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.

Development of a Platform to Assess the Risk of Musculoskeletal Disorders in Manual Load Handling Activities - Preliminary Results.

Risk identification on workstations is a crucial step to prevent the occurrence of musculoskeletal disorders (MSD) in workers. The available methods and tools used by ergonomists to assess and estimate the risk related to manual handling of loads under repetitive work cycles are usually biased by the inter-evaluator error that can lead to a subjective determination of work-related risks due to the application of, mainly, observational methods. This paper shows the preliminary results of a platform to assess the risk of musculoskeletal disorders during manual load-handling tasks using an instrumented system and using the National Institute for Occupational Safety & Health (NIOSH) method. Eight healthy subjects were measured during lifting activities using an optical-based and inertial-based motion capture systems. The developed software implements a semi-automated instrumented version of the NIOSH method, helping the evaluator with automated calculations of body segment locations, displacements and joint angles making it possible to obtain a objective risk classification. Also, we achieved a reduction of 85% in the time for the estimation of the necessary factors for the digital evaluation methodology, making the proposed platform a promising and attractive alternative for its application in real environments for risk assessments.Occupational health relevance- This work proposes an assistance tool for the detection of musculoskeletal disorders in activities related to manual handling of loads, essential to initiate modification strategies in the workplace, reduce the occurrence of occupational diseases and reduce the time of risk classification.

Utilizing Motion Capture Systems for Instrumenting the OCRA Index: A Study on Risk Classification for Upper Limb Work-Related Activities.

In the search to enhance ergonomic risk assessments for upper limb work-related activities, this study introduced and validated the efficiency of an inertial motion capture system, paired with a specialized platform that digitalized the OCRA index. Conducted in a semi-controlled environment, the proposed methodology was compared to traditional risk classification techniques using both inertial and optical motion capture systems. The inertial method encompassed 18 units in a Bluetooth Low Energy tree topology network for activity recording, subsequently analyzed for risk using the platform. Principal outcomes emphasized the optical system's preeminence, aligning closely with the conventional technique. The optical system's superiority was further evident in its alignment with the traditional method. Meanwhile, the inertial system followed closely, with an error margin of just ±0.098 compared to the optical system. Risk classification was consistent across all systems. The inertial system demonstrated strong performance metrics, achieving F1-scores of 0.97 and 1 for "risk" and "no risk" classifications, respectively. Its distinct advantage of portability was reinforced by participants' feedback on its user-friendliness. The results highlight the inertial system's potential, mirroring the precision of both traditional and optical methods and achieving a 65% reduction in risk assessment time. This advancement mitigates the need for intricate video setups, emphasizing its potential in ergonomic assessments.

Functional brain connectivity indexes derived from low-density EEG of pre-implanted patients as VNS outcome predictors.

Objective. In 1/3 of patients, anti-seizure medications may be insufficient, and resective surgery may be offered whenever the seizure onset is localized and situated in a non-eloquent brain region. When surgery is not feasible or fails, vagus nerve stimulation (VNS) therapy can be used as an add-on treatment to reduce seizure frequency and/or severity. However, screening tools or methods for predicting patient response to VNS and avoiding unnecessary implantation are unavailable, and confident biomarkers of clinical efficacy are unclear.Approach. To predict the response of patients to VNS, functional brain connectivity measures in combination with graph measures have been primarily used with respect to imaging techniques such as functional magnetic resonance imaging, but connectivity graph-based analysis based on electrophysiological signals such as electroencephalogram, have been barely explored. Although the study of the influence of VNS on functional connectivity is not new, this work is distinguished by using preimplantation low-density EEG data to analyze discriminative measures between responders and non-responder patients using functional connectivity and graph theory metrics.Main results. By calculating five functional brain connectivity indexes per frequency band upon partial directed coherence and direct transform function connectivity matrices in a population of 37 refractory epilepsy patients, we found significant differences (p< 0.05) between the global efficiency, average clustering coefficient, and modularity of responders and non-responders using the Mann-Whitney U test with Benjamini-Hochberg correction procedure and use of a false discovery rate of 5%.Significance. Our results indicate that these measures may potentially be used as biomarkers to predict responsiveness to VNS therapy.

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.

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.

Simple Wireless Impedance Pneumography System for Unobtrusive Sensing of Respiration.

This extended paper presents the development and implementation at a prototype level of a wireless, low-cost system for the measurement of the electrical bioimpedance of the chest with two channels using the AD5933 in a bipolar electrode configuration to measure impedance pneumography. The measurement device works for impedance measurements ranging from 1 Ω to 1800 Ω. Fifteen volunteers were measured with the prototype. We found that the left hemithorax has higher impedance compared to the right hemithorax, and the acquired signal presents the phases of the respiratory cycle with variations between 1 Ω, in normal breathing, to 6 Ω in maximum inhalation events. The system can measure the respiratory cycle variations simultaneously in both hemithorax with a mean error of -0.18 ± 1.42 BPM (breaths per minute) in the right hemithorax and -0.52 ± 1.31 BPM for the left hemithorax, constituting a useful device for the breathing rate calculation and possible screening applications.

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.

Myoelectric intuitive control and transcutaneous electrical stimulation of the forearm for vibrotactile sensation feedback applied to a 3D printed prosthetic hand.

This paper presents the development of a myoelectric prosthetic hand based on a 3D printed model. A myoelectric control strategy based on artificial neural networks is implemented on a microcontroller for online position estimation. Position estimation performance achieves a correlation index of 0.78. Also a study involving transcutaneous electrical stimulation was performed to provide tactile feedback. A series of stimulations with controlled parameters were tested on five able-body subjects. A single channel stimulator was used, positioning the electrodes 8 cm on the wrist over the ulnar and median nerve. Controlling stimulation parameters such as intensity, frequency and pulse width, the subjects were capable of distinguishing different sensations over the palm of the hand. Three main sensations where achieved: tickling, pressure and pain. Tickling and pressure were discretized into low, moderate and high according to the magnitude of the feeling. The parameters at which each sensation was obtained are further discussed in this paper.