Abstract: This letter introduces MercuryLive, a platform to enable home monitoring of patients with Parkinson's disease (PD) using wearable sensors. MercuryLive contains three tiers: a resource-aware data collection engine that relies upon wearable sensors, web services for live streaming and storage of sensor data, and a web-based graphical user interface client with video conferencing capability. Besides, the platform has the capability of analyzing sensor (i.e., accelerometer) data to reliably estimate clinical scores capturing the severity of tremor, bradykinesia, and dyskinesia. Testing results showed an average data latency of less than 400 ms and video latency of about 200 ms with video frame rate of about 13 frames/s when 800 kb/s of bandwidth were available and we used a 40% video compression, and data feature upload requiring 1 min of extra time following a 10 min interactive session. These results indicate that the proposed platform is suitable to monitor patients with PD to facilitate the titration of medications in the late stages of the disease.
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.
Abstract: Objective long-term health monitoring can improve the clinical management of several medical conditions ranging from cardiopulmonary diseases to motor disorders. In this paper, we present our work toward the development of a home-monitoring system. The system is currently used to monitor patients with Parkinson's disease who experience severe motor fluctuations. Monitoring is achieved using wireless wearable sensors whose data are relayed to a remote clinical site via a web-based application. The work herein presented shows that wearable sensors combined with a web-based application provide reliable quantitative information that can be used for clinical decision making.
Abstract: Quantitative assessment of motor abilities in stroke survivors undergoing rehabilitation can be a valuable feedback to guide the rehabilitation process. The Functional Ability Scale (FAS) part of Wolf Motor Function Test (WMFT) is used to evaluate movement quality during performance of a set of functional motor tasks. In this paper, we show that information collected using body worn sensors such as accelerometers during performance of functional motor tasks by stroke survivors can be used to build accurate classifiers of FAS scores for individual tasks. We perform feature selection to improve classification accuracy and show that it is possible to estimate the total FAS score from a subset of functional motor tasks taken from the WMFT.
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.
Abstract: This paper is focused on the analysis of data obtained from wearable sensors in patients with Parkinson's Disease. We implemented Support Vector Machines (SVM's) to predict clinical scores of the severity of Parkinsonian symptoms and motor complications. We determined the optimal window length to extract features from the sensor data. Furthermore, we performed tests to determine optimal parameters for the SVM's. Finally, we analyzed how well individual tasks performed by patients captured the severity of various symptoms and motor complications.
Abstract: We present work to develop a wireless wearable sensor system for monitoring patients with Parkinson's disease (PD) in their homes. For monitoring outside the laboratory, a wearable system must not only record data, but also efficiently process data on-board. This manuscript details the analysis of data collected using tethered wearable sensors. Optimal window length for feature extraction and feature ranking were calculated, based on their ability to capture motor fluctuations in persons with PD. Results from this study will be employed to develop a software platform for the wireless system, to efficiently process on-board data.
Abstract: Functional recovery of an impaired gait pattern is a common goal for stroke patients in their rehabilitation. Robotic and mechatronic devices offer a means of facilitating and enhancing gait retraining practices undertaken by clinicians. A new active knee orthosis has been developed for gait retraining of stroke patients that may fulfil this role. Since this device is newly developed, it is important to determine its impact on the walking patterns of healthy individuals before exploring its use in gait retraining of stroke patients. The aim of this study was to analyze adaptations in gait mechanics of healthy subjects due to the added mass of the knee orthosis while worn uni-laterally and bi-laterally. In our preliminary tests we observed significant deviations from normal gait patterns when the knee orthosis was worn uni-laterally. Conversely, minor gait deviations were seen when the knee orthosis was worn bi-laterally. This suggests that a bilateral configuration may be more suited for gait retraining purposes.
