Clinical gait analysis contributes massively to rehabilitation support and improvement of in-patient care. The research project eSHOE aspires to be a useful addition to the rich variety of gait analysis systems. It was designed to fill the gap of affordable, reasonably accurate and highly mobile measurement devices. With the overall goal of enabling individual home-based monitoring and training for people suffering from chronic diseases, affecting the locomotor system. Motion and pressure sensors gather movement data directly on the (users) feet, store them locally and/or transmit them wirelessly to a PC. A combination of pattern recognition and feature extraction algorithms translates the motion data into standard gait parameters. Accuracy of eSHOE were evaluated against the reference system GAITRite in a clinical pilot study. Eleven hip fracture patients (78.4 ± 7.7 years) and twelve healthy subjects (40.8 ± 9.1 years) were included in these trials. All subjects performed three measurements at a comfortable walking speed over 8 m, including the 6-m long GAITRite mat. Six standard gait parameters were extracted from a total of 347 gait cycles. Agreement was analysed via scatterplots, histograms and Bland-Altman plots. In the patient group, the average differences between eSHOE and GAITRite range from -0.046 to 0.045 s and in the healthy group from -0.029 to 0.029 s. Therefore, it can be concluded that eSHOE delivers adequately accurate results. Especially with the prospect as an at home supplement or follow-up to clinical gait analysis and compared to other state of the art wearable motion analysis systems.

In order to perform long-term, low-effort gait analysis an instrumented shoe insole, equipped with an embedded data processing system, a variety of sensors and a wireless data transmission module has been developed. By using specially developed signal fusion algorithms, the sensor's raw data, like pressure, 3D tilt and acceleration, is processed to provide information about the user's gait or moving behaviour. This shoe insole will also form the basis of an "easy to use" balance training system for older people, in order to help decreasing their risk of falling. A set of early prototypes, in the form of two pairs of self-designed and crafted instrumented shoe insoles, has already been developed. For the validation of their functionality a small series of tests, with five users, already took place. A specific test battery was created, consisting of ten tasks, where the stability of gait and coordinative skills were observed. The tasks were compiled from state-of-the-art mobility test and fall assessment strategies. The tests should, on the one hand, prove the stability of the hardware (to endure such testing), the reliability of the wireless connection and should at the same time show, if the procedure of walking can be reproduced from the gathered data. On the other hand these tests were used for gathering gait data from different subjects, which will be used in the future for the training of classification algorithms. Furthermore there is the opportunity that such a wearable system can be used for sophisticated running analysis with qualitative parameters. By logging and/or wirelessly transmitting information about pressure distribution, the course of e.g. the COP and the pitch angle as well as quantitative parameters like time of action, cadence and the number of steps e.g. an activity protocol can be created.

The majority of stroke patients experience deficits in motoric functions, especially in gait and mobility. They need rehabilitation to regain walking independence, which is a major goal of rehabilitation after stroke. To document and assess the rehabilitation progress, instrumented motion analysis and clinical assessments are commonly used. In a clinical pilot study the applicability of an instrumented insole system in stroke rehabilitation is evaluated. Motion parameter of 35 stroke patients were gathered with the system while completing 90 s level walking and Timed Up & Go test at the beginning and end of four weeks inpatient rehabilitation. For level walking the motion parameter were gathered with the clinical reference system simultaneously. The mean stride time for level walking decreased from 1.20 s to 1.16 s (clinical system), or from 1.19 s to 1.12 s (insole system), respectively. Focusing on individual comparison of each patient's progress, 9 gait parameters are extracted for level walking, 6 sub-phases of Timed Up & Go test are detected and analyzed, as well as progress of Center of Pressure in the sub-phases is examined. Although the overall data show wide distribution, the system proofed to be applicable in clinical stroke rehabilitation routine. As the system is location-independent, and has the advantage of assessing additional parameters of the Timed Up & Go test, it is additionally suitable for integration in a tele-or home rehabilitation system.

Summary Background: Preservation of mobility in conjunction with an independent life style is one of the major goals of rehabilitation after stroke. Objectives: The Rehab@Home framework shall support the continuation of rehabilitation at home. Methods: The framework consists of instrumented insoles, connected wirelessly to a 3G ready tablet PC, a server, and a web-interface for medical experts. The rehabilitation progress is estimated via automated analysis of movement data from standardized assessment tests which are designed according to the needs of stroke patients and executed via the tablet PC application. Results: The Rehab@Home framework’s implementation is finished and ready for the field trial (at five patients’ homes). Initial testing of the automated evaluation of the standardized mobility tests shows reproducible results. Conclusions: Therefore it is assumed that the Rehab@Home framework is applicable as monitoring tool for the gait rehabilitation progress in stroke patients.

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