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Photoplethysmography (PPG) is an optical technique used to measure the heart rate (HR) and other cardiovascular variables by analyzing volume changes in the microvascular bed of tissue. At the moment, smartphone users can already measure their HR using PPG applications that use the smartphone's built-in camera. However, available applications are unreliable when artifacts are present, such as those caused by movement, finger pressure, or ambient light changes. This contribution aims to analyze the limitations of a smartphone-based PPG algorithm capable of measuring N-N intervals when such artifacts are present by comparing it to a 2-lead electrocardiography (ECG). By using a Bandpass filter and a zero-crossing detection algorithm on a PPG signal captured at 800  ×  600 pixels and 30 Hz, we have designed an approach capable of assessing N-N intervals when movement artifacts are present. An evaluation performed on n  =  31 users shows our algorithm is capable of measuring N-N intervals with an average relative error of 9.23 ms, when compared to a 2-lead ECG. Our approach proves the reliability of smartphone-based photoplethysmography to measure N-N intervals, even under the presence of movement artifacts, and opens the door for its future use in remote diagnosis scenarios.

Input devices based on arrays of capacitive proximity sensors allow the tracking of a user's hands in three dimensions. They can be hidden behind materials such as wood, wool or plastics without limiting their functionality, making them ideal for application in Ambient Intelligence (AmI) scenarios. Most gesture recognition frameworks are targeted towards classical input devices and interpret two-dimensional data. In this work, we present a concept for adapting classical gesture recognition methods for capacitive input devices by realizing an extension of the feature set to three dimensional input data. This allows more robust gesture recognition for free-space interaction and training specific to capacitive input devices. We have implemented this concept in a prototypical setup and tested the device in various Ambient Intelligence scenarios, ranging from manipulating home appliances to controlling multimedia applications

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In the last decades the demographic change in Europe has become apparent. In Germany already 20% of the population are older than 65. This age group is particularly affected by the increasing complexity of modern public transit systems. In this paper we present the results of a user requirements elicitation of a navigation assistant for elderly people in public transit. This system shall have a targeted user experience and takes into account the personal profile of the different users, e.g. modeling mobility deficiencies that require walking aids and prevents paths that would be impassable. We have performed an exhaustive user evaluation in expert interviews and focus groups to identify suitable interface choices and in the process were able to exclude some systems that were considered obvious in initial assessments

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