|Project title||A novel wearable sensor for continuous human gait analysis and evaluation|
|Funding Organization||EYDE – ETAK|
|Coordinator||Hellenic Mediterranean University|
|Duration||06/09/2018 – 05/03/2022 (42 months)|
|Total Budget||996.057,00 €|
|HMU budget||213.654,00 €|
The basic idea of the project is to design and manufacture a smart, wearable insole (Smart Insole) with built-in pressure measurement sensors, other micro-electronic sensing elements and communication devices to tackle the challenge of efficient gait monitoring in real life. This wearable insole is based on a flexible piezoelectriclayer, with a totally different architecture compared to currently internationally available technologies. The layer consists of a polymeric material with embedded piezoelectric flakes, an inertial measurement unit including a triaxial accelerometer, gyroscope and magnetometer to capture the gait characteristics in motion. Based on existing laboratory data from the early stages of sensor construction, by controlling the piezo cell size and layer thickness, it is possible to control the sensitivity of the system. Applying force to the layer creates a potential difference across the material, different for each cell – depending on the pressure at that point.In this way we can accurately map the pressures exerted along the length and width of the footpad, the motion motifs and gait morphology in general, transferring this information in a wireless way for analysis in appropriate computational systems. At the same time, the accompanying software will provide easy-to-use tools for analysis and visualization of the complex gait data. Meanwhile, it is lightweight, thin, and comfortable to wear, providing an unobtrusive way to perform continues, unobtrusive gait monitoring. Furthermore, a smartphone application will display the sensor data in real-time via Bluetooth low energy connection. The project also aims at the development of specialized patient/citizen monitoring services which are made feasible by the Smart Insole device. These services target specific but large population groups.
The main innovative features of the project are: a) the development of a new wearable based on a novel architecture, as compared to existing approaches, in terms of sensitivity and cost; b) the devel-opment of an advanced spatiotemporal signal processing algorithms; and c) the development and application of innovative machine learning methods for accurate analysis of gait and its morphology. These innovations will be the basis for designing new services for the early diagnosis of specific patho-logical conditions, monitoring their development and evaluating the use of wearable sensor platforms as clinical diagnostic tools.