Showing papers by "Paolo Bonato published in 2019"

Gait impairments in Parkinson's disease.

Abstract: Summary Gait impairments are among the most common and disabling symptoms of Parkinson's disease. Nonetheless, gait is not routinely assessed quantitatively but is described in general terms that are not sensitive to changes ensuing with disease progression. Quantifying multiple gait features (eg, speed, variability, and asymmetry) under natural and more challenging conditions (eg, dual-tasking, turning, and daily living) enhanced sensitivity of gait quantification. Studies of neural connectivity and structural network topology have provided information on the mechanisms of gait impairment. Advances in the understanding of the multifactorial origins of gait changes in patients with Parkinson's disease promoted the development of new intervention strategies, such as neurostimulation and virtual reality, aimed at alleviating gait impairments and enhancing functional mobility. For clinical applicability, it is important to establish clear links between specific gait impairments, their underlying mechanisms, and disease progression to foster the acceptance and usability of quantitative gait measures as outcomes in future disease-modifying clinical trials.

A roadmap for implementation of patient‐centered digital outcome measures in Parkinson's disease obtained using mobile health technologies

Abstract: Obtaining reliable longitudinal information about everyday functioning from individuals with Parkinson's disease (PD) in natural environments is critical for clinical care and research. Despite advances in mobile health technologies, the implementation of digital outcome measures is hindered by a lack of consensus on the type and scope of measures, the most appropriate approach for data capture (eg, in clinic or at home), and the extraction of timely information that meets the needs of patients, clinicians, caregivers, and health care regulators. The Movement Disorder Society Task Force on Technology proposes the following objectives to facilitate the adoption of mobile health technologies: (1) identification of patient-centered and clinically relevant digital outcomes; (2) selection criteria for device combinations that offer an acceptable benefit-to-burden ratio to patients and that deliver reliable, clinically relevant insights; (3) development of an accessible, scalable, and secure platform for data integration and data analytics; and (4) agreement on a pathway for approval by regulators, adoption into e-health systems and implementation by health care organizations. We have developed a tentative roadmap that addresses these needs by providing the following deliverables: (1) results and interpretation of an online survey to define patient-relevant endpoints, (2) agreement on the selection criteria for use of device combinations, (3) an example of an open-source platform for integrating mobile health technology output, and (4) recommendations for assessing readiness for deployment of promising devices and algorithms suitable for regulatory approval. This concrete implementation guidance, harmonizing the collaborative endeavor among stakeholders, can improve assessments of individuals with PD, tailor symptomatic therapy, and enhance health care outcomes. © 2019 International Parkinson and Movement Disorder Society.

The Parkinson's disease e-diary: Developing a clinical and research tool for the digital age.

Abstract: Joaquin A. Vizcarra, MD, Alvaro Sánchez-Ferro, MD, PhD, Walter Maetzler, MD, Luca Marsili, MD, PhD, Lucia Zavala, MD, Anthony E. Lang, MD, FRCPC, Pablo Martinez-Martin, MD, PhD, Tiago A. Mestre, MD, MSc, Ralf Reilmann, MD, Jeffrey M. Hausdorff, PhD, E. Ray Dorsey, MD, MBA, Serene S. Paul, PhD, Judith W. Dexheimer, PhD, Benjamin D. Wissel, BS, Rebecca L. M. Fuller, PhD, Paolo Bonato, PhD, Ai Huey Tan, MD, MRCP, Bastiaan R. Bloem, MD, PhD, Catherine Kopil, PhD, Margaret Daeschler, BA, Lauren Bataille, MS, Galit Kleiner, MD, FRCPC, Jesse M. Cedarbaum, MD, Jochen Klucken, MD, Aristide Merola, MD, PhD, Christopher G. Goetz, MD, Glenn T. Stebbins, PhD, and Alberto J. Espay, MD, MSc,* on behalf of the MDS Technology Task Force and the MDS Rating Scales Program Electronic Development Ad-Hoc Committee

The Use of a Finger-Worn Accelerometer for Monitoring of Hand Use in Ambulatory Settings

Abstract: Objective assessment of stroke survivors’ upper limb movements in ambulatory settings can provide clinicians with important information regarding the real impact of rehabilitation outside the clinic and help to establish individually-tailored therapeutic programs. This paper explores a novel approach to monitor the amount of hand use, which is relevant to the purposeful, goal-directed use of the limbs, based on a body networked sensor system composed of miniaturized finger- and wrist-worn accelerometers. The main contributions of this paper are twofold. First, this paper introduces and validates a new benchmark measurement of the amount of hand use based on data recorded by a motion capture system, the gold standard for human movement analysis. Second, this paper introduces a machine learning-based analytic pipeline that estimates the amount of hand use using data obtained from the wearable sensors and validates its estimation performance against the aforementioned benchmark measurement. Based on data collected from 18 neurologically intact individuals performing 11 motor tasks resembling various activities of daily living, the analytic results presented herein show that our new benchmark measure is reliable and responsive, and that the proposed wearable system can yield an accurate estimation of the amount of hand use (normalized root mean square error of 0.11 and average Pearson correlation of 0.78). This study has the potential to open up new research and clinical opportunities for monitoring hand function in ambulatory settings, ultimately enabling evidence-based, patient-centered rehabilitation and healthcare.

A novel upper-limb function measure derived from finger-worn sensor data collected in a free-living setting.

Abstract: The use of wrist-worn accelerometers has recently gained tremendous interest among researchers and clinicians as an objective tool to quantify real-world use of the upper limbs during the performance of activities of daily living (ADLs). However, wrist-worn accelerometers have shown a number of limitations that hinder their adoption in the clinic. Among others, the inability of wrist-worn accelerometers to capture hand and finger movements is particularly relevant to monitoring the performance of ADLs. This study investigates the use of finger-worn accelerometers to capture both gross arm and fine hand movements for the assessment of real-world upper-limb use. A system of finger-worn accelerometers was utilized to monitor eighteen neurologically intact young adults while performing nine motor tasks in a laboratory setting. The system was also used to monitor eighteen subjects during the day time of a day in a free-living setting. A novel measure of real-world upper-limb function-comparing the duration of activities of the two limbs-was derived to identify which upper limb subjects predominantly used to perform ADLs. Two validated handedness self-reports, namely the Waterloo Handedness Questionnaire and the Fazio Laterality Inventory, were collected to assess convergent validity. The analysis of the data recorded in the laboratory showed that the proposed measure of upper-limb function is suitable to accurately detect unilateral vs. bilateral use of the upper limbs, including both gross arm movements and fine hand movements. When applied to recordings collected in a free-living setting, the proposed measure showed high correlation with self-reported handedness indices (i.e., ρ = 0.78 with the Waterloo Handedness Questionnaire scores and ρ = 0.77 with the Fazio Laterality Inventory scores). The results herein presented establish face and convergent validity of the proposed measure of real-world upper-limb function derived using data collected by means of finger-worn accelerometers.

Development of a “transparent operation mode” for a lower-limb exoskeleton designed for children with cerebral palsy

Abstract: Robot-assisted rehabilitation in children and young adults with Cerebral Palsy (CP) is expected to lead to neuroplasticity and reduce the burden of motor impairments. For a lower-limb exoskeleton to perform well in this context, it is essential that the robot be "transparent" to the user and produce torques only when voluntarily-generated motor outputs deviate significantly from the target trajectory. However, the development of transparent operation modes and assistance-as-need control schema are still open problems with several implementation challenges. This paper presents a theoretical approach and provides a discussion of the key issues pertinent to designing a transparent operation mode for a lower-limb exoskeleton suitable for children and young adults with CP. Based on the dynamics of exoskeletons as well as friction models and human-robot interaction models, we propose a control strategy aimed to minimize human-machine interaction forces when subjects generate motor outputs that match the target trajectory. The material is presented as a conceptual framework that can be generalized to other exoskeleton systems for overground walking.

Towards the Design of a Ring Sensor-based mHealth System to Achieve Optimal Motor Function in Stroke Survivors

Abstract: Maximizing the motor practice in stroke survivors' living environments may significantly improve the functional recovery of their stroke-affected upper-limb. A wearable system that can continuously monitor upper-limb performance has been considered as an effective clinical solution for its potential to provide patient-centered, data-driven feedback to improve the motor dosage. Towards that end, we investigate a system leveraging a pair of finger-worn, ring-type accelerometers capable of monitoring both gross-arm and fine-hand movements that are clinically relevant to the performance of daily activities. In this work, we conduct a mixed-methods study to (1) quantitatively evaluate the efficacy of finger-worn accelerometers in measuring clinically relevant information regarding stroke survivors' upper-limb performance, and (2) qualitatively investigate design requirements for the self-monitoring system, based on data collected from 25 stroke survivors and seven occupational therapists. Our quantitative findings demonstrate strong face and convergent validity of the finger-worn accelerometers, and its responsiveness to changes in motor behavior. Our qualitative findings provide a detailed account of the current rehabilitation process while highlighting several challenges that therapists and stroke survivors face. This study offers promising directions for the design of a self-monitoring system that can encourage the affected limb use during stroke survivors' daily living.

Assessing aberrant muscle activity patterns via the analysis of surface EMG data collected during a functional evaluation

Abstract: Surface electromyographic (EMG) recordings collected during the performance of functional evaluations allow clinicians to assess aberrant patterns of muscle activity associated with musculoskeletal disorders. This assessment is typically achieved via visual inspection of the surface EMG data. This approach is time-consuming and leads to accurate results only when the assessment is carried out by an EMG expert. A set of algorithms was developed to automatically evaluate aberrant patterns of muscle activity. EMG recordings collected during the performance of functional evaluations in 62 subjects (22 to 61 years old) were used to develop and characterize the algorithms. Clinical scores were generated via visual inspection by an EMG expert using an ordinal scale capturing the severity of aberrant patterns of muscle activity. The algorithms were used in a case study (i.e. the evaluation of a subject with persistent back pain following instrumented lumbar fusion who underwent lumbar hardware removal) to assess the clinical suitability of the proposed technique. The EMG-based algorithms produced accurate estimates of the clinical scores. Results were primarily obtained using a linear regression approach. However, when the results were not satisfactory, a regression implementation of a Random Forest was utilized, and the results compared with those obtained using a linear regression approach. The root-mean-square error of the clinical score estimates produced by the algorithms was a small fraction of the ordinal scale used to rate the severity of the aberrant patterns of muscle activity. Regression coefficients and associated 95% confidence intervals showed that the EMG-based estimates fit well the clinical scores generated by the EMG expert. When applied to the clinical case study, the algorithms appeared to capture the characteristics of the muscle activity patterns associated with persistent back pain following instrumented lumbar fusion. The proposed approach relies on EMG-based measures to generate accurate estimates of the severity of aberrant patterns of muscle activity. The results obtained in the case study suggest that the proposed technique is suitable to derive clinically-relevant information from EMG data collected during functional evaluations.

State-of-the-art and SWOT analysis of building integrated solar envelope systems : deliverables A.1 and A.2

Abstract: The present document includes a state-of-the-art review of solar envelope systems that are already on the market or that can potentially reach that stage in a short-medium timeframe. The analysis focuses on the technological integration of such solutions in the envelope and building, but non-technical issues such as aesthetic, architectural integration and customer acceptance are also tackled. The solar envelope systems are classified in: Solar harvesting systems: systems that generate electricity or heat; Solar gains control systems, controlling; Hybrid systems: combination of solar harvesting and solar gains control systems.

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.