Most clinical and biomedical data contain missing values. A patient’s record may be split across multiple institutions, devices may fail, and sensors may not be worn at all times. While these missing values are often ignored, this can lead to bias and error when the data are mined. Further, the data are not simply missing at random. Instead the measurement of a variable such as blood glucose may depend on its prior values as well as that of other variables. These dependencies exist across time as well, but current methods have yet to incorporate these temporal relationships as well as multiple types of missingness. To address this, we propose an imputation method (FLk-NN) that incorporates time lagged correlations both within and across variables by combining two imputation methods, based on an extension to k-NN and the Fourier transform. This enables imputation of missing values even when all data at a time point is missing and when there are different types of missingness both within and across variables. In comparison to other approaches on three biological datasets (simulated and actual Type 1 diabetes datasets, and multi-modality neurological ICU monitoring) the proposed method has the highest imputation accuracy. This was true for up to half the data being missing and when consecutive missing values are a significant fraction of the overall time series length.

Activity recognition has many health applications, from helping individuals track meals and exercise to providing treatment reminders to people with chronic illness and improving closed-loop control of diabetes. While eating is one of the most fundamental health-related activities, it has proven difficult to recognize accurately and unobtrusively. Body-worn and environmental sensors lack the needed specificity, while acoustic and accelerometer sensors worn around the neck may be intrusive and uncomfortable. We propose a new approach to identifying eating based on head movement data from Google Glass. We develop the Glass Eating and Motion (GLEAM) dataset using sensor data collected from 38 participants conducting a series of activities including eating. We demonstrate that head movement data are sufficient to allow recognition of eating with high precision and minimal impact on privacy and comfort.

No abstract

No abstract

No abstract