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Use of Accelerometry for Long Term Monitoring of Stroke Patients

Published on Jan 1, 2019in IEEE Journal of Translational Engineering in Health and Medicine
· DOI :10.1109/JTEHM.2019.2897306
Alfredo Lucas (UCSD: University of California, San Diego), John Hermiz3
Estimated H-index: 3
(UCSD: University of California, San Diego)
+ 3 AuthorsVikash Gilja21
Estimated H-index: 21
(UCSD: University of California, San Diego)
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Abstract
Stroke patients are monitored hourly by physicians and nurses in an attempt to better understand their physical state. To quantify the patients’ level of mobility, hourly movement (i.e. motor) assessment scores are performed, which can be taxing and time-consuming for nurses and physicians. In this paper, we attempt to find a correlation between patient motor scores and continuous accelerometer data recorded in subjects who are unilaterally impaired due to stroke. The accelerometers were placed on both upper and lower extremities of four severely unilaterally impaired patients and their movements were recorded continuously for 7 to 14 days. Features that incorporate movement smoothness, strength, and characteristic movement patterns were extracted from the accelerometers using time-frequency analysis. Support vector classifiers were trained with the extracted features to test the ability of the long term accelerometer recordings in predicting dependent and antigravity sides, and significantly above baseline performance was obtained in most instances ( $P ). Finally, a leave-one-subject-out approach was carried out to assess the generalizability of the proposed methodology, and above baseline performance was obtained in two out of the three tested subjects. The methodology presented in this paper provides a simple, yet effective approach to perform long term motor assessment in neurocritical care patients.
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Published on Dec 1, 2018in Journal of Neuroengineering and Rehabilitation 3.58
Yoav Beck2
Estimated H-index: 2
(Tel Aviv Sourasky Medical Center),
Talia Herman32
Estimated H-index: 32
(Tel Aviv Sourasky Medical Center)
+ 3 AuthorsJeffrey M. Hausdorff84
Estimated H-index: 84
Impairments in biomechanics and neural control can disrupt the timing and muscle pattern activation necessary for smooth gait. Gait is one of the most affected motor characteristics in Parkinson’s disease (PD), but its smoothness has not been well-studied. This work applies the recently proposed spectral arc length measure (SPARC) to study, for the first time, gait in patients with PD. We hypothesized that the gait of patients with PD would be less smooth than that of healthy controls, as reflec...
3 Citations Source Cite
Published on Dec 1, 2018in Scientific Reports 4.01
Lyndia C. Wu6
Estimated H-index: 6
(Stanford University),
Calvin J. Kuo52
Estimated H-index: 52
(Stanford University)
+ 9 AuthorsJillian E. Urban11
Estimated H-index: 11
(Wake Forest University)
Accumulation of head impacts may contribute to acute and long-term brain trauma. Wearable sensors can measure impact exposure, yet current sensors do not have validated impact detection methods for accurate exposure monitoring. Here we demonstrate a head impact detection method that can be implemented on a wearable sensor for detecting field football head impacts. Our method incorporates a support vector machine classifier that uses biomechanical features from the time domain and frequency domai...
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Published on May 25, 2018in PeerJ 2.35
Vanessa Ibáñez2
Estimated H-index: 2
(Universidad Católica de Valencia San Vicente Mártir),
Josep Silva11
Estimated H-index: 11
(Polytechnic University of Valencia),
Omar Cauli30
Estimated H-index: 30
(University of Valencia)
11 Citations Source Cite
Published on Apr 25, 2018in arXiv: Human-Computer Interaction
Anis Davoudi3
Estimated H-index: 3
,
Kumar Rohit Malhotra1
Estimated H-index: 1
+ 8 AuthorsAzra Bihorac29
Estimated H-index: 29
Currently, many critical care indices are repetitively assessed and recorded by overburdened nurses, e.g. physical function or facial pain expressions of nonverbal patients. In addition, many essential information on patients and their environment are not captured at all, or are captured in a non-granular manner, e.g. sleep disturbance factors such as bright light, loud background noise, or excessive visitations. In this pilot study, we examined the feasibility of using pervasive sensing technol...
1 Citations
Published on Nov 14, 2016 in SenSys (International Conference on Embedded Networked Sensor Systems)
Sukhoon Lee3
Estimated H-index: 3
(Ajou University),
Jaeyeon Park1
Estimated H-index: 1
(Ajou University)
+ 4 AuthorsJeongGil Ko18
Estimated H-index: 18
(Ajou University)
Analyzing large quantities of bio-signal data can lead to new findings in patient status diagnosis and medical emergency event prediction. Specifically, improvements in machine learning schemes suggest that by inputting clinical waveforms, designing mechanisms to predict medical emergencies, such as ventricular arrhythmia or sepsis, can soon be possible. However, we are still lacking the data-vaults that provide such clinically useful bio-signal data. With the goal of providing such an environme...
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Published on Dec 11, 2015in Sensors 3.03
Ferhat Attal3
Estimated H-index: 3
,
Samer Mohammed16
Estimated H-index: 16
+ 3 AuthorsYacine Amirat19
Estimated H-index: 19
This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-N...
123 Citations Source Cite
Published on Dec 1, 2015in Critical Care 6.96
Greet Hermans27
Estimated H-index: 27
(Katholieke Universiteit Leuven),
Van den Berghe Gh66
Estimated H-index: 66
(Katholieke Universiteit Leuven)
A substantial number of patients admitted to the ICU because of an acute illness, complicated surgery, severe trauma, or burn injury will develop a de novo form of muscle weakness during the ICU stay that is referred to as “intensive care unit acquired weakness” (ICUAW). This ICUAW evoked by critical illness can be due to axonal neuropathy, primary myopathy, or both. Underlying pathophysiological mechanisms comprise microvascular, electrical, metabolic, and bioenergetic alterations, interacting ...
98 Citations Source Cite
Published on Jul 1, 2015in Journal of Neurologic Physical Therapy 2.61
Anna E. Mattlage6
Estimated H-index: 6
,
Sara A. Redlin2
Estimated H-index: 2
+ 3 AuthorsSandra A. Billinger16
Estimated H-index: 16
Observational studies demonstrate low levels of physical activity during inpatient stroke rehabilitation. There are no prior studies that have objectively measured sedentary time on the acute stroke unit and whether sedentary time is related to functional outcomes. The purpose of this study was to characterize sedentary time after acute stroke and determine whether there is a relationship to functional performance at discharge.Thirty-two individuals (18 men; 56.5 ± 12.7 years) with acute stroke ...
22 Citations Source Cite
Published on Apr 7, 2015in Respiratory Care 1.74
Avelino C. Verceles10
Estimated H-index: 10
,
Erin R. Hager11
Estimated H-index: 11
(UMB: University of Maryland, Baltimore)
Medical management of critically ill patients often incorporates prolonged bed rest, which, in combination with the underlying illness, results in global muscle weakness and atrophy. Recent evidence has demonstrated improvements in clinical and functional outcomes when exercise and physical activity are incorporated early in the management of ICU patients. Accurate monitoring of ICU patients' physical activity is essential for proper prescription and escalation of activity levels. Accelerometry ...
5 Citations Source Cite
Nicolas Bayle6
Estimated H-index: 6
,
Stephen J Fried1
Estimated H-index: 1
+ 4 AuthorsJean-Michel Gracies29
Estimated H-index: 29
Objective: While considered a key symptom, bradykinesia is not specific to Parkinson's disease (PD). Measuring movement smoothness may help distinguish PD-induced from volitional bradykinesia. Methods: Eight PD patients and 12 healthy subjects performed alternating, maximal speed, small and large elbow flexion-extension movements. Six of the healthy subjects also performed the task while matching the average speed of PD patients. From angular displacement, we derived speed, acceleration, jerk me...
2 Citations Source Cite
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