Classification

Adams, Roy, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar, and Benjamin Marlin. "Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams." International Conference on Machine Learning. 2016. Abstracticml2016_hns.pdf

The field of mobile health (mHealth) has the potential to yield new insights into health and behavior through the analysis of continuously recorded data from wearable health and activity sensors. In this paper, we present a hierarchical span-based conditional random field model for the key problem of jointly detecting discrete events in such sensor data streams and segmenting these events into high-level activity sessions. Our model includes higher-order cardinality factors and inter-event duration factors to capture domain-specific structure in the label space. We show that our model supports exact MAP inference in quadratic time via dynamic programming, which we leverage to perform learning in the structured support vector machine framework. We apply the model to the problems of smoking and eating detection using four real data sets. Our results show statistically significant improvements in segmentation performance at the p=0.005 level relative to a hierarchical pairwise CRF.

Dadkhahi, Hamid, Nazir Saleheen, Santosh Kumar, and Benjamin Marlin. "Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications." ICML On Device Intelligence Workshop. 2016. Abstractdadkhahi-icml-odi2017.pdf

The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions. However, existing work has largely focused on analyzing collected data in the off-line setting. In this paper, we propose a novel approach to learning shallow detection cascades developed explicitly for use in a real-time wearable-phone or wearable-phone-cloud systems. We apply our approach to the problem of cigarette smoking detection from a combination of wrist-worn actigraphy data and respiration chest band data using two and three stage cascades.

Nguyen, Thai, Roy J. Adams, Annamalai Natarajan, and Benjamin M. Marlin Parsing Wireless Electrocardiogram Signals with the CRF-CFG Model. Conference on Uncertainty in Artificial Intelligence Machine Learning for Health Workshop., 2016. Abstractnguyen-uai-health2016.pdf

Recent advances in wearable sensor technology have made it possible to simultaneously collect multiple streams of physiological and context data from individuals as they go about their daily activities in natural environments. However, extracting reliable higher-level inferences from these raw data streams remains a key data analysis challenge. In this paper, we focus on the specific case of the analysis of data from wireless electrocardiogram (ECG) sensors. We present a new robust probabilistic approach to ECG morphology extraction using conditional random field context free grammar models, which have traditionally been applied to parsing problems in natural language processing. We introduce a robust context free grammar for parsing noisy ECG data, and show significantly improved performance on the ECG morphological labeling task.

Nguyen, Thai, Roy J. Adams, Annamalai Natarajan, and Benjamin M. Marlin. "Parsing Wireless Electrocardiogram Signals with Context Free Grammar Conditional Random Fields." IEEE Wireless Health. 2016. Abstractnguyen-wh2016.pdf

Recent advances in wearable sensor technology have made it possible to simultaneously collect multiple streams of physiological and context data from individuals as they go about their daily activities in natural environments. However, extracting reliable higher-level inferences from these raw data streams remains a key data analysis challenge. In this paper, we focus on the specific case of the analysis of data from wireless electrocardiogram (ECG) sensors. We present a new robust probabilistic approach to ECG morphology extraction using conditional random field context free grammar models, which have traditionally been applied to parsing problems in natural language processing. We introduce a robust context free grammar for parsing noisy ECG data, and show significantly improved performance on the ECG morphological labeling task.

Natarajan, Annamalai, Gustavo Angarita, Edward Gaiser, Robert Malison, Deepak Ganesan, and Benjamin Marlin. "Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection using Wearable ECG." 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2016. Abstractnatarajan-ubicomp16.pdf

Mobile health research on illicit drug use detection typically involves a two-stage study design where data to learn detectors is first collected in lab-based trials, followed by a deployment to subjects in a free-living environment to assess detector performance. While recent work has demonstrated the feasibility of wearable sensors for illicit drug use detection in the lab setting, several key problems can limit lab-to-field generalization performance. For example, lab-based data collection often has low ecological validity, the ground-truth event labels collected in the lab may not be available at the same level of temporal granularity in the field, and there can be significant variability between subjects. In this paper, we present domain adaptation methods for assessing and mitigating potential sources of performance loss in lab-to-field generalization and apply them to the problem of cocaine use detection from wearable electrocardiogram sensor data.

Li, Steven Cheng-Xian, and Benjamin M. Marlin A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification. Advances in Neural Information Processing Systems., 2016. Abstractli-nips2016.pdf

We present a general framework for classification of sparse and irregularly-sampled time series. The properties of such time series can result in substantial uncertainty about the values of the underlying temporal processes, while making the data difficult to deal with using standard classification methods that assume fixed-dimensional feature spaces. To address these challenges, we propose an uncertainty-aware classification framework based on a special computational layer we refer to as the Gaussian process adapter that can connect irregularly sampled time series data to to any black-box classifier learnable using gradient descent. We show how to scale up the required computations based on combining the structured kernel interpolation framework and the Lanczos approximation method, and how to discriminatively train the Gaussian process adapter in combination with a number of classifiers end-to-end using backpropagation.

Hiatt, Laura, Roy Adams, and Benjamin Marlin. "An Improved Data Representation for Smoking Detection with Wearable Respiration Sensors." IEEE Wireless Health. 2016. hiatt-wh2016.pdf

Late breaking extended abstract.

Li, Steven Cheng-Xian, and Benjamin M. Marlin Classification of Sparse and Irregularly Sampled Time Series with Mixtures of Expected Gaussian Kernels and Random Features. 31st Conference on Uncertainty in Artificial Intelligence., 2015. Abstractli-uai2015.pdf

This paper presents a kernel-based framework for classification of sparse and irregularly sampled time series. The properties of such time series can result in substantial uncertainty about the values of the underlying temporal processes, while making the data difficult to deal with using standard classification methods that assume fixed-dimensional feature spaces. To address these challenges, we propose to first re-represent each time series through the Gaussian process (GP) posterior it induces under a GP regression model. We then define kernels over the space of GP posteriors and apply standard kernel-based classification. Our primary contributions are (i) the development of a kernel between GPs based on the mixture of kernels between their finite marginals, (ii) the development and analysis of extensions of random Fourier features for scaling the proposed kernel to large-scale data, and (iii) an extensive empirical analysis of both the classification performance and scalability of our proposed approach.

Saleheen, Nazir, Amin Ali, Syed Monowar Hossain, Hillol Sarker, Soujanya Chatterjee, Benjamin Marlin, Emre Ertin, Mustafa al'Absi, and Santosh Kumar puffMarker : A Multi-Sensor Approach for Pinpointing the Timing of First Lapse in Smoking Cessation. 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing., 2015. Abstractpuff-marker.pdf

Smoking is the leading cause of preventable deaths. Mobile technologies can help to deliver just-in-time-interventions to abstinent smokers and assist them in resisting urges to lapse. Doing so, however, it requires identification of high-risk situations that may lead an abstinent smoker to relapse. In this paper, we propose an explainable model for detecting smoking lapses in newly abstinent smokers using respiration and 6-axis inertial sensors worn on wrists. We propose a novel method by identifying windows of data that represent the hand at the mouth. We then develop a model to classify into puff or non-puff. On the training data, the model achieves a recall rate of 98%, for a FP rate of 1.5%. When the model is applied to the data collected from 13 abstainers, the false positive rate is 0.3/hour. Among 15 lapsers, the model is able to pinpoint the timing of first lapse in 13 participants.

Natarajan, Annamalai, Edward Gaiser, Gustavo Angarita, Robert Malison, Deepak Ganesan, and Benjamin Marlin. "Conditional Random Fields for Morphological Analysis of Wireless ECG Signals." Proceedings of the 5th Annual conference on Bioinformatics, Computational Biology and Health Informatics. Newport Beach, CA: ACM, 2014. Abstractcrf_bcb2014.pdf

Thanks to advances in mobile sensing technologies, it has recently become practical to deploy wireless electrocardiograph sensors for continuous recording of ECG signals. This capability has diverse applications in the study of human health and behavior, but to realize its full potential, new computational tools are required to effectively deal with the uncertainty that results from the noisy and highly non-stationary signals collected using these devices. In this work, we present a novel approach to the problem of extracting the morphological structure of ECG signals based on the use of dynamically structured conditional random field (CRF) models. We apply this framework to the problem of extracting morphological structure from wireless ECG sensor data collected in a lab-based study of habituated cocaine users. Our results show that the proposed CRF-based approach significantly out-performs independent prediction models using the same features, as well as a widely cited open source toolkit.