PD groups and also in between progressive stages of PD, such as mild and moderate PD groups with p <; 0.05. Proposed graph connectivity based feature can act as a bio marker to correctly classify PD, identify early onset of PD and trace changes due to disease progression and can also provide information about dynamic pressure distribution during gait.

Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers

Abstract Awkward working postures are the main risk factor for work-related musculoskeletal disorders (WMSDs) causing non-fatal occupational injuries among construction workers. However, it remains a challenge to use existing risk assessment methods for detecting and classifying awkward working postures because these methods are either intrusive or rely on subjective judgment. Therefore, this study developed a novel and non-invasive method to automatically detect and classify awkward working postures based on foot plantar pressure distribution data measured by a wearable insole pressure system. Ten asymptomatic participants performed five different types of awkward working postures (i.e., overhead working, squatting, stooping, semi-squatting, and one-legged kneeling) in a laboratory setting. Four supervised machine learning classifiers (i.e., artificial neural network (ANN), decision tree (DT), K-nearest neighbor (KNN), and support vector machine (SVM)) were used for classification performance using a 0.32 s window size. Cross-validation results showed that the SVM classifier (i.e., the best classifier) obtained a classification performance with an accuracy of 99.70% and a sensitivity of each awkward working posture was above 99.00% at 0.32 s window size. The findings substantiated that it is feasible to use a wearable insole pressure system to identify risk factors for developing WMSDs, and could help safety managers to minimize workers' exposure to awkward working postures.

Fall risk assessment of construction workers based on biomechanical gait stability parameters using wearable insole pressure system

Falls on the same level are a leading cause of non-fatal injuries in the construction industry, and loss of balance events are the primarily contributory risk factors associated with workers’ fall injuries. Previous studies have indicated that changes in biomechanical gait stability parameters provide substantial safety gait metrics for assessing workers’ fall risks. However, scant research has been conducted on changes in biomechanical gait stability parameters based on foot plantar pressure patterns to assess workers’ fall risks. This research examined the changes in spatial foot regions and loss of balance events associated with biomechanical gait stability parameters based on foot plantar pressure patterns measured by wearable insole pressure system. To test the hypotheses of this study, ten asymptomatic participants conducted laboratory simulated loss of balance events which are often initiated by extrinsic fall risk factors. Our results found: (1) statistically significant differences in biomechanical gait stability parameters between spatial foot regions, especially with the peak pressure parameter; and (2) statistically significant differences in biomechanical gait stability parameters between loss of balance events when compared to normal gait (baseline), especially with the pressure-time integral parameter. Overall, the findings of this study not only provide useful safety gait metrics for early detection of specific spatial foot regions but also allow safety managers to understand the mechanism of loss of balance events in order to implement proactive fall-prevention strategies.

Automated detection and classification of construction workers' loss of balance events using wearable insole pressure sensors

Abstract Fall on the same level is the leading cause of non-fatal injuries in construction workers; however, identifying loss of balance events associated with specific unsafe surface conditions in a timely manner remain challenging. The objective of the current study was to develop a novel method to detect and classify loss of balance events that could lead to falls on the same level by using foot plantar pressure distributions data captured from wearable insole pressure sensors. Ten healthy volunteers participated in experimental trials, simulating four major loss of balance events (e.g., slip, trip, unexpected step-down, and twisted ankle) to collect foot plantar pressure distributions data. Supervised machine learning algorithms were used to learn the unique foot plantar pressure patterns, and then to automatically detect loss of balance events. We compared classification performance by varying window sizes, feature groups and types of classifiers, and the best classification accuracy (97.1%) was achieved when using the Random Forest classifier with all feature groups and a window size of 0.32 s. This study is important to researchers and site managers because it uses foot plantar pressure distribution data to objectively distinguish various potential loss of balance events associated with specific unsafe surface conditions. The proposed approach can allow practitioners to proactively conduct automated fall risk monitoring to minimize the risk of falls on the same level on sites.

Reliability quantification and gait loading asymmetry assessment with wearable insoles in transfemoral amputees

Gait defaults of amputees are known to be increased in difficult daily living walking conditions. Among quantitative parameters, load asymmetry is frequently used to assess gait quality. Replication of realistic daily living environment in a motion analysis laboratory (MAL) is difficult. Wearable pressure insoles, by providing normal ground reaction force (NGrF), can be used to quantify load asymmetry in real life conditions. This indicator is useful for physicians to monitor the rehabilitation progress or the prosthetic fitting suitability. The aim of the study was to quantify the reliability of NGrF measurement with Pedoped ® insoles, which gives NGrF in real time with biofeedback, and to assess gait asymmetry in transfemoral (TF) amputees at different walking speeds with the insoles. This prospective study included 5 TF amputees. In a MAL, they walked with their own prosthetic devices at three self-selected speeds on level ground, wearing Pedoped ® insoles. Reliability was assessed by comparing NGrF obtained from both systems using Bland-Altman plots and normalized RMSE (NRMSE). Load asymmetry between prosthetic and intact limb at the three different speeds was computed by Absolute Symmetry Index (ASI) on the following parameters: first and second maxima of NGrF and stance phase duration. The mean error was also assessed for ASI parameters computed with both systems. NRMSE was 7.2% (± 2.8%) for the prosthetic side and 9.8% ( ± 3.5%) for the intact side. Stance phase duration for both systems was consistently similar. The Bland and Altmann plots showed over 95% of the values between the limits of agreement. Mean error of ASI parameters ranged from, 6.2% to 1.4%, with an insole overestimation. All ASI were positive showing a higher value of the parameters of the intact limb compared to the prosthetic limb. Load asymmetry significantly increased with walking speed. The results suggested that the insoles could be used to evaluate load asymmetry using ASI parameters showing the overload on the intact limb, which is consistent with the literature. The biofeedback of NGrF in real time opens perspectives for the improvement of rehabilitation techniques. Connected auto-rehabilitation could be very useful to enhance the recovering of loading symmetry during gait.

User‐driven treadmill using walking speed estimated from plantar pressure sensor

A user-driven treadmill is a walking platform that is put into motion by the action of a user. The estimation of walking speed plays an important role in controlling the treadmill, especially the ones with a small walking area. Plantar pressure potentially has a significant relationship with walking speed. A new walking speed estimation and treadmill control algorithm using plantar data, which is measured using wearable insole pressure sensors is proposed. After capturing the best pressure parameters and developing an appropriate feedforward control scheme, the user experiment and analysis results are analysed to verify the performance and effectiveness of the proposed method.

Reliability quantification and gait loading asymmetry assessment with wearable insoles in transfemoral amputee people at different speeds

Introduction Amputee people have gait defaults, as for example loading asymmetry, which increase with daily living situations. Replication of realistic daily living environment in a motion analysis laboratory (MAL) is difficult. Wearable pressure insoles, by providing normal ground reaction force (NGrF), can be used to quantify loading gait asymmetry in real life conditions. This asymmetry, considered as an indicator of the quality of the gait, is useful for physicians to monitor the rehabilitation progress or the prosthetic fitting suitability. The study aimed at quantifying the reliability of NGrF measurement and assessing the gait asymmetry of transfemoral amputee people with Pedoped ® insoles against force plates. Walking speed effect was also evaluated on gait asymmetry for transfemoral amputee people (TFP). Material and methods In a MAL, five active TFP walked at three self-selected speeds on level ground wearing Pedoped ® insoles. Reliability was assessed by comparing NGrF obtained from both systems with Bland-Altman plots, normalized RMSE (NRMSE) and correlation coefficient. Gait loading asymmetry was computed by Absolute Symmetry Index in loading at the three self-selected speeds with insoles. Results The mean NRMSE of NGrF was 7.2% (± 2.8%) and 9.8% (± 3.5%); and coefficient correlation was 0.91 and 0.95 for the prosthetic side and the intact side respectively. Loading asymmetry increased significantly with walking speed for each specific variable of NGrF. Discussion–conclusion After overcoming the calibration problem in amputee population, Pedoped ® insoles could be easily used for gait asymmetry follow-up during rehabilitation.

A Self-Powered Insole for Human Motion Recognition.

Biomechanical energy harvesting is a feasible solution for powering wearable sensors by directly driving electronics or acting as wearable self-powered sensors. A wearable insole that not only can harvest energy from foot pressure during walking but also can serve as a self-powered human motion recognition sensor is reported. The insole is designed as a sandwich structure consisting of two wavy silica gel film separated by a flexible piezoelectric foil stave, which has higher performance compared with conventional piezoelectric harvesters with cantilever structure. The energy harvesting insole is capable of driving some common electronics by scavenging energy from human walking. Moreover, it can be used to recognize human motion as the waveforms it generates change when people are in different locomotion modes. It is demonstrated that different types of human motion such as walking and running are clearly classified by the insole without any external power source. This work not only expands the applications of piezoelectric energy harvesters for wearable power supplies and self-powered sensors, but also provides possible approaches for wearable self-powered human motion monitoring that is of great importance in many fields such as rehabilitation and sports science.

Ambulatory activity classification with dendogram-based support vector machine: Application in lower-limb active exoskeleton

Ambulatory activity classification is an active area of research for controlling and monitoring state initiation, termination, and transition in mobility assistive devices such as lower-limb exoskeletons. State transition of lower-limb exoskeletons reported thus far are achieved mostly through the use of manual switches or state machine-based logic. In this paper, we propose a postural activity classifier using a ‘dendogram-based support vector machine’ (DSVM) which can be used to control a lower-limb exoskeleton.A pressure sensor-based wearable insole and two six-axis inertial measurement units (IMU) have been used for recognising two static and seven dynamic postural activities: sit, stand, and sit-to-stand, stand-to-sit, level walk, fast walk, slope walk, stair ascent and stair descent. Most of the ambulatory activities are periodic in nature and have unique patterns of response. The proposed classification algorithm involves the recognition of activity patterns on the basis of the periodic shape of trajectories. Polynomial coefficients extracted from the hip angle trajectory and the centre-of-pressure (CoP) trajectory during an activity cycle are used as features to classify dynamic activities.The novelty of this paper lies in finding suitable instrumentation, developing post-processing techniques, and selecting shape-based features for ambulatory activity classification. The proposed activity classifier is used to identify the activity states of a lower-limb exoskeleton. The DSVM classifier algorithm achieved an overall classification accuracy of 95.2%.

Use of Ecological Gestures in Soccer Games Running on Mobile Devices

The strong integration of “intelligent mobile devices” into modern societies offers a great potential for a wide spread distribution of mobile serious games. As in the case of Virtual Reality based systems, in order to be useful and efficient, these serious games need to be validated ecologically. In this context, this paper addresses the use of ecological interactions for a mobile serious game. We exploit a wearable insole in order to let users interact with a virtual soccer game via real-world soccer movements. We analyzed the concept of ecological interactions. The system used for recognition of ecological gestures is also detailed. A primary study showed that proposed system can be exploited for real time gesture recognition on a mobile device.

Real-Time Pedobarography Analysis by Piezoresistive Wearable Insole

Real-Time Pedobarography Analysis by Piezoresistive Wearable Insole

A Wearable Biomechatronic Interface for Controlling Robots with Voluntary Foot Movements

This paper presents an experimental investigation on a novel interface for high level control of mechatronic systems, by exploiting voluntary user's foot movements. Based on a biomechanical analysis of the foot anatomy and joint kinematics, a sensory system is designed for detecting pressure variations on selected areas of the insole, obtained from four different foot movements that can be purposively controlled by the person. A prototype is developed that integrates four sensitive areas, battery, and electronics into a wearable insole; electronics are used for data acquisition and wireless transmission, in order to have a stand-alone device. The prototype foot interface is experimentally tested in the control of a prosthetic hand, as a model of a typical device that can be effectively operated by foot movements. Experimental trials were conducted with ten able-bodied subjects and the results confirmed the usability and effectiveness of the foot interface in terms of correct and prompt transmission of the user's intention to the controlled device. Comparative experimental trials were performed with electromyography (EMG)-based control of the same prosthesis, which represents the most advanced interface currently available in clinical implants for amputees. The comparative results showed a significant decrease in required adaptation and learning from the user's side