Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks

تُعَدُّ أنظمة النعال الحسّاسة للحركة تقنية واعدة للعديد من التطبيقات في الرعاية الصحية والرياضة. حيث يمكن أن توفّر هذه الأنظمة معلومات قيّمة حول توزيع الضغط على القدم وأنماط المشي لأفراد مختلفين. ومع ذلك، فإن تصميم وتنفيذ مثل هذه الأنظمة يواجه العديد من التحديات، مثل اختيار الحسّاسات والمعايرة ومعالجة البيانات والتفسير. في هذه الدراسة، نقترح نظام نعل حساس باستخدام مقاومات استشعار القوى  لقياس الضغط المطبّق من القدم على مناطق مختلفة من النعل. يقوم هذا النظام بتصنيف أربعة أنواع من تشوهات القدم: طبيعي، مسطح، انحراف القدم إلى الداخل، وزيادة انحراف القدم إلى الخارج. تستخدم مرحلة التصنيف قيم الضغط الفرقية على نقاط الضغط كمدخلات لنموذج التغذية الأمامية للشبكات العصبية. تم جمع البيانات من 60 فرداً تم تشخيصهم بالحالات المدروسة. حقق تنفيذ التغذية الأمامية للشبكات العصبية دقة بنسبة 96.6٪ باستخدام 50٪ من المجموعة البيانية كبيانات تدريبية و 92.8٪ باستخدام 30٪ من البيانات التدريبية فقط. ويوضح المقارنة مع الأعمال ذات الصلة الأثر الإيجابي لاستخدام القيم الفرق لنقاط الضغط كمدخلات للشبكات العصبية مقارنة بالبيانات الأولية.Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforward neural network (FNN) model. Data acquisition involved 60 subjects diagnosed with the studied cases. The implementation of FNN achieved an accuracy of 96.6% using 50% of the dataset as training data and 92.8% using only 30% training data. The comparison with related work shows good impact of using the differential values of pressure points as input for neural networks compared with raw data

Sensors for Foam Balance Pad

Diplomová práce se zabývá návrhem vlastního senzorického řešení pro detekci pohybů prováděných na pěnové balanční podložce AIREX® Elite. Součástí práce je teoretický popis balančních cvičebních pomůcek a jejich aplikace v oblasti fyzioterapie. Dále je zde uvedena rešerše současných technických řešení pro snímání pohybu na balanční pomůcce. Pro realizaci vlastního řešení byl vybrán princip kapacitního měření vzdálenosti s využitím vodivých textilií pro realizaci senzoru. Další část je věnována návrhu hardwarového řešení, je zde popsán návrh senzorické matice, velikost jednotlivých snímacích prvků a vzdálenost mezi nimi a sběrem dat pomocí mikrokontroléru STM32 a zpracováním těchto dat v prostředí LabVIEW. Součástí vlastní práce je návrh vlastního uživatelského rozhraní k vizualizaci pohybu na pěnové balanční podložce a testování vytvořeného řešení v reálných podmínkách při rehabilitaci v domácím prostředí.The thesis deals with the design of a custom sensor solution for the detection of movements performed on the AIREX® Elite foam balance pad. The thesis includes a theoretical description of balance exercise aids and their application in the field of physiotherapy. Furthermore, a survey of current technical solutions for motion sensing on balance aids is presented. For the implementation of the actual solution, the principle of capacitive distance measurement using conductive textiles was chosen for the sensor implementation. The next section is devoted to the design of the hardware solution, it describes the design of the sensor matrix, the size of the individual sensing elements and the distance between them and the data acquisition using the STM32 microcontroller and the processing of this data in the LabVIEW environment. The actual work includes the design of a custom user interface to visualize the motion on the foam balance pad and testing of the developed solution in real conditions during rehabilitation in a home environment.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Foot Plantar Pressure Measurement System Using Highly Sensitive Crack-Based Sensor

Measuring the foot plantar pressure has the potential to be an important tool in many areas such as enhancing sports performance, diagnosing diseases, and rehabilitation. In general, the plantar pressure sensor should have robustness, durability, and high repeatability, as it should measure the pressure due to body weight. Here, we present a novel insole foot plantar pressure sensor using a highly sensitive crack-based strain sensor. The sensor is made of elastomer, stainless steel, a crack-based sensor, and a 3D-printed frame. Insoles are made of elastomer with Shore A 40, which is used as part of the sensor, to distribute the load to the sensor. The 3D-printed frame and stainless steel prevent breakage of the crack-based sensor and enable elastic behavior. The sensor response is highly repeatable and shows excellent durability even after 20,000 cycles. We show that the insole pressure sensor can be used as a real-time monitoring system using the pressure visualization program

Foot Motion-Based Falling Risk Evaluation for Patients with Parkinson’s Disease

Parkinson’s disease (PD) affects motor functionalities, which are closely associated with increased risks of falling and decreased quality of life. However, there is no easy-to-use definitive tools for PD patients to quantify their falling risks at home. To address this, in this dissertation, we develop Monitoring Insoles (MONI) with advanced data processing techniques to score falling risks of PD patients following Falling Risk Questionnaire (FRQ) developed by the U.S. Centers for Disease Control and Prevention (CDC). To achieve this, we extract motion tasks from daily activities and select the most representative features associated with PD that facilitate accurate falling risk scoring. To address the challenge in uncontrolled daily life environments and to identify the most representative features associated with PD and falling risks, the proposed data processing method firstly recognizes foot motions such as walking and toe tapping from continuous movements with stride detection and fast labeling framework, and then extracts time-axis and acceleration-axis features from the motion tasks, at the end provides a score of falling risks using regression. The data processing method can be integrated into a mobile game to be used at home with MONI. The main contributions of this dissertation includes: (i) developing MONI as a low power solution for daily life use; (ii) utilizing stride detection and developing fast labeling framework for motion recognition that improves recognition accuracy for daily life applications; (iii) analyzing two walking and two toe tapping tasks that are close to real life scenarios and identifying important features associated with PD and falling risks; (iv) providing falling scores as quantitative evaluation to PD patients in daily life through simple foot motion tasks and setups