Impact of remote biometric monitoring on cost and hospitalization outcomes in peritoneal dialysis.

INTRODUCTION: Peritoneal dialysis is a home-based therapy for individuals with end-stage renal disease. Telehealth, and in particular - remote monitoring, is making inroads in managing this cohort. METHODS: We examined whether daily remote biometric monitoring (RBM) of blood pressure and weight among peritoneal dialysis patients was associated with changes in hospitalization rate and hospital length of stay, as well as outpatient, inpatient and overall cost of care. RESULTS: Outpatient visit claim payment amounts (in US dollars derived from CMS data) decreased post-intervention relative to pre-intervention for those at age 18-54 years. For certain subgroups, non- or nearly-significant changes were found among female and Black participants. There was no change in inpatient costs post-intervention relative to pre-intervention for females and while the overall visit claim payment amounts increased in the outpatient setting slightly (US$511.41 (1990.30) vs. US$652.61 (2319.02), p = 0.0783) and decreased in the inpatient setting (US$10,835.30 (6488.66) vs. US$10,678.88 (15,308.17), p = 0.4588), these differences were not statistically significant. Overall cost was lower if RBM was used for assessment of blood pressure and/or weight (US$-734.51, p < 0.05). Use of RBM collected weight was associated with fewer hospitalizations (adjusted odds ratio 0.54, 95% confidence interval 0.33-0.89) and fewer days hospitalized (adjusted odds ratio 0.46, 95% confidence interval 0.26-0.81). Use of RBM collected blood pressure was associated with increased days of hospitalization and increased odds of hospitalization. CONCLUSIONS: RBM offers a powerful opportunity to provide care to those receiving home therapies such as peritoneal dialysis. RBM may be associated with reduction in both inpatient and outpatient costs for specific sub-groups receiving peritoneal dialysis.

Perspectives of human verification via binary QRS template matching of single-lead and 12-lead electrocardiogram.

OBJECTIVE: This study aims to validate the 12-lead electrocardiogram (ECG) as a biometric modality based on two straightforward binary QRS template matching characteristics. Different perspectives of the human verification problem are considered, regarding the optimal lead selection and stability over sample size, gender, age, heart rate (HR). METHODS: A clinical 12-lead resting ECG database, including a population of 460 subjects with two-session recordings (>1 year apart) is used. Cost-effective strategies for extraction of personalized QRS patterns (100ms) and binary template matching estimate similarity in the time scale (matching time) and dissimilarity in the amplitude scale (mismatch area). The two-class person verification task, taking the decision to validate or to reject the subject identity is managed by linear discriminant analysis (LDA). Non-redundant LDA models for different lead configurations (I,II,III,aVF,aVL,aVF,V1-V6) are trained on the first half of 230 subjects by stepwise feature selection until maximization of the area under the receiver operating characteristic curve (ROC AUC). The operating point on the training ROC at equal error rate (EER) is tested on the independent dataset (second half of 230 subjects) to report unbiased validation of test-ROC AUC and true verification rate (TVR = 100-EER). The test results are further evaluated in groups by sample size, gender, age, HR. RESULTS AND DISCUSSION: The optimal QRS pattern projection for single-lead ECG biometric modality is found in the frontal plane sector (60°-0°) with best (Test-AUC/TVR) for lead II (0.941/86.8%) and slight accuracy drop for -aVR (-0.017/-1.4%), I (-0.01/-1.5%). Chest ECG leads have degrading accuracy from V1 (0.885/80.6%) to V6 (0.799/71.8%). The multi-lead ECG improves verification: 6-chest (0.97/90.9%), 6-limb (0.986/94.3%), 12-leads (0.995/97.5%). The QRS pattern matching model shows stable performance for verification of 10 to 230 individuals; insignificant degradation of TVR in women by (1.2-3.6%), adults ≥70 years (3.7%), younger <40 years (1.9%), HR<60bpm (1.2%), HR>90bpm (3.9%), no degradation for HR change (0 to >20bpm).

Active Self-Paced Learning for Cost-Effective and Progressive Face Identification.

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://hcp.sysu.edu.cn/projects/aspl/.

A biometric authentication model using hand gesture images.

A novel hand biometric authentication method based on measurements of the user's stationary hand gesture of hand sign language is proposed. The measurement of hand gestures could be sequentially acquired by a low-cost video camera. There could possibly be another level of contextual information, associated with these hand signs to be used in biometric authentication. As an analogue, instead of typing a password 'iloveu' in text which is relatively vulnerable over a communication network, a signer can encode a biometric password using a sequence of hand signs, 'i' , 'l' , 'o' , 'v' , 'e' , and 'u'. Subsequently the features from the hand gesture images are extracted which are integrally fuzzy in nature, to be recognized by a classification model for telling if this signer is who he claimed himself to be, by examining over his hand shape and the postures in doing those signs. It is believed that everybody has certain slight but unique behavioral characteristics in sign language, so are the different hand shape compositions. Simple and efficient image processing algorithms are used in hand sign recognition, including intensity profiling, color histogram and dimensionality analysis, coupled with several popular machine learning algorithms. Computer simulation is conducted for investigating the efficacy of this novel biometric authentication model which shows up to 93.75% recognition accuracy.

Playful biometrics: controversial technology through the lens of play.

This article considers the role of play in the context of technological emergence and expansion, particularly as it relates to recently emerging surveillance technologies. As a case study, I consider the trajectory of automated face recognition­a biometric technology of numerous applications, from its more controversial manifestations under the rubric of national security to a clearly emerging orientation toward play. This shift toward "playful" biometrics­or from a technology traditionally coded as "hard" to one now increasingly coded as "soft"­is critical insofar as it renders problematic the traditional modes of critique that have, up until this point, challenged the expansion of biometric systems into increasingly ubiquitous realms of everyday life. In response to this dynamic, I propose theorizing the expansion of face recognition specifically in relation to "play," a step that allows us to broaden the critical space around newly emerging playful biometrics, as well as playful surveillance more generally. In addition, play may also have relevance for theorizing other forms of controversial technology, particularly given its potential role in processes of obfuscation, normalization, and marginalization.