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478 results on '"Skubic, Marjorie A."'

3. Cardiovascular function and ballistocardiogram: a relationship interpreted via mathematical modeling

Author

Guidoboni, Giovanna, Sala, Lorenzo, Enayati, Moein, Sacco, Riccardo, Szopos, Marcela, Keller, James, Popescu, Mihail, Despins, Laurel, Huxley, Virginia H., and Skubic, Marjorie

Subjects
Physics - Medical Physics, Physics - Biological Physics
Abstract

Objective: to develop quantitative methods for the clinical interpretation of the ballistocardiogram (BCG). Methods: a closed-loop mathematical model of the cardiovascular system is proposed to theoretically simulate the mechanisms generating the BCG signal, which is then compared with the signal acquired via accelerometry on a suspended bed. Results: simulated arterial pressure waveforms and ventricular functions are in good qualitative and quantitative agreement with those reported in the clinical literature. Simulated BCG signals exhibit the typical I, J, K, L, M and N peaks and show good qualitative and quantitative agreement with experimental measurements. Simulated BCG signals associated with reduced contractility and increased stiffness of the left ventricle exhibit different changes that are characteristic of the specific pathological condition. Conclusion: the proposed closed-loop model captures the predominant features of BCG signals and can predict pathological changes on the basis of fundamental mechanisms in cardiovascular physiology. Significance: this work provides a quantitative framework for the clinical interpretation of BCG signals and the optimization of BCG sensing devices. The present study considers an average human body and can potentially be extended to include variability among individuals.

Published
2018

4. Prototype Hat as a Biofeedback System to Address Vestibular Balance Impairment

Author

Suliman, Ahmad, Skubic, Marjorie, Kurkowski, Samantha, Abbott, Carmen, Rivera, Arnaldo, Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Jarm, Tomaz, editor, Cvetkoska, Aleksandra, editor, Mahnič-Kalamiza, Samo, editor, and Miklavcic, Damijan, editor

Published
2021
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5. Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms

Author

Jiao, Changzhe, Su, Bo-Yu, Lyons, Princess, Zare, Alina, Ho, K. C., and Skubic, Marjorie

Subjects
Statistics - Machine Learning
Abstract

A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a "heartbeat concept" that represents an individual's personal ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection and heartbeat characterization as a multiple instance learning problem to address the uncertainty inherent in aligning BCG signals with ground truth during training. Experimental results show that the estimated heartbeat concept found by DL-FUMI is an effective heartbeat prototype and achieves superior performance over comparison algorithms.

Published
2017

11. Heart Beat Characterization from Ballistocardiogram Signals using Extended Functions of Multiple Instances

Author

Jiao, Changzhe, Lyons, Princess, Zare, Alina, Rosales, Licet, and Skubic, Marjorie

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

A multiple instance learning (MIL) method, extended Function of Multiple Instances ($e$FUMI), is applied to ballistocardiogram (BCG) signals produced by a hydraulic bed sensor. The goal of this approach is to learn a personalized heartbeat "concept" for an individual. This heartbeat concept is a prototype (or "signature") that characterizes the heartbeat pattern for an individual in ballistocardiogram data. The $e$FUMI method models the problem of learning a heartbeat concept from a BCG signal as a MIL problem. This approach elegantly addresses the uncertainty inherent in a BCG signal e. g., misalignment between training data and ground truth, mis-collection of heartbeat by some transducers, etc. Given a BCG training signal coupled with a ground truth signal (e.g., a pulse finger sensor), training "bags" labeled with only binary labels denoting if a training bag contains a heartbeat signal or not can be generated. Then, using these bags, $e$FUMI learns a personalized concept of heartbeat for a subject as well as several non-heartbeat background concepts. After learning the heartbeat concept, heartbeat detection and heart rate estimation can be applied to test data. Experimental results show that the estimated heartbeat concept found by $e$FUMI is more representative and a more discriminative prototype of the heartbeat signals than those found by comparison MIL methods in the literature., Comment: IEEE EMBC 2016, pp. 1-5

Published
2016
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13. An Algorithm for Activity Recognition and Assessment of Adults Poststroke.

Author

Proffitt, Rachel, Rasmussen, Kial-Ann M., Ma, Mengxuan, and Skubic, Marjorie

Subjects
ARM, TASK performance, RESEARCH funding, FUNCTIONAL assessment, STATISTICAL sampling, HOME environment, STROKE rehabilitation, RESEARCH, RESEARCH methodology, ELECTRONIC health records, MACHINE learning, BODY movement, ALGORITHMS, ACTIVITIES of daily living, MOTION capture (Human mechanics)
Abstract

Importance: Stroke is the leading cause of long-term disability in the United States. Providers have no robust tools to objectively and accurately measure the activity of people with stroke living at home. Objective: To explore the integration of validated upper extremity assessments poststroke within an activity recognition system. Design: Exploratory descriptive study using data previously collected over 3 mo to report on algorithm testing and assessment integration. Setting: Data were collected in the homes of community-dwelling participants. Participants: Participants were at least 6 mo poststroke, were able to ambulate with or without an assistive device, and self-reported some difficulty using their arm in everyday activities. Outcomes and Measures: The activity detection algorithm's accuracy was determined by comparing its activity labels with manual labels. The algorithm integrated assessment by describing the quality of upper extremity movement, which was determined by reporting extent of reach, mean and maximum speed during movement, and smoothness of movement. Results: Sixteen participants (9 women, 7 men) took part in this study, with an average age of 63.38 yr (SD = 12.84). The algorithm was highly accurate in correctly identifying activities, with 87% to 95% accuracy depending on the movement. The algorithm was also able to detect the quality of movement for upper extremity movements. Conclusions and Relevance: The algorithm was able to accurately identify in-kitchen activities performed by adults poststroke. Information about the quality of these movements was also successfully calculated. This algorithm has the potential to supplement clinical assessments in treatment planning and outcomes reporting. Plain-Language Summary: This study shows that clinical algorithms have the potential to inform occupational therapy practice by providing clinically relevant data about the in-home activities of adults poststroke. The algorithm accurately identified activities that were performed in the kitchen by adults poststroke. The algorithm also identified the quality of upper extremity movements of people poststroke who were living at home. This study shows that clinical algorithms have the potential to inform occupational therapy practice by providing clinically relevant data about the in-home activities of adults poststroke. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Non-invasive Classification of Sleep Stages with a Hydraulic Bed Sensor Using Deep Learning

Author

Gargees, Rayan, Keller, James M., Popescu, Mihail, Skubic, Marjorie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pagán, José, editor, Mokhtari, Mounir, editor, Aloulou, Hamdi, editor, Abdulrazak, Bessam, editor, and Cabrera, María Fernanda, editor

Published
2019
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15. Development and Comparison of Customized Voice-Assistant Systems for Independent Living Older Adults

Author

Shalini, Shradha, Levins, Trevor, Robinson, Erin L., Lane, Kari, Park, Geunhye, Skubic, Marjorie, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Zhou, Jia, editor, and Salvendy, Gavriel, editor

Published
2019
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22. TigerPlace

Author

Skubic, Marjorie, Rantz, Marilyn J., van Hoof, Joost, editor, Demiris, George, editor, and Wouters, Eveline J.M., editor

Published
2017
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27. HCI Challenges for Consumer-Based Aging in Place Technologies

Author

Skubic, Marjorie, Mishra, Anup, Harris, Bradford, Abbott, Carmen, Craver, Andrew, Musterman, Katy, Rantz, Marilyn, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Zhou, Jia, editor, and Salvendy, Gavriel, editor

Published
2016
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29. Design and Usability of a Smart Home Sensor Data User Interface for a Clinical and Research Audience

Author

Sheahen, Mary, Skubic, Marjorie, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bodine, Cathy, editor, Helal, Sumi, editor, Gu, Tao, editor, and Mokhtari, Mounir, editor

Published
2015
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30. Testing Real-Time In-Home Fall Alerts with Embedded Depth Video Hyperlink

Author

Stone, Erik E., Skubic, Marjorie, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bodine, Cathy, editor, Helal, Sumi, editor, Gu, Tao, editor, and Mokhtari, Mounir, editor

Published
2015
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36. Histogram of Oriented Normal Vectors for Object Recognition with a Depth Sensor

Author

Tang, Shuai, Wang, Xiaoyu, Lv, Xutao, Han, Tony X., Keller, James, He, Zhihai, Skubic, Marjorie, Lao, Shihong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Lee, Kyoung Mu, editor, Matsushita, Yasuyuki, editor, Rehg, James M., editor, and Hu, Zhanyi, editor

Published
2013
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37. Testing Classifiers for Embedded Health Assessment

Author

Skubic, Marjorie, Guevara, Rainer Dane, Rantz, Marilyn, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Donnelly, Mark, editor, Paggetti, Cristiano, editor, Nugent, Chris, editor, and Mokhtari, Mounir, editor

Published
2012
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41. Functional Assessment Technologies

Author

Rantz, Marilyn, Skubic, Marjorie, Burks, Kathryn, Yu, Jie, Demiris, George, Hensel, Brian K., Alexander, Gregory L., He, Zhihai, Tyrer, Harry W., Hamilton, Marc, Lee, Jia, Brown, Marybeth, Pignolo, Robert J., editor, Forciea, Mary Ann, editor, Johnson, Jerry C., editor, Alwan, Majd, editor, and Felder, Robin A., editor

Published
2008
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42. Using Technology to Enhance Aging in Place

Author

Rantz, Marilyn, Skubic, Marjorie, Miller, Steven, Krampe, Jean, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Helal, Sumi, editor, Mitra, Simanta, editor, Wong, Johnny, editor, Chang, Carl K., editor, and Mokhtari, Mounir, editor

Published
2008
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43. Continuous in-home symptom and mobility measures for individuals with multiple sclerosis: a case presentation

Author

Newland, Pamela, Kimutis, Alyssa, Salter, Amber, Flick, Louise, Thomas, Florian P., Rantz, Marilyn, and Skubic, Marjorie

Subjects
Multiple sclerosis -- Case studies -- Diagnosis -- Complications and side effects, Gait disorders -- Case studies -- Causes of -- Diagnosis, Health care industry
Abstract

ABSTRACT Gait impairment represents one of the most common and disabling symptoms of multiple sclerosis (MS). To identify which temporal or spatial parameters of gait could be used as outcome [...]

Published
2017
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46. Novel clinically-relevant assessment of upper extremity movement using depth sensors.

Author

Proffitt, Rachel, Ma, Mengxuan, and Skubic, Marjorie

Subjects
HAND physiology, PILOT projects, RESEARCH, STATISTICS, STROKE, ANALYSIS of variance, VIRTUAL reality, PHYSICAL therapy, HOME care services, CONVALESCENCE, QUANTITATIVE research, ARM, COMPARATIVE studies, BODY movement, STROKE rehabilitation, STROKE patients, RESEARCH funding, MOTION capture (Human mechanics), VIDEO games, DATA analysis, KINEMATICS, ALGORITHMS, EXERCISE therapy, DISEASE complications
Abstract

For individuals post-stroke, home-based programs are necessary to deliver additional hours of therapy outside of the limited time in the clinic. Virtual reality (VR)-based approaches show modest outcomes in improving client function when delivered in the home. The movement sensors used in these VR-based approaches, such as the Microsoft Kinect® have been validated against gold standards tools but have not been used as an assessment of upper extremity movement quality in the stroke population. The purpose of this study was to explore the use of a movement sensor paired with a VR-based intervention to assess upper extremity movement for individuals post-stroke. Movement data captured with the Microsoft Kinect® from four separate studies were aggregated for analysis (n = 8 individuals post-stroke, n = 30 individuals without disabilities). For all participants, the skeletal data (x, y, z coordinates for 15 tracked joints) for each game play session were processed in MatLab and movement variables (normalized jerk, movement path ratio, average path sway) were calculated using an OPTICS density-based cluster algorithm. Data from the 30 healthy individuals created a normative baseline for the three kinematic variables. Individuals post-stroke were less efficient and had more jerky movements in both upper extremities as compared to healthy individuals. It is feasible to use a movement sensor paired with a VR-based intervention to quantify and qualify upper extremity movement for individuals post-stroke. Further research with a larger cohort is necessary to establish clinical sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Published
2023
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47. Mechanism-Driven Modeling to Aid Non-invasive Monitoring of Cardiac Function via Ballistocardiography

Author

Zaid, Mohamed, primary, Sala, Lorenzo, additional, Ivey, Jan R., additional, Tharp, Darla L., additional, Mueller, Christina M., additional, Thorne, Pamela K., additional, Kelly, Shannon C., additional, Silva, Kleiton Augusto Santos, additional, Amin, Amira R., additional, Ruiz-Lozano, Pilar, additional, Kapiloff, Michael S., additional, Despins, Laurel, additional, Popescu, Mihail, additional, Keller, James, additional, Skubic, Marjorie, additional, Ahmad, Salman, additional, Emter, Craig A., additional, and Guidoboni, Giovanna, additional

Published
2022
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