Publications by authors named "John Lach"

22 Publications

  • Page 1 of 1

Wearable Respiration Monitoring: Interpretable Inference with Context and Sensor Biomarkers.

IEEE J Biomed Health Inform 2020 Nov 4;PP. Epub 2020 Nov 4.

Continuous monitoring of breathing rate (BR), minute ventilation (VE), and other respiratory parameters could transform care for and empower patients with chronic cardio-pulmonary conditions, such as asthma. However, the clinical standard for measuring respiration, namely Spirometry, is hardly suitable for continuous use. Wearables can track many physiological signals, like ECG and motion, yet respiration tracking faces many challenges. In this work, we infer respiratory parameters from wearable ECG and wrist motion signals. We propose a modular and generalizable classification-regression pipeline to utilize available context information, such as physical activity, in learning context-conditioned inference models. Novel morphological and power domain features from the wearable ECG are extracted to use with these models. Exploratory feature selection methods are incorporated in this pipeline to discover application-driven interpretable biomarkers. Using data from 15 subjects, we evaluate two implementations of the proposed inference pipeline: for BR and VE. Each implementation compares generalized linear model, random forest, support vector machine, Gaussian process regression, and neighborhood component analysis as regression models. Permutation, regularization, and relevance determination methods are used to rank the ECG features to identify robust ECG biomarkers across models and activities. This work demonstrates the potential of wearable sensors not only in continuous monitoring, but also in designing biomarker-driven preventive measures.
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http://dx.doi.org/10.1109/JBHI.2020.3035776DOI Listing
November 2020

Understanding the Experience of Cancer Pain From the Perspective of Patients and Family Caregivers to Inform Design of an In-Home Smart Health System: Multimethod Approach.

JMIR Form Res 2020 Aug 26;4(8):e20836. Epub 2020 Aug 26.

The George Washington University School of Engineering & Applied Science, Washington, DC, United States.

Background: Inadequately managed pain is a serious problem for patients with cancer and those who care for them. Smart health systems can help with remote symptom monitoring and management, but they must be designed with meaningful end-user input.

Objective: This study aims to understand the experience of managing cancer pain at home from the perspective of both patients and family caregivers to inform design of the Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C) smart health system.

Methods: This was a descriptive pilot study using a multimethod approach. Dyads of patients with cancer and difficult pain and their primary family caregivers were recruited from an outpatient oncology clinic. The participant interviews consisted of (1) open-ended questions to explore the overall experience of cancer pain at home, (2) ranking of variables on a Likert-type scale (0, no impact; 5, most impact) that may influence cancer pain at home, and (3) feedback regarding BESI-C system prototypes. Qualitative data were analyzed using a descriptive approach to identity patterns and key themes. Quantitative data were analyzed using SPSS; basic descriptive statistics and independent sample t tests were run.

Results: Our sample (n=22; 10 patient-caregiver dyads and 2 patients) uniformly described the experience of managing cancer pain at home as stressful and difficult. Key themes included (1) unpredictability of pain episodes; (2) impact of pain on daily life, especially the negative impact on sleep, activity, and social interactions; and (3) concerns regarding medications. Overall, taking pain medication was rated as the category with the highest impact on a patient's pain (=4.79), followed by the categories of wellness (=3.60; sleep quality and quantity, physical activity, mood and oral intake) and interaction (=2.69; busyness of home, social or interpersonal interactions, physical closeness or proximity to others, and emotional closeness and connection to others). The category related to environmental factors (temperature, humidity, noise, and light) was rated with the lowest overall impact (=2.51). Patients and family caregivers expressed receptivity to the concept of BESI-C and reported a preference for using a wearable sensor (smart watch) to capture data related to the abrupt onset of difficult cancer pain.

Conclusions: Smart health systems to support cancer pain management should (1) account for the experience of both the patient and the caregiver, (2) prioritize passive monitoring of physiological and environmental variables to reduce burden, and (3) include functionality that can monitor and track medication intake and efficacy; wellness variables, such as sleep quality and quantity, physical activity, mood, and oral intake; and levels of social interaction and engagement. Systems must consider privacy and data sharing concerns and incorporate feasible strategies to capture and characterize rapid-onset symptoms.
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http://dx.doi.org/10.2196/20836DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7481872PMC
August 2020

Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review.

NPJ Digit Med 2020 13;3:38. Epub 2020 Mar 13.

1Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089 USA.

Dietary intake, eating behaviors, and context are important in chronic disease development, yet our ability to accurately assess these in research settings can be limited by biased traditional self-reporting tools. Objective measurement tools, specifically, wearable sensors, present the opportunity to minimize the major limitations of self-reported eating measures by generating supplementary sensor data that can improve the validity of self-report data in naturalistic settings. This scoping review summarizes the current use of wearable devices/sensors that automatically detect eating-related activity in naturalistic research settings. Five databases were searched in December 2019, and 618 records were retrieved from the literature search. This scoping review included  = 40 studies (from 33 articles) that reported on one or more wearable sensors used to automatically detect eating activity in the field. The majority of studies ( = 26, 65%) used multi-sensor systems (incorporating > 1 wearable sensors), and accelerometers were the most commonly utilized sensor ( = 25, 62.5%). All studies ( = 40, 100.0%) used either self-report or objective ground-truth methods to validate the inferred eating activity detected by the sensor(s). The most frequently reported evaluation metrics were Accuracy ( = 12) and F1-score ( = 10). This scoping review highlights the current state of wearable sensors' ability to improve upon traditional eating assessment methods by passively detecting eating activity in naturalistic settings, over long periods of time, and with minimal user interaction. A key challenge in this field, wide variation in eating outcome measures and evaluation metrics, demonstrates the need for the development of a standardized form of comparability among sensors/multi-sensor systems and multidisciplinary collaboration.
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http://dx.doi.org/10.1038/s41746-020-0246-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7069988PMC
March 2020

BESI: Behavioral and Environmental Sensing and Intervention for Dementia Caregiver Empowerment-Phases 1 and 2.

Am J Alzheimers Dis Other Demen 2020 Jan-Dec;35:1533317520906686

Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA.

Background And Objectives: Caregiver burden associated with dementia-related agitation is one of the commonest reasons a community-dwelling person with dementia (PWD) transitions to a care facility. Behavioral and Environmental Sensing and Intervention for Dementia Caregiver Empowerment (BESI) is a system of body-worn and in-home sensors developed to provide continuous, noninvasive agitation assessment and environmental context monitoring to detect early signs of agitation and its environmental triggers.

Research Design And Methods: This mixed methods, remote ethnographic study is explored in a 3-phase, multiyear plan. In Phase 1, we developed and refined the BESI system and completed usability studies. Validation of the system and the development of dyad-specific models of the relationship between agitation and the environment occurred in Phase 2.

Results: Phases 1 and 2 results facilitated targeted changes in BESI, thus improving its overall usability for the final phase of the study, when real-time notifications and interventions will be implemented.

Conclusion: Our results show a valid relationship between the presence of dementia related agitation and environmental factors and that persons with dementia and their caregivers prefer a home-based monitoring system like BESI.
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http://dx.doi.org/10.1177/1533317520906686DOI Listing
December 2020

Inferring Respiratory Minute Volume from Wrist Motion.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:6935-6938

Exposure to air pollutants poses major health risk for patients with chronic pulmonary diseases such as asthma, bronchitis, and emphysema. Such risk can be mitigated by continuous exposure tracking. The effective dose of exposure is directly proportional to the respiratory minute volume, aka minute ventilation (VE). Till date, the clinical standard for measuring VE is Spirometry, a highly invasive and cumbersome modality, which is not suitable for continuous day-to-day use. This paper presents a novel non-invasive method toward continuous assessment of VE using a wrist-mount wearable motion sensor. Data from 25 healthy subjects were collected while they performed ambulatory and sedentary activities and physical exercises. Noise and artifacts of the motion signal are removed and the processed signal is used to extract explanatory features. The features are used to train and evaluate multiple regression models, among which, the probabilistic Gaussian process regression achieves the best performance in inferring VE from the wearable motion signal. The effects of inter- and intra-personal variations are explored to demonstrate the potential of the proposed method for continuously monitoring pollutant exposure risk in respiratory health applications.
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http://dx.doi.org/10.1109/EMBC.2019.8857949DOI Listing
July 2019

Multiple-Instance Learning for Sparse Behavior Modeling from Wearables: Toward Dementia-Related Agitation Prediction.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:1330-1333

Agitation in persons with dementia (PWD) poses major health risks both for themselves and for their caregivers. Passive sensing based continuous behavior tracking can prevent the escalation of such episodes. But, predicting such behavior from sensor streams, especially in real-world residential settings, is still an active area of research. Major challenges include the sparsity, unpredictability, and variations in such behavior, as well as the "weak" annotations from real-world participants. This paper proposes a novel approach to overcome these issues in predicting agitation episodes from the PWD's wrist motion data. In a transdisciplinary study on dementia dyads residing in their homes, the PWD motion is continuously sensed from their smart watch inertial sensors, while agitation episodes are actively marked by the caregivers. The data from 10 residential deployments, each with 30 days duration, are analyzed in this paper, and multiple-instance learning (MIL) based models are implemented to learn from such sparse and weakly annotated data. These models are compared with single-instance models in predicting the agitated behavior. The results show the potential of MIL models in sparsely labeled behavior inference from wearables in-the-wild.
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http://dx.doi.org/10.1109/EMBC.2019.8856502DOI Listing
July 2019

Leveraging Smart Health Technology to Empower Patients and Family Caregivers in Managing Cancer Pain: Protocol for a Feasibility Study.

JMIR Res Protoc 2019 Dec 9;8(12):e16178. Epub 2019 Dec 9.

The George Washington University School of Engineering & Applied Science, Washington, DC, United States.

Background: An estimated 60%-90% of patients with cancer experience moderate to severe pain. Poorly managed cancer pain negatively affects the quality of life for both patients and their family caregivers and can be a particularly challenging symptom to manage at home. Mobile and wireless technology ("Smart Health") has significant potential to support patients with cancer and their family caregivers and empower them to safely and effectively manage cancer pain.

Objective: This study will deploy a package of sensing technologies, known as Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C), and evaluate its feasibility and acceptability among patients with cancer-family caregiver dyads. Our primary aims are to explore the ability of BESI-C to reliably measure and describe variables relevant to cancer pain in the home setting and to better understand the dyadic effect of pain between patients and family caregivers. A secondary objective is to explore how to best share collected data among key stakeholders (patients, caregivers, and health care providers).

Methods: This descriptive two-year pilot study will include dyads of patients with advanced cancer and their primary family caregivers recruited from an academic medical center outpatient palliative care clinic. Physiological (eg, heart rate, activity) and room-level environmental variables (ambient temperature, humidity, barometric pressure, light, and noise) will be continuously monitored and collected. Behavioral and experiential variables will be actively collected when the caregiver or patient interacts with the custom BESI-C app on their respective smart watch to mark and describe pain events and answer brief, daily ecological momentary assessment surveys. Preliminary analysis will explore the ability of the sensing modalities to infer and detect pain events. Feasibility will be assessed by logistic barriers related to in-home deployment, technical failures related to data capture and fidelity, smart watch wearability issues, and patient recruitment and attrition rates. Acceptability will be measured by dyad perceptions and receptivity to BESI-C through a brief, structured interview and surveys conducted at deployment completion. We will also review summaries of dyad data with participants and health care providers to seek their input regarding data display and content.

Results: Recruitment began in July 2019 and is in progress. We anticipate the preliminary results to be available by summer 2021.

Conclusions: BESI-C has significant potential to monitor and predict pain while concurrently enhancing communication, self-efficacy, safety, and quality of life for patients and family caregivers coping with serious illness such as cancer. This exploratory research offers a novel approach to deliver personalized symptom management strategies, improve patient and caregiver outcomes, and reduce disparities in access to pain management and palliative care services.

International Registered Report Identifier (irrid): DERR1-10.2196/16178.
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http://dx.doi.org/10.2196/16178DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928698PMC
December 2019

Sensing eating mimicry among family members.

Transl Behav Med 2019 05;9(3):422-430

Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA.

Family relationships influence eating behavior and health outcomes (e.g., obesity). Because eating is often habitual (i.e., automatically driven by external cues), unconscious behavioral mimicry may be a key interpersonal influence mechanism for eating within families. This pilot study extends existing literature on eating mimicry by examining whether multiple family members mimicked each other's bites during natural meals. Thirty-three participants from 10 families were videotaped while eating an unstructured family meal in a kitchen lab setting. Videotapes were coded for participants' bite occurrences and times. We tested whether the likelihood of a participant taking a bite increased when s/he was externally cued by a family eating partner who had recently taken a bite (i.e., bite mimicry). A paired-sample t-test indicated that participants had a significantly faster eating rate within the 5 s following a bite by their eating partner, compared to their bite rate at other times (t = 7.32, p < .0001). Nonparametric permutation testing identified five of 78 dyads in which there was significant evidence of eating mimicry; and 19 of 78 dyads that had p values < .1. This pilot study provides preliminary evidence that suggests eating mimicry may occur among a subset of family members, and that there may be types of family ties more prone to this type of interpersonal influence during meals.
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http://dx.doi.org/10.1093/tbm/ibz051DOI Listing
May 2019

Use of Body Sensors to Examine Nocturnal Agitation, Sleep, and Urinary Incontinence in Individuals With Alzheimer's Disease.

J Gerontol Nurs 2018 Aug;44(8):19-26

Nighttime agitation, sleep disturbances, and urinary incontinence (UI) occur frequently in individuals with dementia and can add additional burden to family caregivers, although the co-occurrence of these symptoms is not well understood. The purpose of the current study was to determine the feasibility and acceptability of using passive body sensors in community-dwelling individuals with Alzheimer's disease (AD) by family caregivers and the correlates among these distressing symptoms. A single-group, descriptive design with convenience sampling of participants with AD and their family caregivers was undertaken to address the study aims. Results showed that using body sensors was feasible and acceptable and that patterns of nocturnal agitation, sleep, and UI could be determined and were correlated in study participants. Using data from body sensors may be useful to develop and implement targeted, individualized interventions to lessen these distressing symptoms and decrease caregiver burden. Further study in this field is warranted. [Journal of Gerontological Nursing, 44(8), 19-26.].
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http://dx.doi.org/10.3928/00989134-20180626-03DOI Listing
August 2018

Dynamical Properties of Postural Control in Obese Community-Dwelling Older Adults .

Sensors (Basel) 2018 May 24;18(6). Epub 2018 May 24.

Muhammad Ali Parkinson Center (MAPC), Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ 85013, USA.

Postural control is a key aspect in preventing falls. The aim of this study was to determine if obesity affected balance in community-dwelling older adults and serve as an indicator of fall risk. The participants were randomly assigned to receive a comprehensive geriatric assessment followed by a longitudinal assessment of their fall history. The standing postural balance was measured for 98 participants with a Body Mass Index (BMI) ranging from 18 to 63 kg/m², using a force plate and an inertial measurement unit affixed at the sternum. Participants' fall history was recorded over 2 years and participants with at least one fall in the prior year were classified as fallers. The results suggest that body weight/BMI is an additional risk factor for falling in elderly persons and may be an important marker for fall risk. The linear variables of postural analysis suggest that the obese fallers have significantly higher sway area and sway ranges, along with higher root mean square and standard deviation of time series. Additionally, it was found that obese fallers have lower complexity of anterior-posterior center of pressure time series. Future studies should examine more closely the combined effect of aging and obesity on dynamic balance.
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http://dx.doi.org/10.3390/s18061692DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6021983PMC
May 2018

Understanding the Physiological Significance of Four Inertial Gait Features in Multiple Sclerosis.

IEEE J Biomed Health Inform 2018 01;22(1):40-46

Gait impairment in multiple sclerosis (MS) can result from muscle weakness, physical fatigue, lack of coordination, and other symptoms. Walking speed, as measured by a number of clinician-administered walking tests, is the primary measure of gait impairment used by clinical researchers, but inertial gait features from body-worn sensors have been proven to add clinical value. This paper seeks to understand and differentiate the physiological significance of four such features with proven value in MS to facilitate adoption by clinical researchers and incorporation in gait monitoring and analysis systems. In addition, this information can be used to select features that might be appropriate in other forms of disability. Two of the four features are computed using the dynamic time warping (DTW) algorithm: The "DTW Score" is based on the usual DTW distance, and the "Warp Score" is based on the warping length. The third feature, based on kernel density estimation (KDE), is the "KDE Peak" value. Finally, the "Causality Index" is based on the phase slope index between inertial signals from different body parts. Relationships between these measures and the aforementioned gait-related symptoms are determined by applying factor analysis to three common, clinical walking outcomes, then correlating the inertial measures as well as walking speed to each extracted factor. Statistically significant differences in correlation coefficients to the three extracted clinical factors support their distinct physiological meaning and suggest they may have complimentary roles in the analysis of MS-related walking disability.
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http://dx.doi.org/10.1109/JBHI.2017.2773629DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5774022PMC
January 2018

Real-world walking in multiple sclerosis: Separating capacity from behavior.

Gait Posture 2018 01 16;59:211-216. Epub 2017 Oct 16.

Department of Neurology, University of Virginia, United States. Electronic address:

Background: Habitual physical activity (HPA) measurement addresses the impact of MS on real-world walking, yet its interpretation is confounded by the competing influences of MS-associated walking capacity and physical activity behaviors.

Objective: To develop specific measures of MS-associated walking capacity through statistically sophisticated HPA analysis, thereby more precisely defining the real-world impact of disease.

Methods: Eighty-eight MS and 38 control subjects completed timed walks and patient-reported outcomes in clinic, then wore an accelerometer for 7days. HPA was analyzed with several new statistics, including the maximum step rate (MSR) and habitual walking step rate (HWSR), along with conventional methods, including average daily steps. HPA statistics were validated using clinical walking outcomes.

Results: The six-minute walk (6MW) step rate correlated most strongly with MSR (r=0.863, p<10) and HWSR (r=0.815, p<10) rather than average daily steps (r=0.676, p<10). The combination of MSR and HWSR correlated more strongly with the 6MW step rate than either measure alone (r=0.884, p<10). The MSR overestimated the 6MW step rate (μ=10.4, p<10), whereas the HWSR underestimated it (μ=-18.2, p<10).

Conclusions: Conventional HPA statistics are poor measures of capacity due to variability in activity behaviors. The MSR and HWSR are valid, specific measures of real-world capacity which capture subjects' highest step rate and preferred step rate, respectively.
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http://dx.doi.org/10.1016/j.gaitpost.2017.10.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5695705PMC
January 2018

EHDC: An Energy Harvesting Modeling and Profiling Platform for Body Sensor Networks.

IEEE J Biomed Health Inform 2018 01 31;22(1):33-39. Epub 2017 Jul 31.

Energy harvesting is a promising solution to the limited battery lifetimes of body sensor nodes. Self-powered sensor systems capable of quasi-perpetual operation enable the possibility of truly continuous monitoring of patients beyond the clinic. However, the discontinuous and dynamic characteristics of harvesting in real-world scenarios-and their implications for the design and operation of self-powered systems-are not yet well understood. This paper presents a mobile energy harvesting and data collection (EHDC) platform designed to provide a deeper understanding of energy harvesting dynamics. The EHDC platform monitors and records the instantaneous usable power generated by body-worn harvesters, while also collecting human activity and environmental data to provide a comprehensive real-world evaluation of two energy harvesting modalities common to body sensor networks: solar and thermoelectric. The platform was initially validated with benchtop tests and later with real-world deployments on two subjects. 7-h-long multimodal energy harvesting profiles were generated, and the environmental and behavioral data were used to expand upon previously developed Kalman filter based mathematical models for energy harvesting prediction. Results confirm the validity of the EHDC platform and harvesting models, establishing the potential for longer term monitoring of energy harvesting characteristics; thus, informing the design and operation of self-powered body sensor networks.
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http://dx.doi.org/10.1109/JBHI.2017.2733549DOI Listing
January 2018

Remotely engaged: Lessons from remote monitoring in multiple sclerosis.

Int J Med Inform 2017 04 12;100:26-31. Epub 2017 Jan 12.

Department of Neurology, University of Virginia, Charlottesville, VA, USA.

Objectives: Evaluate web-based patient-reported outcome (wbPRO) collection in MS subjects in terms of feasibility, reliability, adherence, and subject-perceived benefits; and quantify the impact of MS-related symptoms on perceived well-being.

Methods: Thirty-one subjects with MS completed wbPROs targeting MS-related symptoms over six months using a customized web portal. Demographics and clinical outcomes were collected in person at baseline and six months.

Results: Approximately 87% of subjects completed wbPROs without assistance, and wbPROs strongly correlated with standard PROs (r>0.91). All wbPROs were completed less frequently in the second three months (p<0.05). Frequent wbPRO completion was significantly correlated with higher step on the Expanded Disability Status Scale (EDSS) (p=0.026). Nearly 52% of subjects reported improved understanding of their disease, and approximately 16% wanted individualized wbPRO content. Over half (63.9%) of perceived well-being variance was explained by MS symptoms, notably depression (r=-0.459), fatigue (r=-0.390), and pain (r=-0.389).

Conclusions: wbPRO collection was feasible and reliable. More disabled subjects had higher completion rates, yet most subjects failed requirements in the second three months. Remote monitoring has potential to improve patient-centered care and communication between patient and provider, but tailored PRO content and other innovations are needed to combat declining adherence.
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http://dx.doi.org/10.1016/j.ijmedinf.2017.01.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5331862PMC
April 2017

Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review.

IEEE J Biomed Health Inform 2016 11;20(6):1521-1537

After decades of evolution, measuring instruments for quantitative gait analysis have become an important clinical tool for assessing pathologies manifested by gait abnormalities. However, such instruments tend to be expensive and require expert operation and maintenance besides their high cost, thus limiting them to only a small number of specialized centers. Consequently, gait analysis in most clinics today still relies on observation-based assessment. Recent advances in wearable sensors, especially inertial body sensors, have opened up a promising future for gait analysis. Not only can these sensors be more easily adopted in clinical diagnosis and treatment procedures than their current counterparts, but they can also monitor gait continuously outside clinics - hence providing seamless patient analysis from clinics to free-living environments. The purpose of this paper is to provide a systematic review of current techniques for quantitative gait analysis and to propose key metrics for evaluating both existing and emerging methods for qualifying the gait features extracted from wearable sensors. It aims to highlight key advances in this rapidly evolving research field and outline potential future directions for both research and clinical applications.
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http://dx.doi.org/10.1109/JBHI.2016.2608720DOI Listing
November 2016

Quantifying six-minute walk induced gait deterioration with inertial sensors in multiple sclerosis subjects.

Gait Posture 2016 09 27;49:340-345. Epub 2016 Jul 27.

Department of Neurology, University of Virginia, P.O. Box 800394, Charlottesville, VA, 22908, USA. Electronic address:

Background: The six-minute walk (6MW) is a common walking outcome in multiple sclerosis (MS) thought to measure fatigability in addition to overall walking disability. However, direct evidence of 6MW induced gait deterioration is limited by the difficulty of measuring qualitative changes in walking.

Objectives: This study aims to (1) define and validate a measure of fatigue-related gait deterioration based on data from body-worn sensors; and (2) use this measure to detect gait deterioration induced by the 6MW.

Methods: Gait deterioration was assessed using the Warp Score, a measure of similarity between gait cycles based on dynamic time warping (DTW). Cycles from later minutes were compared to baseline cycles in 89 subjects with MS and 29 controls. Correlation, corrected (partial) correlation, and linear regression were used to quantify relationships to walking and fatigue outcomes.

Results: Warp Scores rose between minute 3 and minute 6 in subjects with mild and moderate disability (p<0.001). Statistically significant correlations (p<0.001) to the MS walking scale (MSWS-12), modified fatigue impact scale (MFIS) physical subscale, and cerebellar and pyramidal functional system scores (FSS) were observed even after controlling for walking speed. Regression of MSWS-12 scores on Warp Scores and walking speed explained 73.9% of response variance. Correlations to individual MSWS-12 and MFIS items strongly suggest a relationship to fatigability.

Conclusion: The Warp Score has been validated in MS subjects as an objective measure of fatigue-related gait deterioration. Progressive changes to gait cycles induced by the 6MW often appeared in later minutes, supporting the importance of sustained walking in clinical assessment.
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http://dx.doi.org/10.1016/j.gaitpost.2016.07.184DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5035201PMC
September 2016

Causality Analysis of Inertial Body Sensors for Multiple Sclerosis Diagnostic Enhancement.

IEEE J Biomed Health Inform 2016 09 9;20(5):1273-80. Epub 2016 Jul 9.

Inertial body sensors have emerged in recent years as an effective tool for evaluating mobility impairment resulting from various diseases, disorders, and injuries. For example, body sensors have been used in 6-min walk (6 MW) tests for multiple sclerosis (MS) patients to identify gait features useful in the study, diagnosis, and tracking of the disease. However, most studies to date have focused on features localized to the lower or upper extremities and do not provide a holistic assessment of mobility. This paper presents a causality analysis method focused on the coordination between extremities to identify subtle whole-body mobility impairment that may aid disease diagnosis. This method was developed for and utilized in an MS pilot study with 41 subjects (28 persons with MS (PwMS) and 13 healthy controls) performing 6 MW tests. Compared with existing methods, the causality analysis provided better discrimination between healthy controls and PwMS and a deeper understanding of MS disease impact on mobility.
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http://dx.doi.org/10.1109/JBHI.2016.2589902DOI Listing
September 2016

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE).

Multivariate Behav Res 2015 ;50(6):706-20

g Department of Psychiatry, Virginia Commonwealth University.

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belong to, be controlled by, and remain in the possession of the participants themselves. Distributed likelihood estimation refers to fitting statistical models by sending an objective function and vector of parameters to each participant's personal device (e.g., smartphone, tablet, computer), where the likelihood of that individual's data is calculated locally. Only the likelihood value is returned to the central optimizer. The optimizer aggregates likelihood values from responding participants and chooses new vectors of parameters until the model converges. A MIDDLE study provides significantly greater privacy for participants, automatic management of opt-in and opt-out consent, lower cost for the researcher and funding institute, and faster determination of results. Furthermore, if a participant opts into several studies simultaneously and opts into data sharing, these studies automatically have access to individual-level longitudinal data linked across all studies.
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http://dx.doi.org/10.1080/00273171.2015.1094387DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4804354PMC
December 2016

Activity classification in users of ankle foot orthoses.

Gait Posture 2014 Jan 25;39(1):111-7. Epub 2013 Jul 25.

Department of Mechanical & Aerospace Engineering, University of Virginia, Charlottesville, VA, United States.

A framework for activity classification using inertial sensors mounted on ankle foot orthoses (AFOs) is presented. A decision tree-nearest neighbor algorithm classifies activities using subject-specific training. Eight volunteer subjects wore modified bilateral AFOs with shank and foot mounted triaxial accelerometers and gyroscopes. The AFOs were fitted with hardware to induce different gait perturbations: free rotation of the ankle, plantarflexion or "equinus" gait, and locked ankle joint. For each condition, the subject performed eight gait activities at varied slopes and standing, sitting, and lying postures. Using video for ground truth, the algorithm had an overall mean sensitivity of 95% using 50% of the data (≈ 140 s) for training and demonstrated upwards of 90% sensitivity with 25% of the data (≈ 70 s) for training. High sensitivities (≥ 87%) and PPV (≥ 90%) were achieved for all annotated gait patterns for all perturbations, excluding stair climbing (63%, 77%) and descending (80%, 78%). Postures were classified with less sensitivity and PPV than gait activities: lying (98%, 93%), standing (80%, 84%) and sitting (64%, 75%). Non-annotated walking (68%) and standing (73%) were classified with less sensitivity than were corresponding annotated events. Our results indicate that AFOs are a suitable sensor platform for future research in activity classification and gait monitoring in AFO users with perturbed gait using limited training data.
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http://dx.doi.org/10.1016/j.gaitpost.2013.06.005DOI Listing
January 2014

Effects of hemodialysis therapy on sit-to-walk characteristics in end stage renal disease patients.

Ann Biomed Eng 2013 Apr 5;41(4):795-805. Epub 2012 Dec 5.

School of Biomedical Engineering and Sciences, Virginia Tech Wake Forest University, Blacksburg, VA 24061, USA.

Patients with end stage renal diseases (ESRD) undergoing hemodialysis (HD) have high morbidity and mortality due to multiple causes; one of which is dramatically higher fall rates than the general population. In spite of the multiple efforts aiming to decrease the high mortality and improve quality of life in ESRD patients, limited success has been achieved. If adequate interventions for fall prevention are to be achieved, the functional and mobility mechanisms consistent with falls in this population must be understood. Human movements such as sit-to-walk (STW) tasks are clinically significant, and analysis of these movements provides a meaningful evaluation of postural and locomotor performance in elderly patients with functional limitations indicative of fall risks. In order to assess the effects of HD therapy on fall risks, 22 sessions of both pre- and post-HD measurements were obtained in six ESRD patients utilizing customized inertial measurement units (IMU). IMU signals were denoised using ensemble empirical mode decomposition and Savistky-Golay filtering methods to detect relevant events for identification of STW phases. The results indicated that patients were slower to get out of the chair (as measured by trunk flexion angular accelerations, time to peak trunk flexion, and overall STW completion time) following the dialysis therapy session. STW is a frequent movement in activities of daily living, and HD therapy may influence the postural and locomotor control of these movements. The analysis of STW movement may assist in not only assessing a patient's physical status, but in identifying HD-related fall risk as well. This preliminary study presents a non-invasive method of kinematic measurement for early detection of increased fall risk in ESRD patients using portable inertial sensors for out-patient monitoring. This can be helpful in understanding the pathogenesis better, and improve awareness in health care providers in targeting interventions to identify individuals at risk for fall.
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http://dx.doi.org/10.1007/s10439-012-0701-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3606691PMC
April 2013

Validation of noninvasive body sensor network technology in the detection of agitation in dementia.

Am J Alzheimers Dis Other Demen 2012 Aug;27(5):346-54

Department of Geriatric Medicine, Virginia Tech Carilion School of Medicine, Roanoke, VA, USA.

Objective: Agitated behaviors are one of the most frequent reasons that patients with dementia are placed in long-term care settings. This study aims to validate the ability of a custom Body Sensor Network (BSN) to capture the presence of agitation against currently accepted subjective measures, the Cohen-Mansfield Agitation Inventory (CMAI) and the Aggressive Behavior Scale (ABS) and to discriminate between agitation and cognitive decline.

Methods: Six patients identified as being at high risk for agitated behaviors were enrolled in this study. The devices were applied at three sites for three hours while behaviors were annotated simultaneously and subsequently repeated twice for each enrolled subject.

Results: We found that the BSN was a valid measure of agitation based on construct validity testing and secondary validation using non-parametric ANOVAs.

Discussion: The BSN shows promise from these pilot results. Further testing with a larger sample is needed to replicate these results.
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http://dx.doi.org/10.1177/1533317512452036DOI Listing
August 2012

Portable, Non-Invasive Fall Risk Assessment in End Stage Renal Disease Patients on Hemodialysis.

ACM Trans Comput Hum Interact 2010 :84-93

Virginia Tech, Grado Department of Industrial and Systems Engineering, Blacksburg, VA, USA 24061.

Patients with end stage renal diseases (ESRD) on hemodialysis (HD) have high morbidity and mortality due to multiple causes, one of which is dramatically higher fall rates than the general population. The mobility mechanisms that contribute to falls in this population must be understood if adequate interventions for fall prevention are to be achieved. This study utilizes emerging non-invasive, portable gait, posture, strength, and stability assessment technologies to extract various mobility parameters that research has shown to be predictive of fall risk in the general population. As part of an ongoing human subjects study, mobility measures such as postural and locomotion profiles were obtained from five (5) ESRD patients undergoing HD treatments. To assess the effects of post-HD-fatigue on fall risk, both the pre- and post-HD measurements were obtained. Additionally, the effects of inter-HD periods (two days vs. three days) were investigated using the non-invasive, wireless, body-worn motion capture technology and novel signal processing algorithms. The results indicated that HD treatment influenced strength and mobility (i.e., weaker and slower after the dialysis, increasing the susceptibility to falls while returning home) and inter-dialysis period influenced pre-HD profiles (increasing the susceptibility to falls before they come in for a HD treatment). Methodology for early detection of increased fall risk - before a fall event occurs - using the portable mobility assessment technology for out-patient monitoring is further explored, including targeting interventions to identified individuals for fall prevention.
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http://dx.doi.org/10.1145/1921081.1921092DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3223867PMC
January 2010