Gait Analysis Platform for Measuring Surgery Recovery.

Gait analysis has evolved significantly during last years due to the great development of the Medical Internet of Things (MIoT) platforms that allow an easy integration of sensors (inertial, magnetic and pressure in our case) to the complex analytics required to compute, not only relevant parameters, but also meaningful indexes. In this paper, we extend a previous development based on a fully wireless pair of insoles by implementing an updated version with more reliable and user-friendly devices, smartphone app and web front-end and back-end. We also extend previous work focused on fall analysis (with the corresponding fall risk index or FRI) with the proposal of a new surgery recovery index (SRI) to account for the individual speed recovery speed that can be measured either at clinical facilities or at home in a telemedicine environment or while doing daily life activities. This new index can be personalized for different types of surgeries that affect gait such as hip, knee, etc. This paper presents the case of hip recovery and is built on top of the clinical standard SPPB test and allows obtaining quantitative parameters directly from the sensors.

Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study.

Falls are a major health problem for older adults with immediate effects, such as fractures and head injuries, and longer term effects including fear of falling, loss of independence, and disability. The goals of the WIISEL project were to develop an unobtrusive, self-learning and wearable system aimed at assessing gait impairments and fall risk of older adults in the home setting; assessing activity and mobility in daily living conditions; identifying decline in mobility performance and detecting falls in the home setting. The WIISEL system was based on a pair of electronic insoles, able to transfer data to a commercially available smartphone, which was used to wirelessly collect data in real time from the insoles and transfer it to a backend computer server via mobile internet connection and then onwards to a gait analysis tool. Risk of falls was calculated by the system using a novel Fall Risk Index (FRI) based on multiple gait parameters and gait pattern recognition. The system was tested by twenty-nine older users and data collected by the insoles were compared with standardized functional tests with a concurrent validity approach. The results showed that the FRI captures the risk of falls with accuracy that is similar to that of conventional performance-based tests of fall risk. These preliminary findings support the idea that theWIISEL system can be a useful research tool and may have clinical utility for long-term monitoring of fall risk at home and in the community setting.

Real-Time Constant Monitoring of Fall Risk Index by Means of Fully-Wireless Insoles.

Constant monitoring of gait in real life conditions is considered the best way to assess Fall Risk Index (FRI) since most falls happen out of the ideal conditions in which clinicians are currently analyzing the patient's behavior. This paper presents the WIISEL platform and results obtained through the use of the first full-wireless insole devices that can measure almost all gait related data directly on the feet (not in the upper part of the body as most existing wearable solutions). The platform consists of a complete tool-chain: insoles, smartphone & app, server & analysis tool, FRI estimation and user access. Results are obtained by combining parameters in a personalized way to build individual fall risk index assessed by experts with the help of data analytics. New FRI has been compared with standards that validate the quality of its prediction in a statistically significant way. That qualitatively relevant information is being provided to the platform users, being either end-users/patients, relatives or caregivers and the related clinicians to ideally assess about their long term evolution.

A wireless sensor insole for collecting gait data.

This paper presents the status of the EU project WIISEL - Wireless Insole for Independent and Safe Elderly Living, with the focus on sensors and wireless communications. Pressure and inertial sensors are embedded into insoles and a smartphone collects data utilizing Bluetooth Low Energy.