Showing papers by "Paolo Bonato published in 2009"

Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors

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.

Mercury: a wearable sensor network platform for high-fidelity motion analysis

Abstract: This paper describes Mercury, a wearable, wireless sensor platform for motion analysis of patients being treated for neuromotor disorders, such as Parkinson's Disease, epilepsy, and stroke. In contrast to previous systems intended for short-term use in a laboratory, Mercury is designed to support long-term, longitudinal data collection on patients in hospital and home settings. Patients wear up to 8 wireless nodes equipped with sensors for monitoring movement and physiological conditions. Individual nodes compute high-level features from the raw signals, and a base station performs data collection and tunes sensor node parameters based on energy availability, radio link quality, and application specific policies.Mercury is designed to overcome the core challenges of long battery lifetime and high data fidelity for long-term studies where patients wear sensors continuously 12 to 18 hours a day. This requires tuning sensor operation and data transfers based on energy consumption of each node and processing data under severe computational constraints. Mercury provides a high-level programming interface that allows a clinical researcher to rapidly build up different policies for driving data collection and tuning sensor lifetime. We present the Mercury architecture and a detailed evaluation of two applications of the system for monitoring patients with Parkinson's Disease and epilepsy.

Effects of Training With a Robot-Virtual Reality System Compared With a Robot Alone on the Gait of Individuals After Stroke

Abstract: Background and Purpose—Training of the lower extremity (LE) using a robot coupled with virtual environments has shown to transfer to improved overground locomotion. The purpose of this study was to determine whether the transfer of training of LE movements to locomotion was greater using a virtual environment coupled with a robot or with the robot alone. Methods—A single, blind, randomized clinical trial was conducted. Eighteen individuals poststroke participated in a 4-week training protocol. One group trained with the robot virtual reality (VR) system and the other group trained with the robot alone. Outcome measures were temporal features of gait measured in a laboratory setting and the community. Results—Greater changes in velocity and distance walked were demonstrated for the group trained with the robotic device coupled with the VR than training with the robot alone. Similarly, significantly greater improvements in the distance walked and number of steps taken in the community were measured for the group that trained with robot coupled with the VR. These differences were maintained at 3 months’ follow-up. Conclusions—The study is the first to demonstrate that LE training of individuals with chronic hemiparesis using a robotic device coupled with VR improved walking ability in the laboratory and the community better than robot training alone. (Stroke. 2008;40:169-174.)

Patient specific ankle-foot orthotic device

Abstract: The unique advantages of computer-controlled fabrication of a patient-specific orthotic device using an automated fabrication machine capable of following computer instructions to create 3D surface contours and new developments in non-invasive three-dimensional (3D) scanning have made it possible to acquire digital models of freeform surfaces such as the surface anatomy of the human body and to then fabricate such a patient-specific device with high precision. Such a patient-specific device brings significant improvement in patient-specific fit, comfort, and function of medical devices (and, in particular, to orthoses that require a close fit to the wearer's body to act effectively). The combination of these two technologies is ideally suited for the development of patient-specific orthotic devices. A patient specific ankle-foot orthotic device using this technology is disclosed. This exemplary device is used to help stabilize the ankle-foot region, for example, in patients with impaired gait.

Using Wearable Sensors to Monitor Physical Activities of Patients with COPD: A Comparison of Classifier Performance

Abstract: Chronic obstructive pulmonary disease (COPD) is a major public health problem. Early detection and treatment of an exacerbation in the outpatient setting are important to prevent worsening of clinical status and need for emergency room care or hospital admission. In this study we use accelerometers to capture motion data; and heart rate and respiration rate to capture physiological responses from patients with COPD as they perform a range of Activities of Daily Living (ADL) and physical exercises. We present a comparative analysis of classification performance of a set of different classification techniques and factors that affect classification performance for activity recognition based on accelerometer data. This is the first step towards building a wearable sensor monitoring system for tracking changes in physiological responses of patients with COPD with respect to their physical activity level.

Motor unit recruitment and proprioceptive feedback decrease the common drive.

Abstract: It has been documented that concurrently active motor units fire under the control of a common drive. That is, the firing rates show high correlation with near-zero time lag. This degree of correlation has been found to vary among muscles and among contractions performed at different force levels in the same muscle. This study provides an explanation indicating that motor units recruited during a contraction cause an increase in the variation (SD) and a decrease in the degree (amplitude) of the correlation of the firing rates. The degree of correlation is lower in muscles having greater spindle density. This effect appears to be mediated by the proprioceptive feedback from the spindles and possibly the Golgi tendon organs. Muscle spindles in particular respond to the mechanical excitation of the nonfused muscle fibers and provide a discordant excitation to the homonymous motoneurons, resulting in a decrease in the correlation of the firing rates of motor units. The implication of this work is that the decreased correlation of the firing rates in some muscles is not necessarily an indication of a decreased common drive from the CNS, but rather an inhibitory influence of the proprioceptive feedback from the peripheral nervous system. This explanation is useful for understanding various manifestations of the common drive reported in the literature.

Advances in wearable technology for rehabilitation.

Abstract: Assessing the impact of rehabilitation interventions on the real life of individuals is a key element of the decision-making process required to choose a rehabilitation strategy. In the past, therapists and physicians inferred the effectiveness of a given rehabilitation approach from observations performed in a clinical setting and self-reports by patients. Recent developments in wearable technology have provided tools to complement the information gathered by rehabilitation personnel via patient's direct observation and via interviews and questionnaires. A new generation of wearable sensors and systems has emerged that allows clinicians to gather measures in the home and community settings that capture patients' activity level and exercise compliance, the effectiveness of pharmacological interventions, and the ability of patients to perform efficiently specific motor tasks. Available unobtrusive sensors allow clinical personnel to monitor patients' movement and physiological data such as heart rate, respiratory rate, and oxygen saturation. Cell phone technology and the widespread access to the Internet provide means to implement systems designed to remotely monitor patients' status and optimize interventions based on individual responses to different rehabilitation approaches. This chapter summarizes recent advances in the field of wearable technology and presents examples of application of this technology in rehabilitation.

Clinical applications of wearable technology

Abstract: An important factor contributing to the process involved in choosing a rehabilitation intervention is the assessment of its impact on the real life of patients. Therapists and physicians have to infer the effectiveness of rehabilitation approaches from observations performed in the clinical setting and from patients’ feedback. Recent advances in wearable technology have provided means to supplement the information gathered using tools based on patient’s direct observation as well as interviews and questionnaires. A new generation of wearable sensors and systems has recently become available thus providing clinical personnel with a “window of observation” in the home and community settings. These tools allow one to capture patients’ activity level and exercise compliance, facilitate titration of medications in chronic patients, and provide means to assess the ability of patients to perform specific motor activities. In this paper, we review recent advances in the field of wearable technology and provide examples of application of this technology in rehabilitation.

Detecting epileptic seizures using wearable sensors

Abstract: Epileptic seizures usually consist of stereotyped motor movements in association with characteristic changes in the electroencephalogram (EEG). Accurate recognition and quantification of seizures in patients with epilepsy is essential for diagnosis, selection of treatment and assessing the effects of therapy. In this paper, we present some preliminary results from our on-going research study on using wearable sensors to detect epileptic seizures. Examples are presented from data recorded on one patient.

Optimizing deep brain stimulation settings using wearable sensing technology

Abstract: Parkinson's disease is a neurodegenerative movement disorder resulting in rigidity, bradykinesia (slowness), tremor and gait disorder. Deep brain stimulation (DBS) of the subthalamic nucleus has been shown to be effective in managing symptoms, but quantitative methods to facilitate the adjustment of the stimulator settings are needed. In this paper, we present preliminary results from a study aimed at investigating the use of wearable sensors to quantitatively track changes in the severity of symptoms in patients with Parkinson's disease undergoing programming of the stimulator. We developed a technique that relies upon features derived from wearable sensors to track changes in the severity of symptoms over a period during which patient's motor activities are monitored. Preliminary results indicate that wearable sensors could be utilized to help clinicians achieve optimal settings of the stimulator by providing quantitative feedback concerning the impact of different settings on the severity of Parkinsonian symptoms.

Variable Resistance Hand Device using an electro-rheological fluid damper

Abstract: This paper presents the design, fabrication, control and testing of the third generation prototype of a novel, one degree of freedom (DOF) Variable Resistance Hand Device (VRHD) that was designed for isotonic, isokinetic, and variable resistance grasp and release exercises. Its principle functionality is derived from an electro-rheological fluid based controllable damper that allows continuously variable modulation of dynamic resistance throughout its stroke. The VRHD system consists of the patient actuated device, the control electronics and software, the practitioner graphical interface and the patient's virtual reality game software. VRHD was designed and experimentally shown to be fully Magnetic Resonance Imaging (MRI) compatible so that it can be used in brain MR imaging during handgrip rehabilitation.

Characterization of motor unit behavior in patients with amyotrophic lateral sclerosis

Abstract: In this study, we investigated the behavior of active motor units identified via analysis of electromyographic (EMG) signals recorded from the first dorsal interosseous (FDI) muscle using a quadrifilar needle electrode. Data was collected from control subjects and patients with both lower (LMN) and upper (UMN) motor neuron dominant forms of amyotrophic lateral sclerosis (ALS). EMG recordings were gathered during isometric contractions reaching 20 or 50 % of the force output produced during a maximum voluntary contraction (MVC). Recordings were analyzed using available EMG decomposition software (EMGLAB). Results showed differences in mean motor unit firing rates between patients with ALS and control subjects. Differences were also observed between patients with LMN- and UMN-dominant forms of ALS. Motor unit substitution was observed in patients despite the contractions lasting just a few seconds. Finally, we observed that motor unit action potential (MUAP) waveforms recorded from patients were more complex than those recorded from control subjects as often observed in motor neuron diseases.

Assessing the feasibility of classifying toe-walking severity in children with cerebral palsy using a sensorized shoe

Abstract: The clinical management of children with cerebral palsy (CP) relies on monitoring changes in the severity of gait abnormalities and on planning appropriate clinical interventions. Currently available technology does not make it possible to perform clinical gait evaluations as often as it would be desirable from a clinical standpoint. The use of wearable technology (e.g. a sensorized shoe) could provide an effective means to monitor changes in the severity of gait abnormalities in children with CP. In this paper, we studied a group of children with CP who showed an equinus (i.e. toe-walking) gait pattern, a gait abnormality often observed in children with CP. The aim of the study was to determine the feasibility of relying upon a sensorized shoe to assess changes in the severity of toe-walking. We demonstrated that it is possible to use features extracted from the center of pressure trajectory and ankle kinematics to predict the severity of toe-walking. Our results motivate the development and clinical testing of a sensorized shoe to assess changes in gait patterns that accompany the development, and the response to clinical interventions, of children with CP.

Motor unit firing characteristics in patients with amyotrophic lateral sclerosis

Abstract: In this study, we investigated the behavior of active motor units identified via analysis of electromyographic (EMG) signals recorded from the first dorsal interosseous (FDI) muscle using a quadrifilar needle electrode. Data was collected from control subjects and patients with both upper (UMN) and lower (LMN) motor neuron dominant forms of amyotrophic lateral sclerosis (ALS). EMG recordings were gathered during isometric contractions reaching 20 or 50 % of the force output produced during a maximum voluntary contraction (MVC). Recordings were processed using freely available EMG decomposition software (EMGLAB). Results showed differences in mean motor unit firing rates and variability between ALS patients and control subjects. Differences in mean motor unit firing rates and variability were also observed between ALS patients with LMN-and UMN-dominant forms of ALS.

A sensorized glove for hand rehabilitation

Abstract: Sensorized garments represent a breakthrough in wearable sensor technology. Sensorized garments have potential for the development of exciting new applications in rehabilitation. In this paper, we present our work toward the development of a system based on a sensorized glove that tracks hand movements during hand grasp/release tasks. The project aims at developing technology to facilitate the implementation of rehabilitation protocols focused on improving hand function. Preliminary results presented in this paper show that the sensorized glove can reliably track hand aperture during static and dynamic tasks.

Poster 191: Application of Time-Frequency Surface Electromyographic Procedures for the Classification of Impaired Muscle Function in Musculoskeletal Pain Conditions

Abstract: nication skills to be within functional limits as per the Cognitive-Linguistic QUICK Test. The patient’s speech was characterized by dysrhythmic phonations, interjections, and part-word/whole-word repetitions ranging from 3 to 16 repetitions per syllable/word within all syllable positions and within all word classes. Fluency characteristics remained consistent throughout a variety of speech tasks ranging from automatic speech, repetition, reading, singing, and pacing/ masking techniques. These dysfluencies were accompanied by secondary physical concomitants such as eye blinks, head movements, and excessive tension of the facial/laryngeal region musculature. In addition, the patient presented with severe apraxia exhibiting reduced lingual coordination and agility. She exhibited no anxiety regarding dysfluencies. Setting: Free-standing rehabilitation hospital. Results: The patient received outpatient speech therapy services for 5 months. Therapy techniques focused upon facial/body relaxation, respiratory exercises, oral motor exercises, and use of “slow smooth easy speech” fluency strategies. She is currently 100% intelligible in conversation with 0-1 dysfluencies in a 60-minute timeframe. Conclusions: This is a rare case of neurogenic stuttering following onset of migraine.

Analyzing the decision space of deep brain stimulation settings

Abstract: Deep brain stimulation of the subthalamic nucleus (STN DBS) is a surgical procedure used to treat the debilitating symptoms of advanced Parkinson's disease (PD); characterized by tremor, slowness of movement (bradykinesia) and involuntary movement (dyskinesia) The selection of optimal stimulation parameters is based upon subjective and qualitative clinical observations made across several programming sessions The overall objective of our work is to quantitatively track changes in the severity of symptoms throughout the course of these sessions using wearable kinematic sensors In this paper we present preliminary results indicating a high correlation between features derived from sensor data and clinical scores of Parkinsonian symptoms which suggests that quantitative feedback could be provided to clinicians, aiding the selection of optimal stimulation parameters