An Overview of Smart Shoes in the Internet of Health Things: Gait and Mobility Assessment in Health Promotion and Disease Monitoring

New smart technologies and the internet of things increasingly play a key role in healthcare and wellness, contributing to the development of novel healthcare concepts. These technologies enable a comprehensive view of an individual’s movement and mobility, potentially supporting healthy living as well as complementing medical diagnostics and the monitoring of therapeutic outcomes. This overview article specifically addresses smart shoes, which are becoming one such smart technology within the future internet of health things, since the ability to walk defines large aspects of quality of life in a wide range of health and disease conditions. Smart shoes offer the possibility to support prevention, diagnostic work-up, therapeutic decisions, and individual disease monitoring with a continuous assessment of gait and mobility. This overview article provides the technological as well as medical aspects of smart shoes within this rising area of digital health applications, and is designed especially for the novel reader in this specific field. It also stresses the need for closer interdisciplinary interactions between technological and medical experts to bridge the gap between research and practice. Smart shoes can be envisioned to serve as pervasive wearable computing systems that enable innovative solutions and services for the promotion of healthy living and the transformation of health care

Master of Science

thesisComputing and data acquisition have become an integral part of everyday life. From reading emails on cell phones to kids playing with motion sensing game consoles, we are surrounded with sensors and mobile computing devices. As the availability of powerful computing devices increases, applications in previously limited environments become possible. Training devices in rehabilitation are becoming increasingly common and more mobile. Community based rehabilitative devices are emerging that embrace these mobile advances. To further the flexibility of devices used in rehabilitation, research has explored the use of smartphones as a means to process data and provide feedback to the user. In combination with sensor embedded insoles, smartphones provide a powerful tool for the clinician in gathering data and as a standalone training tool in rehabilitation. This thesis presents the continuing research of sensor based insoles, feedback systems and increasing the capabilities of the Adaptive Real-Time Instrumentation System for Tread Imbalance Correction, or ARTISTIC, with the introduction of ARTISTIC 2.0. To increase the capabilities of the ARTISTIC an Inertial Measurement Unit (IMU) was added, which gave the system the ability to quantify the motion of the gait cycle and, more specifically, determine stride length. The number of sensors in the insole was increased from two to ten, as well as placing the microprocessor and a vibratory motor in the insole. The transmission box weight was reduced by over 50 percent and the volume by over 60 percent. Stride length was validated against a motion capture system and found the average stride length to be within 2.7 ± 6.9 percent. To continue the improvement of the ARTISTIC 2.0, future work will include implementing real-time stride length feedback

Just find it: The Mymo approach to recommend running shoes

Wearing inappropriate running shoes may lead to unnecessary injury through continued strain upon the lower extremities; potentially damaging a runner’s performance. Many technologies have been developed for accurate shoe recommendation, which centre on running gait analysis. However, these often require supervised use in the laboratory/shop or exhibit too high a cost for personal use. This work addresses the need for a deployable, inexpensive product with the ability to accurately assess running shoe-type recommendation. This was achieved through quantitative analysis of the running gait from 203 individuals through use of a tri-axial accelerometer and tri-axial gyroscope-based wearable (Mymo). In combination with a custom neural network to provide the shoe-type classifications running within the cloud, we experience an accuracy of 94.6 in classifying the correct type of shoe across unseen test data

WSN and M2M for mountain biking performance assessment

The thesis describes the design and implementation of the "Smart Mountain Bike” monitoring system enables the acquisition, storage and visualization of data on athlete training referring the cycling activity. The signals provided by the measurement channels are acquired and processed in order to better understand of the variables involved in this sport and consecutively to improve the methodology for the training of athletes. The "Smart Mountain Bike" system consists of a wireless sensor network that acquire data related to the applied force and body position during a training session. Each network end node comprises a microcontroller, a conditioning circuit and a set of sensors. The coordinator node Zig Bee compatible is composed by microcomputer (eg. Raspberry PI or BeagleBone), a GPS and an IMU. The cloud interfacing is done using a 3G/UMTS USB module connected to the microcomputer board. As the main component of the cloud the implemented database is accessed through a mobile application implemented in an Android OS device. The mobile application allows the visualization of the acquired and processed data by the user expressed by the athlete or the coach. This system can be used for other sports and other activities in which it is necessary to monitor physical activities such as physical therapy.Este documento descreve o desenvolvimento de um protótipo "Smart Mountain Bike", este sistema de monitorização permite a recolha, armazenamento e visualização dos dados relativos aos treinos do atleta durante a atividade ciclismo. Esta informação contribui para um melhor entendimento das variáveis envolvidas da prática deste desporto e consecutivamente, melhorar a metodologia de treino dos atletas. O sistema "Smart Mountain Bike" é constituído por uma rede sensores sem fios que recolhe a dados sobre força aplicada e posição do corpo numa sessão de treino, cada nó final da rede é composto por um microcontrolador, um circuito condicionador e um conjunto de sensores. O nó coordenador é composto por um microcomputador, um recetor GPS, um IMU e um módulo de comunicação móvel, este módulo permite um cenário Machine-to-Machine, onde o microcomputador comunica com o a nuvem permitindo o armazenamento da informação recolhida numa base de dados. Esta informação é acedida através de uma aplicação móvel desenvolvida para este projeto, a aplicação móvel permite ao utilizador, atleta ou treinador, visualizar e correlacionar os dados. Este sistema pode ser utilizado noutros desportos e noutras atividades em que seja necessário monitorizar atividades físicas, como por exemplo, fisioterapi