R. Hughes

Bio: R. Hughes is an academic researcher from Harvard University. The author has contributed to research in topics: Deep brain stimulation & Spectrograph. The author has an hindex of 10, co-authored 10 publications receiving 1042 citations.

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.

A Novel Approach to Monitor Rehabilitation Outcomes in Stroke Survivors Using Wearable Technology

Abstract: Quantitative assessment of motor abilities in stroke survivors can provide valuable feedback to guide clinical interventions. Numerous clinical scales were developed in the past to assess levels of impairment and functional limitation in individuals after stroke. The Functional Ability Scale is one of these clinical scales. It is a 75-point scale used to evaluate the functional ability of subjects by grading movement quality during performance of 15 motor tasks. Performance of these motor tasks requires subjects to reach for objects (e.g., a pencil on a table) and manipulate them (e.g., lift the pencil). In this paper, we show that accelerometer data recorded during performance of a subset of the motor tasks pertaining to the Functional Ability Scale can be relied upon to derive accurate estimates of the scores provided by a clinician using this scale. Accelerometer-based estimates of clinical scores were obtained by segmenting the recordings into movement components (reaching, manipulation, release/return), extracting data features, selecting features that maximized the separation among classes associated with different clinical scores, feeding these features to Random Forests to estimate scores for individual motor tasks, and using a linear equation to estimate the total Functional Ability Scale score based on the sum of the clinical scores for individual motor tasks derived from the accelerometer data. Results showed that it is possible to achieve estimates of the total Functional Ability Scale score marked by a bias of only 0.04 points of the scale and a standard deviation of only 2.43 points when using as few as three sensors to collect data during performance of only six motor tasks.

A Stark-effect microwave spectrograph of high sensitivity.

Abstract: A microwave spectrograph is described in which the absorption of the gas being studied is modulated by the application of a periodic Stark‐effect field so that the output of the crystal detector contains a radiofrequency component which can be amplified with a narrow‐band amplifier. By using a very narrow band‐width, achieved by employing phase‐sensitive detection, high sensitivity is obtained. The klystron is swept mechanically over the frequency range. Details of the components are given.

Using wearable sensors to measure motor abilities following stroke

Abstract: Motor abilities of stroke survivors are often severely affected. Post-stroke rehabilitation is guided by the use of clinical assessments of motor abilities. Clinical assessment scores can be predicted by models based on features extracted from the wearable sensor data. Wearable sensors would allow monitoring of subjects in the home and provide accurate assessments to guide the rehabilitation process. We propose the use of a wearable sensor system to assess the motor abilities of stroke victims. Preliminary results from twelve subjects show the ability of this system to predict clinical scores of motor abilities.

The Pharmacological Basis of Therapeutics

Abstract: The Pharmacological Basis of Therapeutics A Textbook of Pharmacology, Toxicology and Therapeutics for Physicians and Medical Students. By Prof. Louis Goodman and Prof. Alfred Gilman. Pp. xiii + 1383. (New York: The Macmillan Company, 1941.) 50s. net.

A review of wearable sensors and systems with application in rehabilitation.

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.

Wearable Sensors for Human Activity Monitoring: A Review

Abstract: An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded systems, wireless communication technologies, nano technologies, and miniaturization makes it possible to develop smart systems to monitor activities of human beings continuously. Wearable sensors detect abnormal and/or unforeseen situations by monitoring physiological parameters along with other symptoms. Therefore, necessary help can be provided in times of dire need. This paper reviews the latest reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed to tackle the challenges.

Sensor-Based Activity Recognition

Abstract: Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.