Objective: Evaluate a method for the estimation of the nocturnal systolic blood pressure (SBP) dip from 24-hour blood pressure trends using a wrist-worn photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines.Approach: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 days during which 5111 reference values for blood pressure (BP) were obtained with a 24-hour ambulatory BP monitor and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms. Long-and short term memory (LSTM) networks, dense networks, random forests and linear regression models were trained and evaluated in their capability of tracking trends in BP, as well as the estimation of the SBP dip.Main results: Best performance for estimating the SBP dip were obtained with a deep LSTM neural network with a root mean squared error (RMSE) of 3.12±2.20 ∆mmHg and a correlation of 0.69 (p = 3 * 10 −5 ). This dip was derived from trend estimates of BP which had an RMSE of 8.22±1.49 mmHg for systolic and 6.55±1.39 mmHg for diastolic BP (DBP). While other models had similar performance for the tracking of relative BP, they did not perform as well as the LSTM for the SBP dip.Significance: The work provides first evidence for the unobtrusive estimation of the nocturnal SBP dip, a highly prognostic clinical parameter. It is also the first to evaluate unobtrusive BP measurement in a large data set of unconstrained 24hour measurements in free-living individuals and provides evidence for the utility of LSTM models in this domain.

Abstract. Biometric information is regarded as highly sensitive information and therefore encryption techniques for biometric information are needed to address security and privacy requirements of biometric information. Most security analyses for these encryption techniques focus on the scenario of one user enrolled in a single biometric system. In practice, biometric systems are deployed at different places and the scenario of one user enrolled in many biometric systems is closer to reality. In this scenario, cross-matching (tracking users enrolled in multiple databases) becomes an important privacy threat. To prevent such cross-matching, various methods to create renewable and indistinguishable biometric references have been published. In this paper, we investigate the indistinguishability or the protection against cross-matching of a continuous-domain biometric cryptosystem, the QIM. In particular our contributions are as follows. Firstly, we present a technique, which allows an adversary to decide whether two protected biometric reference data come from the same person or not. Secondly, we quantify the probability of success of an adversary who plays the indistinguishability game and thirdly, we compare the probability of success of an adversary to the authentication performance of the biometric system for the MCYT fingerprint database. The results indicate that although biometric cryptosystems represent a step in the direction of privacy enhancement, we are not there yet.

In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples.

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Abstract-In the health self-management services it is beneficial to identify and address the already existing healthy activity patterns of the user. Some of these healthy activity patterns might be of a utilitarian nature, e.g. commuting to work by bike or on foot, or might be for leisure, like taking a walk in a park. In this paper we discuss one possibility to detect the utilitarian or leisure nature of a particular ambulatory path based on the geometry of the trajectory. In essence, a leisure trip is more commonly a round-trip while an utilitarian A-to-B trips follow the single shortest path between A and B. We define a generic measure for the characterization of utilitarian and leisure paths based on GPS location data and develop an algorithm for approaching the same based on only magnetometer data from a wearable device.

In the health self-management services it is beneficial to identify and address the already existing healthy activity patterns of the user. Some of these healthy activity patterns might be of a utilitarian nature, e.g. commuting to work by bike or on foot, or might be for leisure, like taking a walk in a park. In the paper we discuss one possibility to detect the utilitarian or leisure nature of a particular ambulatory path based on the geometry of the trajectory. In essence, a leisure trip is more commonly a round-trip while an utilitarian A-to-B trips follow the single shortest path between A and B. We define a generic measure for the characterization of utilitarian and leisure paths based on GPS location data and develop an algorithm for approaching the same based on only magnetometric data from a wearable device.

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