Time filter

Source Type

Pittsburgh, PA, United States

Hiremath S.V.,Human Engineering Research Laboratories | Hiremath S.V.,University of Pittsburgh | Ding D.,Human Engineering Research Laboratories | Ding D.,University of Pittsburgh | And 3 more authors.
Archives of Physical Medicine and Rehabilitation | Year: 2012

Objective: To develop and evaluate new energy expenditure (EE) prediction models for manual wheelchair users (MWUs) with spinal cord injury (SCI) based on a commercially available multisensor-based activity monitor. Design: Cross-sectional. Setting: Laboratory. Participants: Volunteer sample of MWUs with SCI (N=45). Intervention: Subjects were asked to perform 4 activities including resting, wheelchair propulsion, arm-ergometer exercise, and deskwork. Criterion EE using a metabolic cart and raw sensor data from a multisensor activity monitor was collected during each of these activities. Main Outcome Measures: Two new EE prediction models including a general model and an activity-specific model were developed using enhanced all-possible regressions on 36 MWUs and tested on the remaining 9 MWUs. Results: The activity-specific and general EE prediction models estimated the EE significantly better than the manufacturer's model. The average EE estimation error using the manufacturer's model and the new general and activity-specific models for all activities combined was -55.31% (overestimation), 2.30% (underestimation), and 4.85%, respectively. The average EE estimation error using the manufacturer's model, the new general model, and activity-specific models for various activities varied from -19.10% to -89.85%, -18.13% to 25.13%, and -4.31% to 9.93%, respectively. Conclusions: The predictors for the new models were based on accelerometer and demographic variables, indicating that movement and subject parameters were necessary in estimating the EE. The results indicate that the multisensor activity monitor with new prediction models can be used to estimate EE in MWUs with SCI during wheelchair-related activities mentioned in this study. © 2012 American Congress of Rehabilitation Medicine.

Hiremath S.V.,Human Engineering Research Laboratories | Hiremath S.V.,University of Pittsburgh | Ding D.,Human Engineering Research Laboratories | Ding D.,University of Pittsburgh | And 4 more authors.
Spinal Cord | Year: 2013

Study design:Validation.Objectives:The primary aim of this study was to develop and evaluate activity classification algorithms for a multisensor-based SenseWear (SW) activity monitor that can recognize wheelchair-related activities performed by manual wheelchair users (MWUs) with spinal cord injury (SCI). The secondary aim was to evaluate how the accuracy in activity classification affects the estimation of energy expenditure (EE) in MWUs with SCI.Setting:University-based laboratory.Methods:Forty-five MWUs with SCI wore a SW on their upper arm and participated in resting, wheelchair propulsion, arm-ergometery and deskwork activities. The investigators annotated the start and end of each activity trial while the SW collected multisensor data and a portable metabolic cart collected criterion EE. Three methods including linear discriminant analysis, quadratic discriminant analysis (QDA), and Naïve Bayes (NB) were used to develop classification algorithms for four activities based on the training data set from 36 subjects.Results:The classification accuracy was 96.3% for QDA and 94.8% for NB when the classification algorithms were tested on the validation data set from nine subjects. The average EE estimation errors using the activity-specific EE prediction model were 5.3±21.5% and 4.6±22.8% when the QDA and NB classification algorithms were applied, respectively, as opposed to 4.9±20.7% when 100% classification accuracy was assumed.Conclusion:The high classification accuracy and low EE estimation errors suggest that the SW can be used by researchers and clinicians to classify and estimate the EE for the four activities tested in this study among MWUs with SCI. © 2013 International Spinal Cord Society.

Rollins D.K.,Iowa State University | Bhandari N.,Iowa State University | Kleinedler J.,Iowa State University | Kotz K.,Iowa State University | And 7 more authors.
Journal of Process Control | Year: 2010

The goal of this work is to present a causation modeling methodology with the ability to accurately infer blood glucose levels using a large set of highly correlated noninvasive input variables over an extended period of time. These models can provide insight to improve glucose monitoring, and glucose regulation through advanced model-based control technologies. The efficacy of this approach is demonstrated using real data from a type 2 diabetic (T2D) subject collected under free-living conditions over a period of 25 consecutive days. The model was identified and tested using eleven variables that included three food variables as well as several activity and stress variables. The model was trained using 20 days of data and validated using 5 days of data. This gave a fitted correlation coefficient of 0.70 and an average absolute error (AAE) (i.e., the average of the absolute values for the measured glucose concentration minus modeled glucose concentration) of 13.3 mg/dL for the validation data. This AAE result was significantly better than the subject's personal glucose meter AAE of 15.3 mg/dL for replicated measurements.

Beverlin L.P.,Intel Corporation | Rollins D.K.,Iowa State University | Vyas N.,BodyMedia Inc. | Andre D.,BodyMedia Inc.
Mathematical Problems in Engineering | Year: 2011

The purpose of this work is to present a new methodology for fitting Wiener networks to datasets with a large number of variables. Wiener networks have the ability to model a wide range of data types, and their structures can yield parameters with phenomenological meaning. There are several challenges to fitting such a model: model stiffness, the nonlinear nature of a Wiener network, possible overfitting, and the large number of parameters inherent with large input sets. This work describes a methodology to overcome these challenges by using several iterative algorithms under supervised learning and fitting subsets of the parameters at a time. This methodology is applied to Wiener networks that are used to predict blood glucose concentrations. The predictions of validation sets from models fit to four subjects using this methodology yielded a higher correlation between observed and predicted observations than other algorithms, including the Gauss-Newton and Levenberg-Marquardt algorithms. © 2011 Lucas P. Beverlin et al.

Rickards C.A.,University of North Texas Health Science Center | Rickards C.A.,University of Texas at San Antonio | Vyas N.,BodyMedia Inc. | Ryan K.L.,U.S. Army | And 5 more authors.
Journal of Applied Physiology | Year: 2014

Due to limited remote triage monitoring capabilities, combat medics cannot currently distinguish bleeding soldiers from those engaged in combat unless they have physical access to them. The purpose of this study was to test the hypothesis that low-level physiological signals can be used to develop a machine-learning algorithm for tracking changes in central blood volume that will subsequently distinguish central hypovolemia from physical activity. Twenty-four subjects underwent central hypovolemia via lower body negative pressure (LBNP), and a supine-cycle exercise protocol. Exercise workloads were determined by matching heart rate responses from each LBNP level. Heart rate and stroke volume (SV) were measured via Finometer. ECG, heat flux, skin temperature, galvanic skin response, and two-axis acceleration were obtained from an armband (SenseWear Pro2) and used to develop a machine-learning algorithm to predict changes in SV as an index of central blood volume under both conditions. The algorithm SV was retrospectively compared against Finometer SV. A model was developed to determine whether unknown data points could be correctly classified into these two conditions using leave-one-out cross-validation. Algorithm vs. Finometer SV values were strongly correlated for LBNP in individual subjects (mean r = 0.92; range 0.75- 0.98), but only moderately correlated for exercise (mean r = 0.50; range -0.23- 0.87). From the first level of LBNP/exercise, the machine-learning algorithm was able to distinguish between LBNP and exercise with high accuracy, sensitivity, and specificity (all ≥90%). In conclusion, a machine-learning algorithm developed from low-level physiological signals could reliably distinguish central hypovolemia from exercise, indicating that this device could provide battlefield remote triage capabilities. © 2014 the American Physiological Society.

Discover hidden collaborations