An Insole Plantar Pressure Measurement System Based on 3D Forces Piezoelectric Sensor

In this paper, an insole plantar pressure device based on 3-D forces piezoelectric sensor, integrated into a shoe, was developed for monitoring plantar pressure under real-life conditions. The device consisted of an insole with eight measure points composed of piezoelectric sensor, a wireless data transmission and embedded computer. The piezoelectric sensor with ceramic was embedded into the insole assembled by three different directions (3D) of X, Y and Z. The piezoelectric sensors mathematical model was built, and the layout of these sensors in insole was investigated. Based on the FFT algorithm, the eight measurement points data were collected and analyzed taken by frequency. The plantar changes of the left and right foot in 3D direction were measured, respectively. The results were displayed on the software interface, it showed that the insole system can be used to monitor plantar pressure during daily living and is expected to be useful in various clinical applications

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

Développement et étude de la validité d'une semelle instrumentée pour le comptage de pas

Les semelles instrumentées sont des dispositifs pouvant être utilisées pour la quantification de pas et la reconnaissance des activités. Il existe plusieurs modèles de semelles instrumentées, avec des niveaux de validité variables. Ce mémoire comprend trois objectifs : 1) faire une revue systématique de la littérature sur la validité de critère des semelles instrumentées existantes pour identifier les postures, les types d’activités et compter les pas, 2) développer une semelle instrumentée et 3) étudier sa validité pour le comptage de pas. Pour l’objectif 1, cinq bases de données ont permis de sélectionner 33 articles sur la validité de critère de seize modèles de semelles instrumentées pour la détection de posture, de type d’activités et de pas. Selon les indicateurs utilisés, les validités de critère varient de 65,8% à 100% pour la reconnaissance des activités et des postures et de 96% à 100% pour la détection de pas. En somme, peu d’études ont utilisé les semelles instrumentées pour le comptage de pas bien qu’elles démontrent une très bonne validité. Pour les objectifs 2 et 3, nous avons équipé une semelle commercialisée de cinq capteurs de pression et testé trois méthodes de traitement des signaux de pression pour la quantification de pas. Ces trois méthodes sont basées sur le signal de chaque capteur de pression, la moyenne ou la somme cumulée des cinq signaux de pression. Les résultats ont montré que notre semelle instrumentée détectait le pas avec un taux de succès de 94,8 ± 9,4% à 99,5 ± 0,4% à des vitesses de marche confortable et de 97,0 ± 6,2% à 99,6 ± 0,4% à des vitesses de marche rapide à l’intérieur et à l’extérieur d’un bâtiment avec les trois méthodes. Toutefois, la méthode basée sur la somme cumulée avait les niveaux de précision plus élevés pour le comptage de pasInstrumented insoles are devices which can be used for quantifying steps and recognizing activities. Validity of many instrumented insoles varies from medium to high. This thesis has three objectives: 1) to systematically review the literature on the validity of existing instrumented insoles for posture, type of activities recognition, and step counting, 2) to develop an instrumented insole and 3) to study its criterion validity for step counting. For objective 1, five databases were used to select 33 articles on criterion validity of sixteen insole models for posture and type of activities recognition, and step detection. According to indicators used, validity values vary from 65.8% to 100% for activities and postures recognition and from 96% to 100% for detection of steps. In summary, few studies have used instrumented insoles for steps counting even though they demonstrated a very good validity. For objectives 2 and 3, we equipped a commercialized insole with five pressure sensors and tested three pressure signal processing methods for step quantification. These three methods are based on signal of each pressure sensor, average or cumulative sum of five pressure signals. Results showed that our instrumented insole detected steps with a success rate varying from 94.8 ± 9.4% to 99.5 ± 0.4% at self-selected walking speeds and from 97.0 ± 6.2% to 99.6 ± 0.4% at maximal walking speeds in indoor and outdoor settings with all three processing methods. However, cumulative sum method had the highest levels of accuracy for step counting

Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring

Health authorities in numerous countries and even the World Health Organization (WHO) are concerned with low levels of physical activity and increasing sedentary behaviour amongst the general population. In fact, emerging evidences identify sedentary behaviour as a ubiquitous characteristic of contemporary lifestyles. This has major implications for the general health of people worldwide particularly for the prevalence of non-communicable conditions (NCDs) such as cardiovascular disease, diabetes and cancer and their risk factors such as raised blood pressure, raised blood sugar and overweight. Moreover, sedentary time appears to be uniquely associated with health risks independent of physical activity intensity levels. However, habitual sedentary behaviour may prove complex to be accurately measured as it occurs across different domains, including work, transport, domestic duties and even lei¬sure. Since sedentary behaviour is mostly reflect as too much sitting, one of the main concerns is being able to distinguish among different activities, such as sitting and standing. Widely used devices such as accelerometer-based activity monitors have a limited ability to detect sedentary activities accurately. Thus, there is a need of a viable large-scale method to efficiently monitor sedentary behaviour. This thesis proposes and demonstrates how a plantar pressure based wearable device and machine learning classification techniques have significant capability to monitor daily life sedentary behaviour. Firstly, an in-depth review of research and market ready plantar pressure and force technologies is performed to assess their measurement capabilities and limitations to measure sedentary behaviour. Afterwards, a novel methodology for measuring daily life sedentary behaviour using plantar pressure data and a machine learning predictive model is developed. The proposed model and its algorithm are constructed using a dataset of 20 participants collected at both laboratory-based and free-living conditions. Sitting and standing variations are included in the analysis as well as the addition of a potential novel activities, such as leaning. Video footage is continuously collected using of a wearable camera as an equivalent of direct observation to allow the labelling of the training data for the machine learning model. The optimal parameters of the model such as feature set, epoch length, type of classifier is determined by experimenting with multiple iterations. Different number and location of plantar pressure sensors are explored to determine the optimal trade-off between low computational cost and accurate performance. The model s performance is calculated using both subject dependent and subject independent validation by performing 10-fold stratified cross-validation and leave-one-user-out validation respectively. Furthermore, the proposed model activity performance for daily life monitoring is validated against the current criterion (i.e. direct observation) and against the de facto standard, the activPAL. The results show that the proposed machine learning classification model exhibits excel-lent recall rates of 98.83% with subject dependent training and 95.93% with independent training. This work sets the groundwork for developing a future plantar pressure wearable device for daily life sedentary behaviour monitoring in free-living conditions that uses the proposed ma-chine leaning classification model. Moreover, this research also considers important design characteristics of wearable devices such as low computational cost and improved performance, addressing the current gap in the physical activity and sedentary behaviour wearable market

Instrumented shoes for daily activity monitoring in healthy and at risk populations

Daily activity reflects the health status of an individual. Ageing and disease drastically affect all dimensions of mobility, from the number of active bouts to their duration and intensity. Performing less activity leads to muscle deterioration and further weakness that could lead to increased fall risk. Gait performance is also affected by ageing and could be detrimental for daily mobility. Therefore, activity monitoring in older adults and at risk persons is crucial to obtain relevant quantitative information about daily life performance. Activity evaluation has mainly been established through questionnaires or daily logs. These methods are simple but not sufficiently accurate and are prone to errors. With the advent of microelectromechanical systems (MEMS), the availability of wearable sensors has shifted activity analysis towards ambulatory monitoring. In particular, inertial measurement units consisting of accelerometers and gyroscopes have shown to be extremely relevant for characterizing human movement. However, monitoring daily activity requires comfortable and easy to use systems that are strategically placed on the body or integrated in clothing to avoid movement hindrance. Several research based systems have employed multiple sensors placed at different locations, capable of recognizing activity types with high accuracy, but not comfortable for daily use. Single sensor systems have also been used but revealed inaccuracies in activity recognition. To this end, we propose an instrumented shoe system consisting of an inertial measurement unit and a pressure sensing insole with all the sensors placed at the shoe/foot level. By measuring the foot movement and loading, the recognition of locomotion and load bearing activities would be appropriate for activity classification. Furthermore, inertial measurement units placed on the foot can perform detailed gait analysis, providing the possibility of characterizing locomotion. The system and dedicated activity classification algorithms were first designed, tested and validated during the first part of the thesis. Their application to clinical rehabilitation of at risk persons was demonstrated over the second part. In the first part of the thesis, the designed instrumented shoes system was tested in standardized conditions with healthy elderly subjects performing a sequence of structured activities. An algorithm based on movement biomechanics was built to identify each activity, namely sitting, standing, level walking, stairs, ramps, and elevators. The rich array of sensors present in the system included a 3D accelerometer, 3D gyroscope, 8 force sensors, and a barometer allowing the algorithm to reach a high accuracy in classifying different activity types. The tuning parameters of the algorithm were shown to be robust to small changes, demonstrating the suitability of the algorithm to activity classification in older adults. Next, the system was tested in daily life conditions on the same elderly participants. Using a wearable reference system, the concurrent validity of the instrumented shoes in classifying daily activity was shown. Additionally, daily gait metrics were obtained and compared to the literature. Further insight into the relationship between some gait parameters as well as a global activity metric, the activity âcomplexityâ, was discussed. Participants positively rated their comfort while using the system... (Please refer to thesis for full abstract