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Paolo Bonato

Bio: Paolo Bonato is an academic researcher from Spaulding Rehabilitation Hospital. The author has contributed to research in topics: Wearable computer & Gait analysis. The author has an hindex of 52, co-authored 269 publications receiving 12237 citations. Previous affiliations of Paolo Bonato include Polytechnic University of Turin & Wyss Institute for Biologically Inspired Engineering.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a review of wearable sensors and systems that are relevant to the field of rehabilitation is presented, focusing on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders.
Abstract: The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

1,826 citations

Journal ArticleDOI
01 Nov 2009
TL;DR: This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease, and a support vector machine (SVM) classifier was implemented to estimateThe severity of tremor, bradykinesia and dyskinesian symptoms from accelerometers data features.
Abstract: This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease. A support vector machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different SVM kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms and motor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.

563 citations

Journal ArticleDOI
TL;DR: Three distinct patterns of muscle coordination that reflect the multiple neural responses that occur after cortical damage are revealed and can be used as physiological markers of the status of any patient with stroke or trauma, thereby guiding the development of different rehabilitation approaches.
Abstract: The experimental findings herein reported are aimed at gaining a perspective on the complex neural events that follow lesions of the motor cortical areas. Cortical damage, whether by trauma or stroke, interferes with the flow of descending signals to the modular interneuronal structures of the spinal cord. These spinal modules subserve normal motor behaviors by activating groups of muscles as individual units (muscle synergies). Damage to the motor cortical areas disrupts the orchestration of the modules, resulting in abnormal movements. To gain insights into this complex process, we recorded myoelectric signals from multiple upper-limb muscles in subjects with cortical lesions. We used a factorization algorithm to identify the muscle synergies. Our factorization analysis revealed, in a quantitative way, three distinct patterns of muscle coordination—including preservation, merging, and fractionation of muscle synergies—that reflect the multiple neural responses that occur after cortical damage. These patterns varied as a function of both the severity of functional impairment and the temporal distance from stroke onset. We think these muscle-synergy patterns can be used as physiological markers of the status of any patient with stroke or trauma, thereby guiding the development of different rehabilitation approaches, as well as future physiological experiments for a further understanding of postinjury mechanisms of motor control and recovery.

482 citations

Journal ArticleDOI
TL;DR: The work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD is summarized.
Abstract: The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society.

428 citations

Journal ArticleDOI
TL;DR: This special issue is introduced by four commentaries of physicians who share with the readership their vision on future clinical applications of wearable technology, thus pointing out its tremendous potential.
Abstract: ecent advances in miniature devices, as well as mobile and ubiquitous computing, have fostered a dramatic growth of interest for wearable technology. Wearable sensors and systems have evolved to the point that they can be considered ready for clinical application. This is due not only to the tremendous increase in research efforts devoted to this area in the past few years but also to the large number of companies that have recently started investing aggressively in the development of wearable products for clinical applications. Stable trends showing a growth in the use of this technology suggest that soon wearable systems will be part of routine clinical evaluations. The interest for wearable systems originates from the need for monitoring patients over extensive periods of time. This case arises when physicians want to monitor individuals whose chronic condition includes risk of sudden acute events or individuals for whom interventions need to be assessed in the home and outdoor environment. If observations over one or two days are satisfactory, ambulatory systems can be utilized to gather physiological data. An obvious example is the use of ambulatory systems for ECG monitoring, which has been part of the routine evaluation of cardiovascular patients for almost three decades. However, ambulatory systems are not suitable when monitoring has to be accomplished over periods of several weeks or months, as is desirable in a number of clinical applications. Wearable systems are totally nonobtrusive devices that allow physicians to overcome the limitations of ambulatory technology and provide a response to the need for monitoring individuals over weeks or even months. They typically rely on wireless, miniature sensors enclosed in patches or bandages, or in items that can be worn, such as a ring or a shirt. They take advantage of hand-held units to temporarily store physiological data and then periodically upload that data to a database server via a wireless LAN or a cradle that allow Internet connection. The data sets recorded using these systems are then processed to detect events predictive of possible worsening of the patient’s clinical situation or they are explored to assess the impact of clinical interventions. All these aspects of wearable technology are covered by this special issue, which is introduced by four commentaries of physicians who share with the readership their vision on future clinical applications of wearable technology, thus pointing out its tremendous potential. Phil Binkley, M.D., professor of medicine at The Ohio State University Division of Cardiology, shares with us his vision on potential applications of wearable devices in cardiovascular research and clinical practice. Walter Frontera, M.D., Ph.D., chairman of the Department of Physical Medicine and Rehabilitation at Harvard Medical School, provides us with a clinician’s viewpoint of the dramatic improvements in patient management that wearable devices could foster. David G. Standaert, M.D., Ph.D., associate professor of neurology, Harvard Medical School and associate neurologist at Massachusetts General Hospital, points out in his commentary the clinical relevance and potential outcomes of monitoring motor fluctuations in patients with Parkinson’s disease. Finally, Joel Stein, M.D., director of the stroke program and chief medical officer at Spaulding Rehabilitation Hospital, highlights the need for monitoring poststroke hemiplegic patients in the home and outdoor environment in order to assess the impact of clinical interventions and plan more effective rehabilitation strategies. The first five articles of this special issue are focused on the development of sensors and systems. In the first article, Asada et al. describe the evolution of the ring sensor over the past eight years. This is likely the most renowned project in the area of wearable devices. The result of several years of work is a pulse oximetry sensor that allows one to continuously monitor heart rate and oxygen saturation in a totally unobtrusive way. The device is shaped like a ring and thus it can be worn for long periods of time without any discomfort to the subject. The ring sensor is equipped with a low-power transceiver that accomplishes bidirectional communication with a base station, thus allowing one to reconfigure the sensor when necessary and to upload data at any point in time. In a nutshell, this is a “jewel” in the wearable technology arena. The second article, by Park and Jayaraman, demonstrates the great impact on the clinical potential of wearable systems of the Georgia Tech Wearable Motherboard, the result of a revolutionary idea that allowed Dr. Jayaraman’s team to develop a garment (i.e., a shirt) that actually functions as a wearable health monitoring system. This concept has been developed into a product that is now commercially available and allows one to record heart rate, body temperature, motion, position, barrier penetration, and the like in a totally nonencumbering manner. Park and Jayaraman point out a

355 citations


Cited by
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01 Jan 2002

9,314 citations

01 Jan 2020
TL;DR: Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future.
Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

4,408 citations

Journal ArticleDOI
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.

3,848 citations

Journal ArticleDOI
28 Jan 2016-Nature
TL;DR: This work bridges the technological gap between signal transduction, conditioning, processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing.
Abstract: Wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual's state of health. Sampling human sweat, which is rich in physiological information, could enable non-invasive monitoring. Previously reported sweat-based and other non-invasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanically flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing. This application could not have been realized using either of these technologies alone owing to their respective inherent limitations. The wearable system is used to measure the detailed sweat profile of human subjects engaged in prolonged indoor and outdoor physical activities, and to make a real-time assessment of the physiological state of the subjects. This platform enables a wide range of personalized diagnostic and physiological monitoring applications.

3,235 citations