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.

Instrumented Timed Up and Go Test (iTUG)-More Than Assessing Time to Predict Falls: A Systematic Review.

The Timed Up and Go (TUG) test is a widely used tool for assessing the risk of falls in older adults. However, to increase the test's predictive value, the instrumented Timed Up and Go (iTUG) test has been developed, incorporating different technological approaches. This systematic review aims to explore the evidence of the technological proposal for the segmentation and analysis of iTUG in elderlies with or without pathologies. A search was conducted in five major databases, following PRISMA guidelines. The review included 40 studies that met the eligibility criteria. The most used technology was inertial sensors (75% of the studies), with healthy elderlies (35%) and elderlies with Parkinson's disease (32.5%) being the most analyzed participants. In total, 97.5% of the studies applied automatic segmentation using rule-based algorithms. The iTUG test offers an economical and accessible alternative to increase the predictive value of TUG, identifying different variables, and can be used in clinical, community, and home settings.

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.

Predicting risk of falls in elderly using a single Inertial Measurement Unit on the lower-back by estimating spatio-temporal gait parameters.

One of the consequences of aging is the increased risk of falls, especially when someone walks in unknown or uncontrolled environments. Usually, gait is evaluated through observation and clinical assessment scales to identify the state and deterioration of the patient's postural control. Lately, technological systems for bio-mechanical analysis have been used to determine abnormal gait states being expensive, difficult to use, and impossible to apply in real conditions. In this article, we explore the ability of a system based on a single inertial measurement unit located in the lower back to estimate spatio-temporal gait parameters by analyzing the signals available in the Physionet repository "Long Term Movement Monitoring Database" which, together with automatic classification algorithms, allow predicting the risk of falls in the elderly population. Different classification algorithms were trained and evaluated, being the Support Vector Machine classifier with a third-degree polynomial kernel, cost function C = 2 with the best performance, with an Accuracy = 59%, Recall = 91%, and F1- score = 71%, providing promising results regarding a proposal for the quantitative, online and realistic evaluation of gait during activities of daily living, which is where falls actually occur in the target population. Clinical Relevance - This work proposes an early risk of falls detection tool, essential to start preventive treatment strategies to maintain the independence of the elderly through a non-invasive, simple, and low-cost alternative.

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.

A Novel Privacy Preservation and Quantification Methodology for Implementing Home-Care-Oriented Movement Analysis Systems.

Human movement is generally evaluated through both observations and clinical assessment scales to identify the state and deterioration of a patient's motor control. Lately, technological systems for human motion analysis have been used in clinics to identify abnormal movement states, while they generally suffer from privacy challenges and concerns especially at home or in remote places. This paper presents a novel privacy preservation and quantification methodology that imitates the forgetting process of human memory to protect privacy in patient-centric healthcare. The privacy preservation principle of this methodology is to change the traditional data analytic routines into a distributed and disposable form (i.e., DnD) so as to naturally minimise the disclosure of patients' health data. To help judge the efficacy of DnD-based privacy preservation, the researchers further developed a risk-driven privacy quantification framework to supplement the existing privacy quantification techniques. To facilitate validating the methodology, this research also involves a home-care-oriented movement analysis system that comprises a single inertial measurement sensor and a mobile application. The system can acquire personal information, raw data of movements and indexes to evaluate the risk of falls and gait at homes. Moreover, the researchers conducted a technological appreciation survey of 16 health professionals to help understand the perception of this research. The survey obtains positive feedback regarding the movement analysis system and the proposed methodology as suitable for home-care scenarios.

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.

Carbamazepine Biosensor Development for Epilepsy Patient Screening.

The San Carlos population in Chile is an example of an underserved community due to lack of timely access to regular controls and laboratory results. One particular challenge is the adherence to treatment of Epilepsy patients. In this work, we present the design and proof-of-concept of a Point of Care Device (POCD) to measure carbamazepine levels in saliva to screen for correct dose prescription among epilepsy patients. We present the Screen Printed Electrode design and activating circuit and preliminary results to verify feasibility of the biosensor. Future steps include the fabrication of the device itself and validation with the target population.

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.

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.

Validation of a portable system for spatial-temporal gait parameters based on a single inertial measurement unit and a mobile application.

There is a lack of commercially available low-cost technologies to assess gait clinically in non-controlled environments. As a consequence of this, there has been poor massification of motion measurement technologies that are both objective and reliable in nature. Advances about the study of gait and its interpretation in recent years using inertial sensors have allowed proposing acceptable alternatives for the development of portable and low-cost systems that contribute to people's health in places and institutions that cannot acquire or maintain the operation of commercially available systems. A system based on a custom single Inertial Measurement Unit and a mobile application is proposed. Thus, an investigation is carried out using methodologies and algorithms found in the literature in order to get the main gait events and the spatial-temporal gait parameters. Twenty healthy Chilean subjects were assessed using a motion capture system simultaneously with the proposed tool. The results show that it is possible to estimate temporal gait parameters with slight differences respect gold--standard. We reach maximum mean differences of -2.35±5.02[step/min] for cadence, 0.03±0.04[sec] for stride time,0.02±0.03[sec] for step time, ±0.02[sec] for a single support time, 0.01±0.02[sec] for double support time and 0.01±0.03[m] for step length. As a result of experimental findings, we propose a new technological tool that can perform gait analysis. Our proposed system is user-friendly, low-cost, and portable. Therefore, we suggest that it could be an attractive technological tool that healthcare professionals could harness to objectively measure gait in environments that are either within the community or controlled. We also suggest that the tool could be used in countries where advanced clinical tools cannot be acquired. Therefore, we propose in this paper that our system is an attractive, alternative system that can be used for gait analysis by health professionals worldwide.

Use of a Single Wireless IMU for the Segmentation and Automatic Analysis of Activities Performed in the 3-m Timed Up & Go Test.

Falls represent a major public health problem in the elderly population. The Timed Up & Go test (TU & Go) is the most used tool to measure this risk of falling, which offers a unique parameter in seconds that represents the dynamic balance. However, it is not determined in which activity the subject presents greater difficulties. For this, a feature-based segmentation method using a single wireless Inertial Measurement Unit (IMU) is proposed in order to analyze data of the inertial sensors to provide a complete report on risks of falls. Twenty-five young subjects and 12 older adults were measured to validate the method proposed with an IMU in the back and with video recording. The measurement system showed similar data compared to the conventional test video recorded, with a Pearson correlation coefficient of 0.9884 and a mean error of 0.17 ± 0.13 s for young subjects, as well as a correlation coefficient of 0.9878 and a mean error of 0.2 ± 0.22 s for older adults. Our methodology allows for identifying all the TU & Go sub­tasks with a single IMU automatically providing information about variables such as: duration of sub⁻tasks, standing and sitting accelerations, rotation velocity of turning, number of steps during walking and turns, and the inclination degrees of the trunk during standing and sitting.

Wireless Low-Cost Bioimpedance Measurement Device for Lung Capacity Screening.

This paper presents the development and implementation at a prototype level of a wireless, low-cost system for the measurement of the electrical bio-impedance of the chest with two channels using the AD5933 in a bipolar electrode configuration to measure lung volume variation. 15 volunteers were measured with the prototype, and the acquired signal presents the phases of the respiratory cycle, useful for the breathing rate calculation and for possible screening applications.

Android Platform for Realtime Gait Tracking Using Inertial Measurement Units.

One of the most important movements performed by the humans is gait. Biomechanical Gait analysis is usually by optical capture systems. However, such systems are expensive and sensitive to light and obstacles. In order to reduce those costs a system based on Inertial Measurements Units (IMU) is proposed. IMU are a good option to make movement analisys indoor with a low post-processing data, allowing to connect those systems to an Android platform. The design is based on two elements: a) The IMU sensors and the b) Android device. The IMU sensor is simple, small (35 x 35 mm), portable and autonomous (7.8 hrs). A resolution of 0.01° in their measurements is obtained, and sends data via Bluetooth link. The Android application works for Android 4.2 or higher, and it is compatible with Bluetooth devices 2.0 or higher. Three IMU sensors send data to a Tablet wirelessly, in order to evaluate the angles evolution for each joint of the leg (hip, knee and ankle). This information is used to calculate gait index and evaluate the gait quality online during the physical therapist is working with the patient.