A Wireless Flexible Sensorized Insole for Gait Analysis

This paper introduces the design and development of a novel pressure-sensitive foot insole for real-time monitoring of plantar pressure distribution during walking. The device consists of a flexible insole with 64 pressure-sensitive elements and an integrated electronic board for high-frequency data acquisition, pre-filtering, and wireless transmission to a remote data computing/storing unit. The pressure-sensitive technology is based on an optoelectronic technology developed at Scuola Superiore Sant'Anna. The insole is a low-cost and low-power battery-powered device. The design and development of the device is presented along with its experimental characterization and validation with healthy subjects performing a task of walking at different speeds, and benchmarked against an instrumented force platform

Outcome measures and motion capture systems for assessing lower limb orthosis-based interventions after stroke: a systematic review

Purpose: To review and categorize, according to the International Classification of Functioning, the outcome measures, and motion capture systems for studying the evidence-based practice of orthotic-based interventions in post-stroke gait rehabilitation. Methods: An electronic literature search was conducted up to February 2018 in Web of Science, Scopus, MEDLINE and Physiotherapy Evidence Database. Randomized trials measuring activity, impairment or participation outcome measures for studying the evidence-based practice of orthoses in gait rehabilitation after an acute or chronic stroke were identified. The studies were assessed through the Cochrane risk-of-bias tool by three authors. Information about stroke’s stage, assessment protocol (goal, timing and motion capture system), orthosis configuration and outcome measures were extracted. Results: Eighteen randomized trials, including 387 post-stroke adults, mostly in the chronic stage, were selected. They assessed 39 outcomes, mainly activity outcome measures such as spatiotemporal (72.2%), kinematic (44.4%) and functional (33.3%) outcomes. Gait speed was the primary outcome in most studies. Participation (22.2%) and impairment (16.7%) outcome measures were less explored. Mostly, non-portable motion capture systems were employed opposing the freely-use of the wearable orthosis. The detection bias risk and the shortage of baseline and follow-up outcome measures affected the studies’ assessment quality. Conclusions: Studies showed heterogeneity in selecting outcomes and timings for assessment. There is evidence for assessing the evidence of orthosis-based gait rehabilitation after stroke through activity outcome measures, primarily the gait speed, recorded by non-wearable motion capture systems. A unified methodology considering wearable sensors for tracking baseline and follow-up measures is needed.Implications for rehabilitation There is evidence on use activity outcome measures to assess the meaningful evidence-based practice of orthosis-based gait rehabilitatio- (undefined

Unsupervised and scalable low train pathology detection system based on neural networks

Currently, there exist different technologies applied in the world of medicine dedicated to the detection of health problems such as cancer, heart diseases, etc. However, these technologies are not applied to the detection of lower body pathologies. In this article, a Neural Network (NN)-based system capable of classifying pathologies of the lower train by the way of walking in a non-controlled scenario, with the ability to add new users without retraining the system is presented. All the signals are filtered and processed in order to extract the Gait Cycles (GCs), and those cycles are used as input for the NN. To optimize the network a random search optimization process has been performed. To test the system a database with 51 users and 3 visits per user has been collected. After some improvements, the algorithm can correctly classify the 92% of the cases with 60% of training data. This algorithm is a first approach of creating a system to make a first stage pathology detection without the requirement to move to a specific place

Predicting three-dimensional ground reaction forces in running by using artificial neural networks and lower body kinematics

This study explored the use of artificial neural networks in the estimation of runners' kinetics from lower body kinematics. Three supervised feed-forward artificial neural networks with one hidden layer each were modelled and assigned individually with the mapping of a single force component. Number of training epochs, batch size and dropout rate were treated as modelling hyper-parameters and their values were optimised with a grid search. A public data set of twenty-eight professional athletes containing running trails of different speeds (2.5 m/sec, 3.5 m/sec and 4.5 m/sec) was employed to train and validate the networks. Movements of the lower limbs were captured with twelve motion capture cameras and an instrumented dual-belt treadmill. The acceleration of the shanks was fed to the artificial neural networks and the estimated forces were compared to the kinetic recordings of the instrumented treadmill. Root mean square error was used to evaluate the performance of the models. Predictions were accompanied with low errors: 0.134 BW for the vertical, 0.041 BW for the anteroposterior and 0.042 BW for the mediolateral component of the force. Vertical and anteroposterior estimates were independent of running speed (p=0.233 and p=.058, respectively), while mediolateral results were significantly more accurate for low running speeds (p=0.010). The maximum force mean error between measured and estimated values was found during the vertical active peak (0.114 ± 0.088 BW). Findings indicate that artificial neural networks in conjunction with accelerometry may be used to compute three-dimensional ground reaction forces in running

Validity and reliability of a wearable insole pressure system for measuring gait parameters to identify safety hazards in construction

Purpose Construction workers are frequently exposed to safety hazards on sites. Wearable sensing systems (e.g. wearable inertial measurement units (WIMUs), wearable insole pressure system (WIPS)) have been used to collect workers' gait patterns for distinguishing safety hazards. However, the performance of measuring WIPS-based gait parameters for identifying safety hazards as compared to a reference system (i.e. WIMUs) has not been studied. Therefore, this study examined the validity and reliability of measuring WIPS-based gait parameters as compared to WIMU-based gait parameters for distinguishing safety hazards in construction. Design/methodology/approach Five fall-risk events were conducted in a laboratory setting, and the performance of the proposed approach was assessed by calculating the mean difference (MD), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and intraclass correlation coefficient (ICC) of five gait parameters. Findings Comparable results of MD, MAE, MAPE and RMSE were found between WIPS-based gait parameters and the reference system. Furthermore, all measured gait parameters had validity (ICC = 0.751) and test-retest reliability (ICC = 0.910) closer to 1, indicating a good performance of measuring WIPS-based gait parameters for distinguishing safety hazards. Research limitations/implications Overall, this study supports the relevance of developing a WIPS as a noninvasive wearable sensing system for identifying safety hazards on construction sites, thus highlighting the usefulness of its applications for construction safety research. Originality/value This is the first study to examine the performance of a wearable insole pressure system for identifying safety hazards in construction

Real-time Hybrid Locomotion Mode Recognition for Lower-limb Wearable Robots

Real-time recognition of locomotion-related activities is a fundamental skill that the controller of lower-limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for real-time locomotion mode recognition of locomotion-related activities in lower-limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a time-based approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy logic method triggered by foot pressure sensors operates in a subject-independent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for a subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10,000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities

Experimental validation of motor primitive-based control for leg exoskeletons during continuous multi-locomotion tasks

An emerging approach to design locomotion assistive devices deals with reproducing desirable biological principles of human locomotion. In this paper, we present a bio-inspired controller for locomotion assistive devices based on the concept of motor primitives. The weighted combination of artificial primitives results in a set of virtual muscle stimulations. These stimulations then activate a virtual musculoskeletal model producing reference assistive torque profiles for different locomotion tasks (i.e., walking, ascending stairs, and descending stairs). The paper reports the validation of the controller through a set of experiments conducted with healthy participants. The proposed controller was tested for the first time with a unilateral leg exoskeleton assisting hip, knee, and ankle joints by delivering a fraction of the computed reference torques. Importantly, subjects performed a track involving ground-level walking, ascending stairs, and descending stairs and several transitions between these tasks. These experiments highlighted the capability of the controller to provide relevant assistive torques and to effectively handle transitions between the tasks. Subjects displayed a natural interaction with the device. Moreover, they significantly decreased the time needed to complete the track when the assistance was provided, as compared to wearing the device with no assistance

Application of Machine Learning Methods for Human Gait Analysis

The majority of human gait analysis methods are limited to clinical gait laboratories. The calculation of gait parameters for athletes, during running in open environment, has endless possibilities of performance analysis to keep track of training. This thesis demonstrates a method to capture three-dimensional measurements of multidimensional human body movements during walking and running by means of GPS-aided-INS equipped data logger and also describes the two-dimensional (forward and vertical) analysis of captured three-dimensional movement. The gait segmentation based on the vertical velocity has been presented and the built data processing software can compute majority of traditional gait metrics such as stride duration, average speed, stride length, cadence and vertical oscillation. The equipment uses inexpensive pressure insoles to generate foot pressure data for model training and indirect estimation of vertical ground reaction force and ground contact time. Both machine and deep learning approaches are detailed for indirect estimation of vertical ground reaction force and ground contact time. The possibilities are also explored to make interpersonal gait parameter estimation by means of generalised prediction models. Both machine leaning and deep learning solution are presented to generate continuous vertical ground reaction force curves along with gait components. The methods, presented in this thesis, help to analyse human motion by means of gait segmentation and to calculate or estimate numerous spatio-temporal gait parameters. The intra-step variations in motion parameters are great help to analyse the different aspects of running in outdoor. The encouraging results reported in this thesis demonstrate the feasibility of device that provides detailed analysis about the performance of an athlete in outdoor running environment

Diseño y construcción de un dispositivo portátil para medición del centro de presión del cuerpo humano

Tesis que describe el diseño, construcción y validación de un sistema electrónico para la evaluación del equilibrio en humanosEl estudio del equilibrio humano es útil para el diagnóstico y seguimiento de diversas patologías como: enfermedades neurológicas (Parkinson, Alzheimer, etc.), alteraciones del sistema músculo-esquelético debidas a razones como: uso de tacones altos, sobrepeso, envejecimiento, inestabilidades posturales, uso de prótesis, etc. Existen numerosas herramientas para valorar cualitativa y cuantitativamente el equilibrio. La mayoría se basa en la medición del CoP (Centro de Presión, por sus siglas en inglés), este parámetro depende a su vez de la posición del CoM (Centro de Masa), la cual es la variable monitoreada y controlada por el Sistema Nervioso Central (SNC) para mantener el equilibrio. Las herramientas cualitativas (observación) son propensas a errores de apreciación por falta de experiencia, cansancio o descuido del evaluador. Las cuantitativas suelen ser muy voluminosas (1 a 2 m 3 y más de 4 kg), costosas (superiores a los 3,000 USD) y por tanto limitadas a su uso en laboratorios especializados. Otras soluciones cuantitativas (plantillas instrumentadas) son más económicas pero son personalizadas para un solo sujeto, lo cual nuevamente limita su impacto en el análisis del equilibrio en grandes poblaciones. En esta tesis se presenta el diseño y construcción de un dispositivo portátil y de bajo costo para evaluar el CoP. El prototipo presentado se basa en 3 sensores FSR (Force Sensing Resistor) por pie en una configuración geométrica tomando como base las regiones donde se concentra el mayor peso del cuerpo. Mediante la adecuación de un algoritmo se obtuvo un cálculo para obtener el CoP basado en esos sensores. El sistema está diseñado para adaptarse a pies de 22 a 29 cm de longitud. La estimación del CoP y de los distintos índices comúnmente usados en el diagnóstico y seguimiento de diversas patologías, son calculados en un sistema embebido y desplegados en una pantalla TFT (Thin Film Transistor). Lo anterior asegura un sistema adaptable, portátil y de bajo costo comparado con los sistemas existentes en el estado del arte. El sistema se utilizó para medir el CoP de 50 sujetos de entre 20 y 39 años de edad (33 hombres y 17 mujeres) cuya edad y peso promedio son (26.04 ± 4.94 años, 68.37 ± 8.15 kg) respectivamente. Los resultados promedio obtenidos se compararon con los reportados en diversos estudios para sujetos con características similares. Éstos indican que el sistema aquí presentado es capaz de medir el CoP, obteniendo resultados similares a los que presentan sistemas basados en plataformas de fuerza (Golden Standard). Se encontró además que el sistema es capaz de discriminar entre sujetos en posición de pie, con ojos cerrados y sujetos con ojos abiertos (p < 0001), diferencia que por criterios convencionales se considera estadísticamente significativa