Harshvardhan Vathsangam

KNOWME: a case study in wireless body area sensor network design

Wireless body area sensing networks have the potential to revolutionize health care in the near term. The coupling of biosensors with a wireless infrastructure enables the real-time monitoring of an individuals health and related behaviors continuously, as well as the provision of realtime feedback with nimble, adaptive, and personalized interventions. The KNOWME platform is reviewed, and lessons learned from system integration, optimization, and in-field deployment are provided. KNOWME is an endto- end body area sensing system that integrates off-the-shelf sensors with a Nokia N95 mobile phone to continuously monitor and analyze the biometric signals of a subject. KNOWME development by an interdisciplinary team and in-laboratory, as well as in-field deployment studies, employing pediatric obesity as a case study condition to monitor and evaluate physical activity, have revealed four major challenges: (1) achieving robustness to highly varying operating environments due to subject-induced variability such as mobility or sensor placement, (2) balancing the tension between acquiring high fidelity data and minimizing network energy consumption, (3) enabling accurate physical activity detection using a modest number of sensors, and (4) designing WBANs to determine physiological quantities of interest such as energy expenditure. The KNOWME platform described in this article directly addresses these challenges.

Determining Energy Expenditure From Treadmill Walking Using Hip-Worn Inertial Sensors: An Experimental Study

We describe an experimental study to estimate energy expenditure during treadmill walking using a single hip-mounted inertial sensor (triaxial accelerometer and triaxial gyroscope). Typical physical-activity characterization using commercial monitors use proprietary counts that do not have a physically interpretable meaning. This paper emphasizes the role of probabilistic techniques in conjunction with inertial data modeling to accurately predict energy expenditure for steady-state treadmill walking. We represent the cyclic nature of walking with a Fourier transform and show how to map this representation to energy expenditure ([(V)\dot]O2, mL/min) using three regression techniques. A comparative analysis of the accuracy of sensor streams in predicting energy expenditure reveals that using triaxial information leads to more accurate energy-expenditure prediction compared to only using one axis. Combining accelerometer and gyroscope information leads to improved accuracy compared to using either sensor alone. Nonlinear regression methods showed better prediction accuracy compared to linear methods but required an order of higher magnitude run time.

Toward free-living walking speed estimation using Gaussian Process-based Regression with on-body accelerometers and gyroscopes

Walking speed is an important determinant of energy expenditure. We present the use of Gaussian Process-based Regression (GPR), a non-linear, non-parametric regression framework to estimate walking speed using data obtained from a single on-body sensor worn on the right hip. We compare the performance of GPR with Bayesian Linear Regression (BLR) and Least Squares Regression (LSR) in estimating treadmill walking speeds. We also examine whether using gyroscopes to augment accelerometry data can improve prediction accuracy. GPR shows a lower average RMS prediction error when compared to BLR and LSR across all subjects. Per subject, GPR has significantly lower RMS prediction error than LSR and BLR (p≪0.05) with increasing training data. The addition of tri-axial gyroscopes as inputs reduces RMS prediction error (p≪0.05 per subject) when compared to using only acclerometers. We also study the effect of using treadmill walking data to predict overground walking speeds and that of combining data from more than one person to predict overground walking speed. A strong linear correlation exists (rX,Y = .8861) between overground walking speeds predicted from treadmill data and ground truth walking speed measured. Combining treadmill data from multiple subjects with similar height characteristics improved the prediction capability of GPR for overground walking speeds as measured by correllation between ground truth and GP-predicted values (rX,Y = .8204 with combined data).

Energy estimation of treadmill walking using on-body accelerometers and gyroscopes

Walking is the most common activity among people who are physically active. Standard practice physical activity characterization from body-mounted inertial sensors uses accelerometer-generated counts. There are two problems with this - imprecison (due to usage of proprietary counts) and incompleteness (due to incomplete description of motion). We address both these problems by directly predicting energy expenditure during steady-state treadmill walking from a hip-mounted inertial sensor comprised of a tri-axial accelerometer and a tri-axial gyroscope. We use Bayesian Linear Regression to predict energy expenditure based on modelling joint probabilities of streaming data. The prediction is significantly better with data from a 6 axis sensor as compared with streaming data from only 2 linear accelerations as is common in current practice. We also show how counts from a commercially available accelerometer can be reproduced from raw streaming acceleration data (up to a linear transformation) with high correlation (.9787 ± .0089 for the X-axis and .9141 ± .0460 for the Y-axis acceleration streams). The paper emphasizes the role of probabilistic techniques in conjunction with joint modeling of tri-axial accelerations and rotational rates to improve energy expenditure prediction for steady-state treadmill walking.

Hierarchical probabilistic regression for AUV-based adaptive sampling of marine phenomena

Marine phenomena such as algal blooms can be detected using in situ measurements onboard autonomous underwater vehicles (AUVs), but understanding plankton ecology and community structure requires retrieval and analysis of water specimens. This process requires shipboard or manual sample collection, followed by onshore lab analysis which is time-consuming. Better understanding of the relationship between the observable environmental features and organism abundance would allow more precisely targeted sampling and thereby save time. In this work, we present an approach to learn and improve models that predict this relationship. Coupled with recent advances in AUV technology allowing selective retrieval of water samples, this constitutes a new paradigm in biological sampling. We use organism abundance models along with spatial models of environmental features learned immediately after AUV deployments to compute spatial distributions of organisms in the coastal ocean purely from in situ AUV data. We use Gaussian process regression along with the unscented transform to fuse the two models, obtaining both the mean and variance of the organism abundance estimates. The uncertainty in organism abundance predictions is used in a sampling strategy to selectively acquire new water specimens that improves the organism abundance models. Simulation results are presented demonstrating the advantage of performing hierarchical probabilistic regression. After the validation through simulation, we show predictions of organism abundance from models learned on lab-analyzed water sample data, and AUV survey data.

Hierarchical Approaches to Estimate Energy Expenditure Using Phone-Based Accelerometers

Physical inactivity is linked with increase in risk of cancer, heart disease, stroke, and diabetes. Walking is an easily available activity to reduce sedentary time. Objective methods to accurately assess energy expenditure from walking that is normalized to an individual would allow tailored interventions. Current techniques rely on normalization by weight scaling or fitting a polynomial function of weight and speed. Using the example of steady-state treadmill walking, we present a set of algorithms that extend previous work to include an arbitrary number of anthropometric descriptors. We specifically focus on predicting energy expenditure using movement measured by mobile phone-based accelerometers. The models tested include nearest neighbor models, weight-scaled models, a set of hierarchical linear models, multivariate models, and speed-based approaches. These are compared for prediction accuracy as measured by normalized average root mean-squared error across all participants. Nearest neighbor models showed highest errors. Feature combinations corresponding to sedentary energy expenditure, sedentary heart rate, and sex alone resulted in errors that were higher than speed-based models and nearest-neighbor models. Size-based features such as BMI, weight, and height produced lower errors. Hierarchical models performed better than multivariate models when size-based features were used. We used the hierarchical linear model to determine the best individual feature to describe a person. Weight was the best individual descriptor followed by height. We also test models for their ability to predict energy expenditure with limited training data. Hierarchical models outperformed personal models when a low amount of training data were available. Speed-based models showed poor interpolation capability, whereas hierarchical models showed uniform interpolation capabilities across speeds.

A Data-Driven Movement Model for Single Cellphone-Based Indoor Positioning

Indoor localization is a promising area with applications in in-home monitoring and tracking. Fingerprinting and propagation model-based WiFi localization techniques have limited spatial resolution because of grid or graph-based representations. An alternative is to incorporate dynamics models based on real-time sensing of human movement and fuse these with WiFi measurements. We present a data-driven dynamic model that tracks the inherent periodicity in walking and converts this representation into velocity. This model however is prone to drift. We correct this drift with WiFi measurements to obtain a combined position estimate. Our approach records and fuses human body movement with WiFi positioning using a single mobile phone. We characterize the movement model to obtain an estimate of error predictions. The movement model showed a best case average RMS prediction error of .25 m/s. We also present a preliminary study characterizing combined system performance across straight line and L-shaped trajectories. The framework showed lower errors across the L-shaped trajectories (mean error = 4.8 m using movement sensing versus a mean error = 6 m without movement sensing) because of the ability assess the validity of a WiFi measurement. Higher errors were observed across the straight line trajectory due to imprecise trajectories.

Towards practical energy expenditure estimation with mobile phones

Regular physical activity plays a significant role in reducing the risk of obesity and maintaining peoples health conditions. Among all the physical activities, walking is a commonly recommended intervention for combating lifestyle diseases. The capability to accurately measure the energy expenditure of walking provides foundations to base the corresponding intervention. In this paper, we develop a set of signal processing and statistical pattern recognition techniques to estimate energy expenditure of walking in real-life settings using mobile phones. We examine the robustness of our proposed techniques to variations in location on the human body and across body types. We show that our proposed techniques can estimate step frequencies for three common locations of phone usage and achieve promising energy expenditure estimation accuracy with limited training data.

Using phone-based activity monitors to promote physical activity in older adults: A pilot study

Physical inactivity is a leading risk factor in a number of chronic diseases. Older adults are at the highest risk because they are the least inactive of all age groups. Smartphone-based intervention techniques present the opportunity to support cost-effective behavioral intervention to promote physical activity. We present a pilot study that examines the feasibility of utilizing the “Strive” smartphone-based passive sensing application to motivate older adults to stay active. Eight participants aged between 50 and 80 years used the application over a period of three weeks to track their daily activity habits. Participants carried smartphones to track daily activities and introspected about their lifestyles as part of weekly check-in sessions. The value that 24×7 smartphone-based sensors provided to this population was an easy-to-use and always available companion to promote awareness about their physical activity. The main motivations for participants to stay active were to prevent loss of personal health due ageing and the need to stay young. Participants increased their daily physical activity by 15% over the first week. This increase was mainly driven by the younger participants. Participants were willing to bear the inconvenience of physical activity tracking due a phone if it meant that they would obtain a better report of their daily physical activities. Any application that tracks physical activity using smartphones needs to address the wearability of the phone.

An inertial sensor-based system to develop motor capacity in children with cerebral palsy

Learning to communicate with alternative augmentative communication devices can be difficult because of the difficulty of achieving controlled interaction while simultaneously learning to communicate. What is needed is a device that harnesses a childs natural motor capabilities and provides the means to reinforce them. We present a kinematic sensor-based system that learns a childs natural gestural capability and allows him/her to practice those capabilities in the context of a game. Movement is captured with a single kinematic sensor that can be worn anywhere on the body. A gesture recognition algorithm interactively learns gesture models using kinematic data with the help of a nearby teacher. Learned gesture models are applied in the context of a game to help the child practice gestures to gain better consistency. The system was successfully tested with a child over two sessions. The system learned four candidate gestures: lift hand, sweep right, twist right and punch forward. These were then used in a game. The child showed better consistency in performing the gestures as each session progressed. We aim to expand on this work by developing qualitative scores of movement quality and quantifying algorithm accuracy on a larger population over long periods of time.