A 3D-Printed Capacitive Smart Insole for Plantar Pressure Monitoring.

Gait analysis refers to the systematic study of human locomotion and finds numerous applications in the fields of clinical monitoring, rehabilitation, sports science and robotics. Wearable sensors for real-time gait monitoring have emerged as an attractive alternative to the traditional clinical-based techniques, owing to their low cost and portability. In addition, 3D printing technology has recently drawn increased interest for the manufacturing of sensors, considering the advantages of diminished fabrication cost and time. In this study, we report the development of a 3D-printed capacitive smart insole for the measurement of plantar pressure. Initially, a novel 3D-printed capacitive pressure sensor was fabricated and its sensing performance was evaluated. The sensor exhibited a sensitivity of 1.19 MPa−1, a wide working pressure range (<872.4 kPa), excellent stability and durability (at least 2.280 cycles), great linearity (R2=0.993), fast response/recovery time (142−160 ms), low hysteresis (DH<10%) and the ability to support a broad spectrum of gait speeds (30−70 steps/min). Subsequently, 16 pressure sensors were integrated into a 3D-printed smart insole that was successfully applied for dynamic plantar pressure mapping and proven able to distinguish the various gait phases. We consider that the smart insole presented here is a simple, easy to manufacture and cost-effective solution with the potential for real-world applications.

Gait and balance patterns related to Free-Walking and TUG tests in Parkinson's Disease based on plantar pressure data.

Continuous monitoring of patients with Parkinson's Disease (PD) is critical for their effective management, as early detection of improvement or degradation signs play an important role on pharmaceutical and/or interventional plans. Within this work, a group of seven PD patients and a group of ten controls performed a set of exercises related to the evaluation of PD gait. Plantar pressure signals were collected and used to derive a set of analytics. Statistical tests and feature selection approaches revealed that the spatial distribution of the Center of Pressure during a static balance exercise is the most discriminative analytic and may be used for every-day monitoring of the patients. Results have revealed that out of the 28 features extracted from the collected signals, 10 were statistically significant (p < 0.05) and can be used to machine learning algorithms and/or similar approaches.

The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson's Patients.

Gait analysis is crucial for the detection and management of various neurological and musculoskeletal disorders. The identification of gait events is valuable for enhancing gait analysis, developing accurate monitoring systems, and evaluating treatments for pathological gait. The aim of this work is to introduce the Smart-Insole Dataset to be used for the development and evaluation of computational methods focusing on gait analysis. Towards this objective, temporal and spatial characteristics of gait have been estimated as the first insight of pathology. The Smart-Insole dataset includes data derived from pressure sensor insoles, while 29 participants (healthy adults, elderly, Parkinson's disease patients) performed two different sets of tests: The Walk Straight and Turn test, and a modified version of the Timed Up and Go test. A neurologist specialized in movement disorders evaluated the performance of the participants by rating four items of the MDS-Unified Parkinson's Disease Rating Scale. The annotation of the dataset was performed by a team of experienced computer scientists, manually and using a gait event detection algorithm. The results evidence the discrimination between the different groups, and the verification of established assumptions regarding gait characteristics of the elderly and patients suffering from Parkinson's disease.

Applicability of SSVEP-based brain-computer interfaces for robot navigation in real environments.

Brain-computer interfaces have been extensively studied and used in order to aid patients suffering from neuromuscular diseases to communicate and control the surrounding environment. Steady-state visual evoked potentials (SSVEP) constitute a very popular BCI stimulation protocol, due to their efficiency and quick response time. In this study, we developed a SSVEP-based BCI along with a low-cost custom radio-controlled robot-car providing live video feedback from a wireless camera mounted on the robot, serving as our testbed. Our goal was to quantitatively assess the applicability of SSVEPs in real time navigation in realistic environments using a pragmatic approach. In order to assess the additional fatigue that the camera video introduces, we designed a two-session experiment, a control one with no connection to the robot and, thus, no live camera feed, and a realistic one where the users could navigate the robot with the provision of front scenes, captured from the camera. Statistical tests revealed a significant decrease of the accuracy of the system during the realistic session that included live video, in comparison with the session that did not. The results suggest that the moving camera image sequence introduces an extra level of fatigue and/or distraction to the users.