Showing papers by "Paolo Bonato published in 2021"

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Abstract: Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning.

Abstract: Background It is not clear how specific gait measures reflect disease severity across the disease spectrum in Parkinson's disease (PD). Objective To identify the gait and mobility measures that are most sensitive and reflective of PD motor stages and determine the optimal sensor location in each disease stage. Methods Cross-sectional wearable-sensor records were collected in 332 patients with PD (Hoehn and Yahr scale I-III) and 100 age-matched healthy controls. Sensors were adhered to the participant's lower back, bilateral ankles, and wrists. Study participants walked in a ~15-meter corridor for 1 minute under two walking conditions: (1) preferred, usual walking speed and (2) walking while engaging in a cognitive task (dual-task). A subgroup (n = 303, 67% PD) also performed the Timed Up and Go test. Multiple machine-learning feature selection and classification algorithms were applied to discriminate between controls and PD and between the different PD severity stages. Results High discriminatory values were found between motor disease stages with mean sensitivity in the range 72%-83%, specificity 69%-80%, and area under the curve (AUC) 0.76-0.90. Measures from upper-limb sensors best discriminated controls from early PD, turning measures obtained from the trunk sensor were prominent in mid-stage PD, and stride timing and regularity were discriminative in more advanced stages. Conclusions Applying machine-learning to multiple, wearable-derived features reveals that different measures of gait and mobility are associated with and discriminate distinct stages of PD. These disparate feature sets can augment the objective monitoring of disease progression and may be useful for cohort selection and power analyses in clinical trials of PD. © 2021 International Parkinson and Movement Disorder Society.

Recommendation to Use Wearable-Based mHealth in Closed-Loop Management of Acute Cardiovascular Disease Patients During the COVID-19 Pandemic

Abstract: Because of the rapid and serious nature of acute cardiovascular disease (CVD) especially ST segment elevation myocardial infarction (STEMI), a leading cause of death worldwide, prompt diagnosis and treatment is of crucial importance to reduce both mortality and morbidity. During a pandemic such as coronavirus disease-2019 (COVID-19), it is critical to balance cardiovascular emergencies with infectious risk. In this work, we recommend using wearable device based mobile health (mHealth) as an early screening and real-time monitoring tool to address this balance and facilitate remote monitoring to tackle this unprecedented challenge. This recommendation may help to improve the efficiency and effectiveness of acute CVD patient management while reducing infection risk.

Predicting and Monitoring Upper-Limb Rehabilitation Outcomes Using Clinical and Wearable Sensor Data in Brain Injury Survivors

Abstract: Objective: Rehabilitation specialists have shown considerable interest for the development of models, based on clinical data, to predict the response to rehabilitation interventions in stroke and traumatic brain injury survivors. However, accurate predictions are difficult to obtain due to the variability in patients’ response to rehabilitation interventions. This study aimed to investigate the use of wearable technology in combination with clinical data to predict and monitor the recovery process and assess the responsiveness to treatment on an individual basis. Methods: Gaussian Process Regression-based algorithms were developed to estimate rehabilitation outcomes (i.e., Functional Ability Scale scores) using either clinical or wearable sensor data or a combination of the two. Results: The algorithm based on clinical data predicted rehabilitation outcomes with a Pearson's correlation of 0.79 compared to actual clinical scores provided by clinicians but failed to model the variability in responsiveness to the intervention observed across individuals. In contrast, the algorithm based on wearable sensor data generated rehabilitation outcome estimates with a Pearson's correlation of 0.91 and modeled the individual responses to rehabilitation more accurately. Furthermore, we developed a novel approach to combine estimates derived from the clinical data and the sensor data using a constrained linear model. This approach resulted in a Pearson's correlation of 0.94 between estimated and clinician-provided scores. Conclusion: This algorithm could enable the design of patient-specific interventions based on predictions of rehabilitation outcomes relying on clinical and wearable sensor data. Significance: This is important in the context of developing precision rehabilitation interventions.

Accelerometer data collected with a minimum set of wearable sensors from subjects with Parkinson's disease.

Abstract: Parkinson's disease (PD) is a neurodegenerative disorder associated with motor and non-motor symptoms Current treatments primarily focus on managing motor symptom severity such as tremor, bradykinesia, and rigidity However, as the disease progresses, treatment side-effects can emerge such as on/off periods and dyskinesia The objective of the Levodopa Response Study was to identify whether wearable sensor data can be used to objectively quantify symptom severity in individuals with PD exhibiting motor fluctuations Thirty-one subjects with PD were recruited from 2 sites to participate in a 4-day study Data was collected using 2 wrist-worn accelerometers and a waist-worn smartphone During Days 1 and 4, a portion of the data was collected in the laboratory while subjects performed a battery of motor tasks as clinicians rated symptom severity The remaining of the recordings were performed in the home and community settings To our knowledge, this is the first dataset collected using wearable accelerometers with specific focus on individuals with PD experiencing motor fluctuations that is made available via an open data repository

The Role Played by Mass, Friction, and Inertia on the Driving Torques of Lower-Limb Gait Training Exoskeletons

Abstract: Lower-limb gait training exoskeletons are extraordinary tools used to reduce the burden of locomotor impairments in patients with neurological diseases. However, the transparent operation and backdrivability of such systems still needs to be improved. Moreover, it is not completely understood how the mechanical design of the robot can interfere with the user’s gait pattern. Aiming to address these shortcomings, we investigate the required driving torques and mechanical power to move the legs under a wide range of actuator’s mass, inertia and friction and thigh/shank lengths. We used the ExoRoboWalker, a six-degree-of-freedom lower-limb exoskeleton, to build a framework model based on the double-pendulum approach integrated with the actuators’ mechanical impedance. Decoupled joint apparent inertia and the Rayleigh’s dissipation function were introduced to the robot’s Lagrangian to consider the effects of gearhead ratio and joint friction in the model. Firstly, it is presented the isolated effect of such variables on the required driving torques of the system. The oscillation frequency for the minimum joint torque was severely affected by variations of inertia, friction, and links length. Secondly, the combined effect of the actuator’s mass, inertia and friction reveled that a heavier exoskeleton with low-ratio transmission required less torque and mechanical power than a lighter one with greater reduction ratio depending on the oscillation frequency, which is remarkable. These findings have important implications for new designs of lower-limb gait training systems.

Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge

Abstract: Consumer wearables and sensors are a rich source of data about patients’ daily disease and symptom burden, particularly in the case of movement disorders like Parkinson’s disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).

Cortical correlates in upright dynamic and static balance in the elderly

Abstract: Falls are the second most frequent cause of injury in the elderly. Physiological processes associated with aging affect the elderly’s ability to respond to unexpected balance perturbations, leading to increased fall risk. Every year, approximately 30% of adults, 65 years and older, experiences at least one fall. Investigating the neurophysiological mechanisms underlying the control of static and dynamic balance in the elderly is an emerging research area. The study aimed to identify cortical and muscular correlates during static and dynamic balance tests in a cohort of young and old healthy adults. We recorded cortical and muscular activity in nine elderly and eight younger healthy participants during an upright stance task in static and dynamic (core board) conditions. To simulate real-life dual-task postural control conditions, the second set of experiments incorporated an oddball visual task. We observed higher electroencephalographic (EEG) delta rhythm over the anterior cortex in the elderly and more diffused fast rhythms (i.e., alpha, beta, gamma) in younger participants during the static balance tests. When adding a visual oddball, the elderly displayed an increase in theta activation over the sensorimotor and occipital cortices. During the dynamic balance tests, the elderly showed the recruitment of sensorimotor areas and increased muscle activity level, suggesting a preferential motor strategy for postural control. This strategy was even more prominent during the oddball task. Younger participants showed reduced cortical and muscular activity compared to the elderly, with the noteworthy difference of a preferential activation of occipital areas that increased during the oddball task. These results support the hypothesis that different strategies are used by the elderly compared to younger adults during postural tasks, particularly when postural and cognitive tasks are combined. The knowledge gained in this study could inform the development of age-specific rehabilitative and assistive interventions.

Muscular and cortical activation during dynamic and static balance in the elderly: A scoping review

Abstract: Falls due to balance impairment are a major cause of injury and disability in the elderly. The study of neurophysiological correlates during static and dynamic balance tasks is an emerging area of research that could lead to novel rehabilitation strategies and reduce fall risk. This review aims to highlight key concepts and identify gaps in the current knowledge of balance control in the elderly that could be addressed by relying on surface electromyographic (EMG) and electroencephalographic (EEG) recordings. The neurophysiological hypotheses underlying balance studies in the elderly as well as the methodologies, findings, and limitations of prior work are herein addressed. The literature shows: 1) a wide heterogeneity in the experimental procedures, protocols, and analyses; 2) a paucity of studies involving the investigation of cortical activity; 3) aging-related alterations of cortical activation during balance tasks characterized by lower cortico-muscular coherence and increased allocation of attentional control to postural tasks in the elderly; and 4) EMG patterns characterized by delayed onset after perturbations, increased levels of activity, and greater levels of muscle co-activation in the elderly compared to younger adults. EMG and EEG recordings are valuable tools to monitor muscular and cortical activity during the performance of balance tasks. However, standardized protocols and analysis techniques should be agreed upon and shared by the scientific community to provide reliable and reproducible results. This will allow researchers to gain a comprehensive knowledge on the neurophysiological changes affecting static and dynamic balance in the elderly and will inform the design of rehabilitative and preventive interventions.

Effect of using of a lower-extremity exoskeleton on disability of people with multiple sclerosis

Abstract: Although ongoing exercise is known to reduce disability in people with multiple sclerosis (MS), participation in lower-extremity exercise programs can be limited by their existing mobility impairme...

Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson's disease.

Abstract: Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13574279

From Hand-Perspective Visual Information to Grasp Type Probabilities: Deep Learning via Ranking Labels

Abstract: Limb deficiency severely affects the daily lives of amputees and drives efforts to provide functional robotic prosthetic hands to compensate this deprivation. Convolutional neural network-based computer vision control of the prosthetic hand has received increased attention as a method to replace or complement physiological signals due to its reliability by training visual information to predict the hand gesture. Mounting a camera into the palm of a prosthetic hand is proved to be a promising approach to collect visual data. However, the grasp type labelled from the eye and hand perspective may differ as object shapes are not always symmetric. Thus, to represent this difference in a realistic way, we employed a dataset containing synchronous images from eye- and hand- view, where the hand-perspective images are used for training while the eye-view images are only for manual labelling. Electromyogram (EMG) activity and movement kinematics data from the upper arm are also collected for multi-modal information fusion in future work. Moreover, in order to include human-in-the-loop control and combine the computer vision with physiological signal inputs, instead of making absolute positive or negative predictions, we build a novel probabilistic classifier according to the Plackett-Luce model. To predict the probability distribution over grasps, we exploit the statistical model over label rankings to solve the permutation domain problems via a maximum likelihood estimation, utilizing the manually ranked lists of grasps as a new form of label. We indicate that the proposed model is applicable to the most popular and productive convolutional neural network frameworks.

The impact of chronotype on circadian rest-activity rhythm and sleep characteristics across the week

Abstract: Circadian rhythms are maintained by a complex "system of systems" that continuously coordinates biological processes with each other and the environment. Although humans predominantly entrain to solar time, individual persons vary in their precise behavioral timing due to endogenous and exogenous factors. Endogenous differences in the timing of individual circadian rhythms relative to a common environmental cue are known as chronotypes, ranging from earlier than average (Morningness) to later than average (Eveningness). Furthermore, individual behavior is often constrained by social constructs such as the 7-day week, and the "sociogenic" impact our social calendar has on our behavioral rhythms is likely modified by chronotype. Our aim in this study was to identify and characterize differences in sleep and rest-activity rhythms (RAR) between weekends and weekdays and between-chronotypes. Male volunteers (n = 24, mean age = 23.46 y) were actigraphically monitored for 4 weeks to derive objective behavioral measures of sleep and RARs. Chronotype was assessed through self-report on the Morningness-Eveningness Questionnaire. Sleep characteristics were derived using Actiware; daily rest-activity rhythms were modeled using a basic 3-parameter cosinor function. We observed that both Eveningness and Morningness Chronotypes were more active and slept later on the weekends than on weekdays. Significant between-chronotype differences in sleep timing and duration were observed within individual days of the week, especially during transitions between weekends and the workweek. Moreover, chronotypes significantly varied in their weekly rhythms: e.g. Morningness Chronotypes generally shifted their sleep duration, timing and quality across work/rest transitions quicker than Eveningness Chronotypes. Although our results should be interpreted with caution due to the limitations of our cosinor model and a homogenous cohort, they reinforce a growing body of evidence that day of the week, chronotype and their interactions must be accounted for in observational studies of human behavior, especially when circadian rhythms are of interest.

Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control

Abstract: For lower arm amputees, robotic prosthetic hands offer the promise to regain the capability to perform fine object manipulation in activities of daily living. Current control methods based on physiological signals such as EEG and EMG are prone to poor inference outcomes due to motion artifacts, variability of skin electrode junction impedance over time, muscle fatigue, and other factors. Visual evidence is also susceptible to its own artifacts, most often due to object occlusion, lighting changes, variable shapes of objects depending on view-angle, among other factors. Multimodal evidence fusion using physiological and vision sensor measurements is a natural approach due to the complementary strengths of these modalities. In this paper, we present a Bayesian evidence fusion framework for grasp intent inference using eye-view video, gaze, and EMG from the forearm processed by neural network models. We analyze individual and fused performance as a function of time as the hand approaches the object to grasp it. For this purpose, we have also developed novel data processing and augmentation techniques to train neural network components. Our experimental data analyses demonstrate that EMG and visual evidence show complementary strengths, and as a consequence, fusion of multimodal evidence can outperform each individual evidence modality at any given time. Specifically, results indicate that, on average, fusion improves the instantaneous upcoming grasp type classification accuracy while in the reaching phase by 13.66% and 14.8%, relative to EMG and visual evidence individually. An overall fusion accuracy of 95.3% among 13 labels (compared to a chance level of 7.7%) is achieved, and more detailed analysis indicate that the correct grasp is inferred sufficiently early and with high confidence compared to the top contender, in order to allow successful robot actuation to close the loop.

Evaluation of a lower-extremity robotic exoskeleton for people with knee osteoarthritis.

Abstract: A multi-site study was conducted to evaluate the efficacy of the Keeogo™ exoskeleton as a mobility assist device for use in the clinic and at home in people with knee osteoarthritis (KOA) Twenty-f