A wearable multi-sensor system for real world gait analysis.

Gait analysis is commonly performed in standardized environments, but there is a growing interest in assessing gait also in ecological conditions. In this regard, an important limitation is the lack of an accurate mobile gold standard for validating any wearable system, such as continuous monitoring devices mounted on the trunk or wrist. This study therefore deals with the development and validation of a new wearable multi-sensor-based system for digital gait assessment in free-living conditions. In particular, results obtained from five healthy subjects during lab-based and real-world experiments were presented and discussed. The in-lab validation, which assessed the accuracy and reliability of the proposed system, shows median percentage errors smaller than 2% in the estimation of spatio-temporal parameters. The system also proved to be easy to use, comfortable to wear and robust during the out-of-lab acquisitions, showing its feasibility for free-living applications.

A method for gait events detection based on low spatial resolution pressure insoles data.

The accurate identification of initial and final foot contacts is a crucial prerequisite for obtaining a reliable estimation of spatio-temporal parameters of gait. Well-accepted gold standard techniques in this field are force platforms and instrumented walkways, which provide a direct measure of the foot-ground reaction forces. Nonetheless, these tools are expensive, non-portable and restrict the analysis to laboratory settings. Instrumented insoles with a reduced number of pressure sensing elements might overcome these limitations, but a suitable method for gait events identification has not been adopted yet. The aim of this paper was to present and validate a method aiming at filling such void, as applied to a system including two insoles with 16 pressure sensing elements (element area = 310 mm2), sampling at 100 Hz. Gait events were identified exploiting the sensor redundancy and a cluster-based strategy. The method was tested in the laboratory against force platforms on nine healthy subjects for a total of 801 initial and final contacts. Initial and final contacts were detected with low average errors of (about 20 ms and 10 ms, respectively). Similarly, the errors in estimating stance duration and step duration averaged 20 ms and <10 ms, respectively. By selecting appropriate thresholds, the method may be easily applied to other pressure insoles featuring similar requirements.

DoMoMEA: a Home-Based Telerehabilitation System for Stroke Patients.

After a cerebral stroke, survivors need to follow a neurorehabilitation program including exercises to be executed under a therapist's supervision or autonomously. Technological solutions are needed to support the early discharge of the patients just after the primary hospital treatments, by still providing an adequate level of rehabilitation. The DoMoMEA Project proposes a fully-wearable m-health solution able to administer a neurorehabilitation therapy in the patient's home or every other place established by the patient for a rehabilitation session. The exploitation of magneto-inertial measurement units only, wirelessly connected to an Android-operated device, provides robustness to different operating conditions and immunity to optical occlusion problems, compared to RGB-D cameras. Patients' engagement is fostered by the exploitation of the exergame version of the ten rehabilitation exercises, implemented in Unity 3D. Store-and-forward telemonitoring features, supported by cloud-based storage and by a web application accessible from anywhere by medical personnel and patients, enable constant transparent monitoring of the rehabilitation progresses. The clinical trial of the DoMoMEA telerehabilitation system will involve 40 post-stroke patients with mild impairment and will start as soon as the restrictions due to the COVID-19 pandemic will allow to enroll patients.

Inter-leg Distance Measurement as a Tool for Accurate Step Counting in Patients with Multiple Sclerosis.

Step detection is commonly performed using wearable inertial devices. However, methods based on the extraction of signals features may deteriorate their accuracy when applied to very slow walkers with abnormal gait patterns. The aim of this study is to test and validate an innovative step counter method (DiSC) based on the direct measurement of inter-leg distance. Data were recorded using an innovative wearable system which integrates a magneto-inertial unit and multiple distance sensors (DSs) attached to the shank. The method allowed for the detection of both left and right steps using a single device and was validated on thirteen people affected by multiple sclerosis (0 <; EDSS <; 6.5) while performing a six-minute walking test. Two different measurement ranges for the distance sensor were tested (DS200: 0-200 mm; DS400: 0-400 mm). Accuracy was evaluated by comparing the estimates of the DiSC method against video recordings used as gold standard. Preliminary results showed a good accuracy in detecting steps with half the errors in detecting the step of the instrumented side compared to the non-instrumented (mean absolute percentage error 2.4% vs 4.8% for DS200; mean absolute percentage error 2% vs 5.4% for DS400). When averaging errors across patients, over and under estimation errors were compensated, and very high accuracy was achieved (E%<; 1.2% for DS200; E%<; 0.7% for DS400). DS400 is the suggested configuration for patients walking with a large base of support.