Papers by Marjorie Skubic
Innovation in Aging, Nov 1, 2019
Ahstract-Falls are a significant cause of injury and accidental death among persons over the age ... more Ahstract-Falls are a significant cause of injury and accidental death among persons over the age of 65. Gait velocity is one of the parameters which have been correlated to the risk of falling. We aim to build a system which monitors gait in seniors and reports any changes to caregivers, who can then perform a clinical assessment and perform corrective and preventative actions to reduce the likelihood of falls. In this paper, we deploy a Doppler radar-based gait measurement system into the apartments of thirteen seniors. In scripted walks, we show the system measures gait velocity with a mean error of 14.5% compared to the time recorded by a clinician. With a calibration factor, the mean error is reduced to 10.5%. The radar is a promising sensing technology for gait velocity in a day-today senior living environment.
ACM transactions on computing for healthcare, Jul 15, 2021
The rapid aging of the population worldwide requires increased attention from healthcare provider... more The rapid aging of the population worldwide requires increased attention from healthcare providers and the entire society. For the elderly to live independently, many health issues related to old age, such as frailty and risk of falling, need increased attention and monitoring. When monitoring daily routines for older adults, it is desirable to detect the early signs of health changes before serious health events, such as hospitalizations, happen so that timely and adequate preventive care may be provided. By deploying multi-sensor systems in homes of the elderly, we can track trajectories of daily behaviors in a feature space defined using the sensor data. In this article, we investigate a methodology for tracking the evolution of the behavior trajectories over long periods (years) using high-dimensional streaming clustering and provide very early indicators of changes in health. If we assume that habitual behaviors correspond to clusters in feature space and diseases produce a change in behavior, albeit not highly specific, tracking trajectory deviations can provide hints of early illness. Retrospectively, we visualize the streaming clustering results and track how the behavior clusters evolve in feature space with the help of two dimension-reduction algorithms: Principal Component Analysis and t-distributed Stochastic Neighbor Embedding. Moreover, our tracking algorithm in the original high-dimensional feature space generates early health warning alerts if a negative trend is detected in the behavior trajectory. We validated our algorithm on synthetic data and tested it on a pilot dataset of four TigerPlace residents monitored with a collection of motion, bed, and depth sensors over 10 years. We used the TigerPlace electronic health records to understand the residents’ behavior patterns and to evaluate the health warnings generated by our algorithm. The results obtained on the TigerPlace dataset show that most of the warnings produced by our algorithm can be linked to health events documented in the electronic health records, providing strong support for a prospective deployment of the approach.
Frontiers in Physiology
Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular ... more Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-pr...
Journal of Ambient Intelligence and Smart Environments, 2011
An investigation of a new, inexpensive depth camera device, the Microsoft Kinect, for passive gai... more An investigation of a new, inexpensive depth camera device, the Microsoft Kinect, for passive gait assessment in home environments is presented. In order to allow older adults to safely continue living in independent settings as they age, the ability to assess their risk of falling, along with detecting the early onset of illness and functional decline, is essential. Daily measurements of temporal and spatial gait parameters would greatly facilitate such an assessment. Ideally, these measurements would be obtained passively, in normal daily activity, without the need for wearable devices or expensive equipment. In this work, the use of the inexpensive Microsoft Kinect for obtaining measurements of temporal and spatial gait parameters is evaluated against an existing web-camera based system, along with a Vicon marker-based motion capture system for ground truth. Techniques for extracting gait parameters from the Kinect data are described, as well as the potential advantages of the Kinect over the web-camera system for passive, in-home gait assessment.
Sports health, 2017
Noncontact anterior cruciate ligament (ACL) injury in adolescent female athletes is an increasing... more Noncontact anterior cruciate ligament (ACL) injury in adolescent female athletes is an increasing problem. The knee-ankle separation ratio (KASR), calculated at initial contact (IC) and peak flexion (PF) during the drop vertical jump (DVJ), is a measure of dynamic knee valgus. The Microsoft Kinect V2 has shown promise as a reliable and valid marker-less motion capture device. The Kinect V2 will demonstrate good to excellent correlation between KASR results at IC and PF during the DVJ, as compared with a "gold standard" Vicon motion analysis system. Descriptive laboratory study. Level 2. Thirty-eight healthy volunteer subjects (20 male, 18 female) performed 5 DVJ trials, simultaneously measured by a Vicon MX-T40S system, 2 AMTI force platforms, and a Kinect V2 with customized software. A total of 190 jumps were completed. The KASR was calculated at IC and PF during the DVJ. The intraclass correlation coefficient (ICC) assessed the degree of KASR agreement between the Kinect...
Journal of the American Medical Directors Association, Jan 12, 2017
Measure the clinical effectiveness and cost effectiveness of using sensor data from an environmen... more Measure the clinical effectiveness and cost effectiveness of using sensor data from an environmentally embedded sensor system for early illness recognition. This sensor system has demonstrated in pilot studies to detect changes in function and in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness. Prospective intervention study in 13 assisted living (AL) communities of 171 residents randomly assigned to intervention (n=86) or comparison group (n=85) receiving usual care. Intervention participants lived with the sensor system an average of one year. Continuous data collected 24 hours/7 days a week from motion sensors to measure overall activity, an under mattress bed sensor to capture respiration, pulse, and restlessness as people sleep, and a gait sensor that continuously measures gait speed, stride length and time, and automatically assess for increasing fall risk as the person walks around the apartment. Con...
Computers, informatics, nursing : CIN, Jan 9, 2017
Aging in place is a preferred and cost-effective living option for older adults. Research indicat... more Aging in place is a preferred and cost-effective living option for older adults. Research indicates that technology can assist with this goal. Information on consumer preferences will help in technology development to assist older adults to age in place. The study aim was to explore the perceptions and preferences of older adults and their family members about a fall risk assessment system. Using a qualitative approach, this study examined the perceptions, attitudes, and preferences of 13 older adults and five family members about their experience living with the fall risk assessment system during five points in time. Themes emerged in relation to preferences and expectations about the technology and how it fits into daily routines. We were able to capture changes that occurred over time for older adult participants. Results indicated that there was acceptance of the technology as participants adapted to it. Two themes were present across the five points in time-safety and usefulnes...
Western journal of nursing research, Jan 27, 2016
This study explored using big data, totaling 66 terabytes over 10 years, captured from sensor sys... more This study explored using big data, totaling 66 terabytes over 10 years, captured from sensor systems installed in independent living apartments to predict falls from pre-fall changes in residents' Kinect-recorded gait parameters. Over a period of 3 to 48 months, we analyzed gait parameters continuously collected for residents who actually fell (n = 13) and those who did not fall (n = 10). We analyzed associations between participants' fall events (n = 69) and pre-fall changes in in-home gait speed and stride length (n = 2,070). Preliminary results indicate that a cumulative change in speed over time is associated with the probability of a fall (p < .0001). The odds of a resident falling within 3 weeks after a cumulative change of 2.54 cm/s is 4.22 times the odds of a resident falling within 3 weeks after no change in in-home gait speed. Results demonstrate using sensors to measure in-home gait parameters associated with the occurrence of future falls.
Journal of Ambient Intelligence and Smart Environments, 2016
One in three elders over the age of 65 falls each year in the United States. This paper describes... more One in three elders over the age of 65 falls each year in the United States. This paper describes a non-invasive fall detection system based on a Doppler radar sensor. The developed system has been tested in two environments: laboratory and real senior living apartments. While some laboratory results appeared in our previous papers, the main novelty of this paper consists in the deployment of our fall detection system in six apartments from TigerPlace (a senior living facility in Columbia, Missouri). The fall detection results obtained in our laboratory were excellent, with the radar placed on the ceiling performing better than on the floor. The fall detection system was then evaluated using radar data collected over two weeks in six TigerPlace apartments. The fall detection system successfully detected all six natural senior falls in an apartment for the examined one week.
Aims: Researchers at the University of Missouri are developing an early illness sensor system tha... more Aims: Researchers at the University of Missouri are developing an early illness sensor system that uses sensor data to detect early signs of illness or functional decline in older adults. Integrated sensor networks have been installed in the apartments of residents at TigerPlace, a retirement community helping residents age in place. The sensor networks include a bed sensor which detects restlessness, pulse and respiration; motion sensors monitor activity of residents in their apartments. A web based interface was developed to graphically display the sensor data for researchers, clinicians, and residents. The research focuses on using the sensor data prospectively and developing alerts to help clinicians detect health status changes. Methods: Qualitative and quantitative methods are being used by the clinicians to identify changes in health status. Engineering researchers are programming alerts based on the retrospectively and prospectively identified patterns of health status chang...
Aims: Researchers at the University of Missouri are investigating the use of passive monitoring t... more Aims: Researchers at the University of Missouri are investigating the use of passive monitoring to enhance registered nurse care coordination of older adults. Integrated sensor networks have been installed in the apartments of residents at TigerPlace, a retirement community helping residents age in place. The sensor networks include a bed sensor which detects presence in bed as well as restlessness, pulse and respiration. Motion sensors also monitor the activity of the resident in the apartment. A web based interface was developed to visually display the sensor data for researchers, clinicians, and residents. Methods: Using a case study methodology, the sensor data was retrospectively compared to adverse health events such as falls, emergency room visits, and hospitalizations in the web based interface to locate patterns in the data which could have been used to notify caregivers of changes in resident status. All adverse events from September 27, 2005 to April 30, 2008 were examine...
Nursing Outlook, 2015
N u r s O u t l o o k 6 3 (2 0 1 5) 6 5 0 e 6 5 5 www.nursingoutlook.org embedded sensor data, ma... more N u r s O u t l o o k 6 3 (2 0 1 5) 6 5 0 e 6 5 5 www.nursingoutlook.org embedded sensor data, may enable care coordinators to assess and intervene on health status changes earlier than is possible in the absence of sensor-generated alerts. Comparison of LOS without sensors TigerPlace (2.6 years) with the national median in residential senior housing (1.8 years) may be attributable to the RN care coordination model at TigerPlace. Cost estimates comparing cost of living at TigerPlace with the sensor technology vs. nursing home reveal potential saving of about $30,000 per person. Potential cost savings to Medicaid funded nursing home (assuming the technology and care coordination were reimbursed) are estimated to be about $87,000 per person. Conclusions: Early alerts for potential health problems appear to enhance the current RN care coordination care delivery model at TigerPlace, increasing LOS for those living with sensors to nearly twice that of those who did not. Sensor technology with care coordination has cost saving potential for consumers and Medicaid.
IEEE Journal of Translational Engineering in Health and Medicine, 2015
We present an example of unobtrusive, continuous monitoring in the home for the purpose of assess... more We present an example of unobtrusive, continuous monitoring in the home for the purpose of assessing early health changes. Sensors embedded in the environment capture behavior and activity patterns. Changes in patterns are detected as potential signs of changing health. We first present results of a preliminary study investigating 22 features extracted from inhome sensor data. A one-dimensional alert algorithm was then implemented to generate health alerts to clinicians in a senior housing facility. Clinicians analyze each alert and provide a rating on the clinical relevance. These ratings are then used as ground truth for training and testing classifiers. Here, we present the methodology for four classification approaches that fuse multisensor data. Results are shown using embedded sensor data and health alert ratings collected on 21 seniors over nine months. The best results show similar performance for two techniques, where one approach uses only domain knowledge and the second uses supervised learning for training. Finally, we propose a health change detection model based on these results and clinical expertise. The system of in-home sensors and algorithms for automated health alerts provides a method for detecting health problems very early so that early treatment is possible. This method of passive in-home sensing alleviates compliance issues. Index Terms-behavioral bio-markers, eldercare monitoring, health alerts, in-home sensing I. INTRODUCTION O UR view of embedded health assessment is the ongoing assessment of health changes based on an individual's behavior and activity patterns and baseline health conditions. Sensors embedded in the environment are used to collect behavior and activity patterns for the purpose of detecting health changes. Early detection is the key to promoting health, independence, and function as people age [1] [2]. Identifying and assessing problems early, while they are still small, provides a window of opportunity for interventions to alleviate problems before they become catastrophic. Older adults will benefit from early detection and recognition of small changes in health conditions and get help early when treatment is the most effective. Most importantly, function can be restored so they can continue living independently. Recently, there has been an increased focus on technology for enabling independent living and healthy aging. A major challenge for studies in this area is the capture of ground truth
Smart Homes and Health Telematics, 2014
A method for sending real-time fall alerts containing an embedded hyperlink to a depth video clip... more A method for sending real-time fall alerts containing an embedded hyperlink to a depth video clip of the suspected fall was evaluated in senior housing. A previously reported fall detection method using the Microsoft Kinect was used to detect naturally occurring falls in the main living area of each apartment. In this paper, evaluation results are included for 12 apartments over a 101 day period in which 34 naturally occurring falls were detected. Based on computed fall confidences, real-time alerts were sent via email to facility staff. The alerts contained an embedded hyperlink to a short depth video clip of the suspected fall. Recipients were able to click on the hyperlink to view the clip on any device supporting play back of MPEG-4 video, such as smart phones, to immediately determine if the alert was for an actual fall or a false alarm. Benefits and limitations of the technology are discussed.
2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011
We present a method for improving human segmentation results in calibrated, multi-view environmen... more We present a method for improving human segmentation results in calibrated, multi-view environments using features derived from both pixel (image) and voxel (volume) space. The main focus of this work is to develop a lowcost, vision-based system for passive activity monitoring of older adults in the home, to capture early signs of illness and functional decline and allow seniors to live independently. Silhouettes are extracted to address privacy concerns. Specific embedded assessment goals include daily gait, fall risk, and overall activity, as well as fall detection. To achieve these goals, accurate, robust segmentation of human subjects (silhouette extraction) from captured video data is required. We present a simple technique that makes use of features acquired from background subtraction results (silhouettes) of multiple calibrated cameras, along with the 3D voxel object formed from the intersection of those multiple silhouettes in a volume space to improve human segmentation results in dynamic environments; moving objects, non-human objects, and lighting changes often complicate this task. The technique is qualitatively evaluated on three data sequences, two of which were captured in an independent living facility for older adults.
Computer Vision and Image Understanding, 2015
We describe a novel technique to combine motion data with scene information to capture activity c... more We describe a novel technique to combine motion data with scene information to capture activity characteristics of older adults using a single Microsoft Kinect depth sensor. Specifically, we describe a method to learn activities of daily living (ADLs) and instrumental ADLs (IADLs) in order to study the behavior patterns of older adults to detect health changes. To learn the ADLs, we incorporate scene information to provide contextual information to build our activity model. The strength of our algorithm lies in its generalizability to model different ADLs while adding more information to the model as we instantiate ADLs from learned activity states. We validate our results in a controlled environment and compare it with another widely accepted classifier, the hidden Markov model (HMM) and its variations. We also test our system on depth data collected in a dynamic unstructured environment at TigerPlace, an independent living facility for older adults. An in-home activity monitoring system would benefit from our algorithm to alert healthcare providers of significant temporal changes in ADL behavior patterns of frail older adults for fall risk, cognitive impairment, and other health changes.
IEEE journal of biomedical and health informatics, 2015
A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-st... more A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near ...
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Papers by Marjorie Skubic