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Roy S.H.,Boston University | Cole B.T.,Boston University | Gilmore L.D.,Boston University | De Luca C.J.,Boston University | And 4 more authors.
Movement Disorders | Year: 2013

Parkinson's disease (PD) can present with a variety of motor disorders that fluctuate throughout the day, making assessment a challenging task. Paper-based measurement tools can be burdensome to the patient and clinician and lack the temporal resolution needed to accurately and objectively track changes in motor symptom severity throughout the day. Wearable sensor-based systems that continuously monitor PD motor disorders may help to solve this problem, although critical shortcomings persist in identifying multiple disorders at high temporal resolution during unconstrained activity. The purpose of this study was to advance the current state of the art by (1) introducing hybrid sensor technology to concurrently acquire surface electromyographic (sEMG) and accelerometer data during unconstrained activity and (2) analyzing the data using dynamic neural network algorithms to capture the evolving temporal characteristics of the sensor data and improve motor disorder recognition of tremor and dyskinesia. Algorithms were trained (n=11 patients) and tested (n=8 patients; n=4 controls) to recognize tremor and dyskinesia at 1-second resolution based on sensor data features and expert annotation of video recording during 4-hour monitoring periods of unconstrained daily activity. The algorithms were able to make accurate distinctions between tremor, dyskinesia, and normal movement despite the presence of diverse voluntary activity. Motor disorder severity classifications averaged 94.9% sensitivity and 97.1% specificity based on 1 sensor per symptomatic limb. These initial findings indicate that new sensor technology and software algorithms can be effective in enhancing wearable sensor-based system performance for monitoring PD motor disorders during unconstrained activities. © 2013 Movement Disorder Society. Source


De Luca C.J.,Delsys Inc. | De Luca C.J.,Boston University | Kuznetsov M.,Boston University | Gilmore L.D.,Boston University | Roy S.H.,Boston University
Journal of Biomechanics | Year: 2012

We investigated the influence of inter-electrode spacing on the degree of crosstalk contamination in surface electromyographic (sEMG) signals in the tibialis anterior (target muscle), generated by the triceps surae (crosstalk muscle), using bar and disk electrode arrays. The degree of crosstalk contamination was assessed for voluntary constant-force isometric contractions and for dynamic contractions during walking. Single-differential signals were acquired with inter-electrode spacing ranging from 5. mm to 40. mm. Additionally, double differential signals were acquired at 10. mm spacing using the bar electrode array. Crosstalk contamination at the target muscle was expressed as the ratio of the detected crosstalk signal to that of the target muscle signal. The crosstalk contamination ratio approached a mean of 50% for the 40. mm spacing for triceps surae muscle contractions at 80% MVC and tibialis anterior muscle contractions at 10% MVC. For single differential recordings, the minimum crosstalk contamination was obtained from the 10. mm spacing. The results showed no significant differences between the bar and disk electrode arrays. During walking, the crosstalk contamination on the tibialis anterior muscle reached levels of 23% for a commonly used 22. mm spacing single-differential disk sensor, 17% for a 10. mm spacing single-differential bar sensor, and 8% for a 10. mm double-differential bar sensor. For both studies the effect of electrode spacing on crosstalk contamination was statistically significant. Crosstalk contamination and inter-electrode spacing should therefore be a serious concern in gait studies when the sEMG signal is collected with single differential sensors. The contamination can distort the target muscle signal and mislead the interpretation of its activation timing and force magnitude. © 2011 Elsevier Ltd. Source


De Luca C.J.,Delsys Inc. | De Luca C.J.,Boston University | Donald Gilmore L.,Boston University | Kuznetsov M.,Boston University | Roy S.H.,Boston University
Journal of Biomechanics | Year: 2010

The surface electromyographic (sEMG) signal that originates in the muscle is inevitably contaminated by various noise signals or artifacts that originate at the skin-electrode interface, in the electronics that amplifies the signals, and in external sources. Modern technology is substantially immune to some of these noises, but not to the baseline noise and the movement artifact noise. These noise sources have frequency spectra that contaminate the low-frequency part of the sEMG frequency spectrum. There are many factors which must be taken into consideration when determining the appropriate filter specifications to remove these artifacts; they include the muscle tested and type of contraction, the sensor configuration, and specific noise source. The band-pass determination is always a compromise between (a) reducing noise and artifact contamination, and (b) preserving the desired information from the sEMG signal. This study was designed to investigate the effects of mechanical perturbations and noise that are typically encountered during sEMG recordings in clinical and related applications. The analysis established the relationship between the attenuation rates of the movement artifact and the sEMG signal as a function of the filter band pass. When this relationship is combined with other considerations related to the informational content of the signal, the signal distortion of filters, and the kinds of artifacts evaluated in this study, a Butterworth filter with a corner frequency of 20. Hz and a slope of 12. dB/oct is recommended for general use. The results of this study are relevant to biomechanical and clinical applications where the measurements of body dynamics and kinematics may include artifact sources. © 2010 Elsevier Ltd. Source


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 597.80K | Year: 2004

The proposed Phase II outlines a plan to develop a completely wireless, ?smart? surface electromyographic (EMG) system. The project is directed at NASA?s request for innovative in-flight and ground-based technologies to improve current methods of monitoring vital organ systems that suffer deleterious effects in micro gravity. The system will provide reliable, accurate, and noninvasive monitoring of the musculoskeletal system without encumbering the user. The innovation builds on recent technological developments of our group in achieving a wireless EMG sensor prototype. The solution we propose is a significant departure from current state of the art systems. Phase I successfully demonstrated the feasibility of such an EMG monitoring system. The proposed work plan for Phase II is directed at: 1) miniaturization and enhancements of the sensors and base station, 2) incorporation of recent R&D to improve the sensor/skin interface, 4) extensions of the sensors? smart features, and 5) expansion of the multi-channel capabilities. The deliverable of an 8-channel wireless EMG system with smart capabilities will be evaluated in human subjects for signal fidelity and usability. A plan for the commercialization of the system in Phase III is outlined to include prospective users in the fields rehabilitation, ergonomics, and military in addition to NASA.


Trademark
Delsys Inc. | Date: 2014-05-23

Computer software for use in operating electromyographic systems. Electromyographic signal monitors, recorders, sensors and interpreters.

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