Gait analysis has been widely studied by researchers due to the impact in clinical fields. It provides relevant information on the condition of a patient’s pathologies. In the last decades, different gait measurement methods have been developed in order to identify parameters that can contribute to gait cycles. Analyzing those parameters, it is possible to segment and identify different phases of gait cycles, making these studies easier and more accurate. This paper proposes a simple gait segmentation method based on plantar pressure measurement. Current methods used by researchers and clinicians are based on multiple sensing devices (e.g., multiple cameras, multiple inertial measurement units (IMUs)). Our proposal uses plantar pressure information from only two sensorized insoles that were designed and implemented with eight custom-made flexible capacitive sensors. An algorithm was implemented to calculate gait parameters and segment gait cycle phases and subphases. Functional tests were performed in six healthy volunteers in a 10 m walking test. The designed in-shoe insole presented an average power consumption of 44 mA under operation. The system segmented the gait phases and sub-phases in all subjects. The calculated percentile distribution between stance phase time and swing phase time was almost 60%/40%, which is aligned with literature reports on healthy subjects. Our results show that the system achieves a successful segmentation of gait phases and subphases, is capable of reporting COP velocity, double support time, cadence, stance phase time percentage, swing phase time percentage, and double support time percentage. The proposed system allows for the simplification of the assessment method in the recovery process for both patients and clinicians.

Millions of workers are required to wear reusable respirators in several industries worldwide. Reusable respirators include filters that protect workers against harmful dust, smoke, gases, and vapors. These hazards may cause cancer, lung impairment, and diseases. Respiratory protection is prone to failure or misuse, such as wearing respirators with filters out of service life and employees wearing respirators loosely. Currently, there are no commercial systems capable of reliably alerting of misuse of respiratory protective equipment during the workday shifts or provide early information about dangerous clogging levels of filters. This paper proposes a low energy and non-obtrusive functional building block with embedded electronics that enable breathing monitoring inside an industrial reusable respirator. The embedded electronic device collects multidimensional data from an integrated pressure, temperature, and relative humidity sensor inside a reusable industrial respirator in real time and sends it wirelessly to an external platform for further processing. Here, the calculation of instantaneous breathing rate and estimation of the filter’s respirator fitting and clogging level is performed. The device was tested with ten healthy subjects in laboratory trials. The subjects were asked to wear industrial reusable respirator with the embedded electronic device attached inside. The signals measured with the system were compared with airflow signals measured with calibrated transducers for validation purposes. The correlation between the estimated breathing rates using pressure, temperature, and relative humidity with the reference signal (airflow) is 0.987, 0.988 and 0.989 respectively, showing that instantaneous breathing rate can be calculated accurately using the information from the embedded device. Moreover, respirator fitting (well-fitted or loose condition) and filter’s clogging levels (≤60%, 80% and 100% clogging) also can be estimated using features extracted from absolute pressure measurements combined to statistical analysis ANOVA models. These experimental outputs represent promising results for further development of data-driven prediction models using machine learning techniques to determine filters end-of-service life. Furthermore, the proposed system would collect relevant data for real-time monitoring of workers’ breathing conditions and respirator usage, helping to improve occupational safety and health in the workplace.

Drop foot is a typical clinical condition associated with stroke. According to the World Health Organization, fifteen million people suffer a stroke per year, and one of three people’s survival gets drop foot. Functional Electrical Stimulation systems are applied over the peroneal motor nerve to achieve the drop foot problem’s dorsiflexion. An accurate and reliable way to identify in real–time the gait phases to trigger and finish the stimulation is needed. This paper proposes a new step sensor with a custom capacitive pressure sensors array located under the heel to detect a gait pattern in real-time to synchronize the stimulation with the user gait. The step sensor uses a capacitive pressure sensors array and hardware, which acquire the signals, execute an algorithm to detect the start and finish of the swing phase in real–time, and send the synchronization signal wirelessly. The step sensor was tested in two ways: 10 meters walk test and walking in a treadmill for 2 minutes. These two tests were performed with two different walk velocities and with thirteen healthy volunteers. Thus, all the 1342 steps were correctly detected. Compared to an inertial sensor located in the lower–back, the proposed step sensor achieves a mean error of 27.60±0.03 ms for the detection of the start of the swing phase and a mean error of 20.86±0.02 ms for the detection of the end of the swing phase. The results show an improvement in time error (respect to others pressure step sensors), sensibility and specificity (both 100%), and comfortability.

This paper presents methods for FEM modelling the peripheral and central nervous systems with considerations for meshing and computational constraints. FEM models in this context are convenient for testing hypothesises about the effects of different stimulation parameters and exploring different electrode designs before moving to in vitro and in vivo experiments. The methods presented in this paper are motivated by assessing differentiation errors from different mesh sizes and the transitions between different materials in the model. We aim to support the development of transparent and reproducible modelling experiments. Accurate and reproducible models are essential, given the importance of the applications in which these models are used. However, a dearth of literature is devoted to promoting best practices in finite element modelling for biophysical models. We evaluate the impact of differentiation errors on calculating the Activating Function and predicting action potentials in a Hodgkin-Huxley (H-H) axon model. We found that poor spatial discretisation facilitates the generation of double-derivative noise. However, it does not generate false predictions of action potentials on the H-H model. Activation thresholds were higher (57.5 mA) for coarser meshes than Fine and Extremely Fine (55 mA). Implementing Multiscale meshes with the finest refined sizes reduced material transition discontinuities reflected in the activating function calculation. Our findings support using the finest spatial discretisations possible within computational constraints, which may rely on adaptive meshing techniques. We advocate coupling the extracellular field to H-H-based axons to further limit potential error sources.