Publications

Large doses of movement practice have been shown to restore upper extremities' motor function in a significant subset of individuals post-stroke. However, such large doses are both difficult to implement in the clinic and highly inefficient. In addition, an important reduction in upper extremity function and use is commonly seen following rehabilitation-induced gains, resulting in “rehabilitation in vain”. For those with mild to moderate sensorimotor impairment, the limited spontaneous use of the more affected limb during activities of daily living has been previously proposed to cause a decline of motor function, initiating a vicious cycle of recovery, in which non-use and poor performance reinforce each other. Here, we review computational, experimental, and clinical studies that support the view that if arm use is raised above an effective threshold, one enters a virtuous cycle in which arm use and function can reinforce each other via self-practice in the wild. If not, one enters a vicious cycle of declining arm use and function. In turn, and in line with best practice therapy recommendations, this virtuous/vicious cycle model advocates for a paradigm shift in neurorehabilitation whereby rehabilitation be embedded in activities of daily living such that self-practice with the aid of wearable technology that reminds and motivates can enhance paretic limb use of those who possess adequate residual sensorimotor capacity. Altogether, this model points to a user-centered approach to recovery post-stroke that is tailored to the participant's level of arm use and designed to motivate and engage in self-practice through progressive success in accomplishing meaningful activities in the wild. Copyright © 2022 Ballester, Winstein and Schweighofer.

JTD Keywords: compensatory movement, computational neurorehabilitation, decision-making, individuals, learned non-use, learned nonuse, monkeys, neurorehabilitation, recovery, rehabilitation, stroke, stroke patients, wearable sensors, wrist, Arm movement, Article, Cerebrovascular accident, Clinical decision making, Clinical practice, Clinical study, Compensatory movement, Computational neurorehabilitation, Computer model, Daily life activity, Decision-making, Experimental study, Human, Induced movement therapy, Learned non-use, Musculoskeletal function, Neurorehabilitation, Paresis, Sensorimotor function, Stroke, Stroke rehabilitation, User-centered design, Vicious cycle, Virtuous cycle, Wearable sensors

Under intrauterine growth restriction (IUGR), abnormal attainment of the nutrients and oxygen by the fetus restricts the normal evolution of the prenatal causing in many cases high morbidity being one of the top-ten causes of neonatal death. The current gold standards in hospitals to detect this relevant problem is the clinical observation by echography, cardiotocography and Doppler. These qualitative techniques are not conclusive and requires risky invasive fetal scalp blood testing and/or amniocentesis. We developed micro-implantable multiparametric electrochemical sensors for measuring ischemia in real time in fetal tissue and vascular. This implantable technology is designed to continuous monitoring for an early detection of ischemia to avoid potential fetal injury. Two miniaturized electrochemical sensors were developed based on oxygen and pH detection. The sensors were optimized in vitro under controlled concentration, to assess the selectivity and sensitivity required. The sensors were then validated in vivo in the ewe fetus model, by means of their insertion in the muscle leg and inside the iliac artery of the fetus. Ischemia was achieved by gradually obstructing the umbilical cord to regulate the amount of blood reaching the fetus. An important challenge in fetal monitoring is the detection of low levels of oxygen and pH changes under ischemic conditions, requiring high sensitivity sensors. Significant differences were observed in both; pH and pO(2) sensors under changes from normoxia to hypoxia states in the fetus tissue and vascular with both sensors. Herein, we demonstrate the feasibility of the developed sensors for future fetal monitoring in medical applications.

JTD Keywords: electrochemical biosensor, implantable sensor, in vivo validation, ischemia detection, tissue and vascular monitoring, Animal experiment, Animal model, Animal tissue, Article, Blood-gases, Brain, Classification, Controlled study, Diagnosis, Doppler, Early diagnosis, Electrochemical analysis, Electrochemical biosensor, Ewe, Feasibility study, Female, Fetus, Fetus disease, Fetus monitoring, Gestational age, Hypoxemia, Iliac artery, Implantable sensor, In vivo validation, Intrauterine growth restriction, Intrauterine growth retardation, Ischemia detection, Leg muscle, Management, Nonhuman, Oxygen consumption, Ph, Ph and oxygen detection, Ph measurement, Process optimization, Sheep, Tissue and vascular monitoring, Umbilical-cord occlusion

After a stroke, a great number of patients experience persistent motor impairments such as hemiparesis or weakness in one entire side of the body. As a result, the lack of use of the paretic limb might be one of the main contributors to functional loss after clinical discharge. We aim to reverse this cycle by promoting the use of the paretic limb during activities of daily living (ADLs). To do so, we describe the key components of a system composed of a wearable bracelet (i.e., a smartwatch) and a mobile phone, designed to bring a set of neurorehabilitation principles that promote acquisition, retention and generalization of skills to the home of the patient. A fundamental question is whether the loss in motor function derived from learned–non–use may emerge as a consequence of decision–making processes for motor optimization. Our system is based on well-established rehabilitation strategies that aim to reverse this behaviour by increasing the reward associated with action execution and implicitly reducing the expected cost of using the paretic limb, following the notion of reinforcement–induced movement therapy (RIMT). Here we validate an accelerometer-based measure of arm use and its capacity to discriminate different activities that require increasing movement of the arm. The usability and acceptance of the device as a rehabilitation tool is tested using a battery of self–reported and objective measurements obtained from acute/subacute patients and healthy controls. We believe that an extension of these technologies will allow for the deployment of unsupervised rehabilitation paradigms during and beyond hospitalization time. © 2021, Springer Nature Switzerland AG.

JTD Keywords: adls, hemiparesis, learned non-use, wearables, Activities of daily living, Adls, Functional loss, Generalisation, Hemiparesis, Learned non-use, Motor impairments, Neurorehabilitation [], Patient experiences, Stroke, Wearable devices, Wearable technology, Wearables

Many studies have focused on novel noninvasive techniques to monitor respiratory rate such as bioimpedance. We propose an algorithm to detect respiratory phases using wearable bioimpedance to compute time parameters like respiratory rate, inspiratory and expiratory times, and duty cycle. The proposed algorithm was compared with two other algorithms from literature designed to estimate the respiratory rate using physiological signals like bioimpedance. We acquired bioimpedance and airflow from 50 chronic obstructive pulmonary disease (COPD) patients during an inspiratory loading protocol. We compared performance of the algorithms by computing accuracy and mean average percentage error (MAPE) between the bioimpedance parameters and the reference parameters from airflow. We found similar performance for the three algorithms in terms of accuracy (>0.96) and respiratory time and rate errors (<3.42 %). However, the proposed algorithm showed lower MAPE in duty cycle (10.18 %), inspiratory time (10.65 %) and expiratory time (8.61 %). Furthermore, only the proposed algorithm kept the statistical differences in duty cycle between COPD severity levels that were observed using airflow. Accordingly, we suggest bioimpedance to monitor breathing pattern parameters in home situations.Clinical relevance - This study exhibits the suitability of wearable thoracic bioimpedance to detect respiratory phases and to compute accurate breathing pattern parameters. © 2021 IEEE.

JTD Keywords: algorithms, copd, signals, Algorithm, Algorithms, Bioimpedance, Breathing rate, Chronic obstructive lung disease, Electronic device, Human, Humans, Lung, Pulmonary disease, chronic obstructive, Respiratory rate, Wearable electronic devices

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

JTD Keywords: autoregressive moving average processes, feature extraction, medical signal processing, patient care, pneumodynamics, signal classification, support vector machines, time series, ARX, autoregressive modeling techniques, autoregressive models with exogenous input, autoregressive moving average model, breath duration time series, classification method, classifier method, discontinuing mechanical ventilation, expiratory time, feature extraction, final prediction errors, fractional inspiratory time, intensive care, interquartile range, linear discriminant analysis, logistic regression analysis, mean inspiratory flow, patient respiratory volume signal, pressure support level, pressure support ventilation, rapid shallow breathing, respiratory pattern variability characterization, support vector machines, tidal volume, weaning trial, Analytical models, Autoregressive processes, Biological system modeling, Estimation, Support vector machines, Time series analysis, Ventilation