Closed-loop parameter optimization for patient-specific phrenic nerve stimulation.

BACKGROUND: Ventilator-induced diaphragm dysfunction occurs rapidly following the onset of mechanical ventilation and has significant clinical consequences. Phrenic nerve stimulation has shown promise in maintaining diaphragm function by inducing diaphragm contractions. Non-invasive stimulation is an attractive option as it minimizes the procedural risks associated with invasive approaches. However, this method is limited by sensitivity to electrode position and inter-individual variability in stimulation thresholds. This makes clinical application challenging due to potentially time-consuming calibration processes to achieve reliable stimulation. METHODS: We applied non-invasive electrical stimulation to the phrenic nerve in the neck in healthy volunteers. A closed-loop system recorded the respiratory flow produced by stimulation and automatically adjusted the electrode position and stimulation amplitude based on the respiratory response. By iterating over electrodes, the optimal electrode was selected. A binary search method over stimulation amplitudes was then employed to determine an individualized stimulation threshold. Pulse trains above this threshold were delivered to produce diaphragm contraction. RESULTS: Nine healthy volunteers were recruited. Mean threshold stimulation amplitude was 36.17 ± 14.34 mA (range 19.38-59.06 mA). The threshold amplitude for reliable nerve capture was moderately correlated with BMI (Pearson's r = 0.66, p = 0.049). Repeating threshold measurements within subjects demonstrated low intra-subject variability of 2.15 ± 1.61 mA between maximum and minimum thresholds on repeated trials. Bilateral stimulation with individually optimized parameters generated reliable diaphragm contraction, resulting in significant inhaled volumes following stimulation. CONCLUSION: We demonstrate the feasibility of a system for automatic optimization of electrode position and stimulation parameters using a closed-loop system. This opens the possibility of easily deployable individualized stimulation in the intensive care setting to reduce ventilator-induced diaphragm dysfunction.

Embedded Electronic Sensor for Monitoring of Breathing Activity, Fitting and Filter Clogging in Reusable Industrial Respirators.

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.

Gait Subphases Classification Based on Hidden Markov Models using in-shoes Capacitive Pressure Sensors: Preliminary Results.

Gait cycle analysis is widely practiced to determine alterations of normal walking. The challenge is to choose the ideal systems that suit the studies. One possibility is to measure the interaction of the sole and the support surface and detect gait events related to the positioning of the foot. This work proposes a gait subphase classification based on Hidden Markov Model that identifies gait stance subphases from a foot pressure measurement. A sensorized insole was used to record the pressure under the foot with eight custom-made capacitive sensors. Tests were performed on six volunteers with a 10-meter trial test. Mean cadence and stance/swing ratio were calculated. These parameters match the normal range for the age of the volunteers found in the literature. The results show that the proposed model can classify the gait in 5 subphases using the Center of Pressure (CoP) anteroposterior position and velocity as input. Changes in the slope of the CoP marks the step between subphases. Clinical Relevance- Most gait studies are performed in highly equipped gait laboratories. Due to technical requirements and the high cost of implementing a gait laboratory, access to these services is difficult for a large population. For this reason, it is necessary to develop equipment, devices, and algorithm to further study pathological and healthy gait.

Exploring Silicone Rubber Skin with Embedded Customizable Shape Capacitive Sensors to Enable Haptic Capabilities on Upper Limb Prosthetics.

Commercially available electromechanical prosthetic devices still lack touch-sensing capabilities, and there is a huge gap between research devices and commercially available ones. There is a need for small flexible touch sensors with high accuracy and sensitivity for this type of device. Touch sensors in prosthetic devices are needed for feedback mechanisms to the user and to achieve high dexterity in control schemes for fragile objects. A brief review of prosthetic touch sensors is presented, addressing desirable characteristics for touch sensing. In this paper, a custom shape flexible capacitive touch sensor is designed and characterized, meeting prosthetic sensors needs, such as thickness, power consumption, accuracy, repeatability, and stability. The designed sensor presented the capability to distinguish up to 0.5N steps with good stability. The sensor accomplished a full sensing range between 5N and 100N with reasonable accuracy, and hysteresis analysis achieved an average of 8.8 %. Clinical Relevance- The custom shape capacitive sensors proposed in this paper contribute to the development of tactile sensors for prosthetic devices as more accurate and sensitive sensor interfaces are required to detect and improve manipulating capabilities.

Static Balance Characterization using a single IMU Located in the Lower Back: Preliminary Results.

Balance refers to the dynamics of body posture to prevent falls. For years, researchers have tried to find out which tasks and measures provide optimal detection of balance disorders, so that they can be quantified. This paper proposes the use of an accelerometer sensor located in the lower back to measure the center of mass accelerations and to characterize the subject's static balance. For characterizing the static balance objectively, we propose using normality circles, a centroid, and a dispersion circle during the modified Clinical Test of Sensory Interaction in Balance (mCTSIB) test. The proposed methodology was tested using two groups of subjects (10 healthy and 3 unhealthy). Our methodology for the static balance was compared to the Berg Balance Scale score. The results shown that a subject with lower BBS score obtain lower dispersion circle and is outside the normality circle. Also, our method outperforms a new option since it characterizes the static balance in an objective, portable, simple, and low-cost way. Clinical Relevance- Our proposed methodology to characterize the static balance can help to simplify objectify and reduce the cost of the clinical practice for balance evaluation.

Non-invasive phrenic nerve stimulation to avoid ventilator-induced diaphragm dysfunction in critical care.

BACKGROUND: Diaphragm muscle atrophy during mechanical ventilation begins within 24 h and progresses rapidly with significant clinical consequences. Electrical stimulation of the phrenic nerves using invasive electrodes has shown promise in maintaining diaphragm condition by inducing intermittent diaphragm muscle contraction. However, the widespread application of these methods may be limited by their risks as well as the technical and environmental requirements of placement and care. Non-invasive stimulation would offer a valuable alternative method to maintain diaphragm health while overcoming these limitations. METHODS: We applied non-invasive electrical stimulation to the phrenic nerve in the neck in healthy volunteers. Respiratory pressure and flow, diaphragm electromyography and mechanomyography, and ultrasound visualization were used to assess the diaphragmatic response to stimulation. The electrode positions and stimulation parameters were systematically varied in order to investigate the influence of these parameters on the ability to induce diaphragm contraction with non-invasive stimulation. RESULTS: We demonstrate that non-invasive capture of the phrenic nerve is feasible using surface electrodes without the application of pressure, and characterize the stimulation parameters required to achieve therapeutic diaphragm contractions in healthy volunteers. We show that an optimal electrode position for phrenic nerve capture can be identified and that this position does not vary as head orientation is changed. The stimulation parameters required to produce a diaphragm response at this site are characterized and we show that burst stimulation above the activation threshold reliably produces diaphragm contractions sufficient to drive an inspired volume of over 600 ml, indicating the ability to produce significant diaphragmatic work using non-invasive stimulation. CONCLUSION: This opens the possibility of non-invasive systems, requiring minimal specialist skills to set up, for maintaining diaphragm function in the intensive care setting.

A Novel Capacitive Step Sensor to Trigger Stimulation on Functional Electrical Stimulators Devices for Drop Foot.

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.

Step capacitive array sensor to trigger stimulation on Functional Electrical Stimulators devices for Drop Foot: Preliminary results.

In this work, a new custom wireless capacitive step sensor and a real-time algorithm are proposed to detect the start and end of the swing phase of the gait to trigger the stimulation in Functional Electrical Stimulator devices (FES) for Drop Foot. For this, an array of capacitive pressure sensors was designed to detect patterns of the gait swing phase through the Heel Center of Pressure (HCOP). The proposed system recognized all the events with an average error of 20.86±0.02[ms] for the heel strike (initial contact) and 27.60±0.03[ms] for the heel-off (final contact) compared with lower-back accelerometer, constituting a viable, robust and promising alternative as a step sensor for functional electrical stimulators.

Gait Segmentation Method Using a Plantar Pressure Measurement System with Custom-Made Capacitive Sensors.

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.

Capacitive Sensors Array for Plantar Pressure Measurement Insole fabricated with Flexible PCB.

Diabetic foot is a pathology associated with diabetic neuropathy, where the vast majority of diabetic foot infections terminate in surgical intervention; from debridement to amputation of the involved limb. Commonly, diabetic foot infections come from ulceration produced by high-pressure areas under the foot. For this reason, researchers have been working on a continuous measurement system to detect the high-pressure areas in-shoe in a low cost way. This paper presents the design and implementation of a continuous monitoring device to measure the pressure in-shoe. The pressure sensors are built from commercial flexible PCB and a dielectric sheet. The system measures the pressure distribution in 8 points and sends the information by a wireless Bluetooth link to a personal computer and gives information to the patient in real time.