John Trimpop

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In this paper, we describe a self adapting algorithm for smart watches to define individual transitions between motion intensities. The algorithm enables for a distinction between high-amplitude motions (e.g. walking, running, or simply moving extremities) low-amplitude motions (e.g. human microvibrations, and heart rate) as well as a general doffed-state.(More)
Since the human body is a living organism, it emits various life signs which can be traced with an action potential sensitive electromyography, but also with motion sensitive sensors such as typical inertial sensors. In this paper, we present a possibility to recognize the heart rate (HR), respiration rate (RR), and the muscular microvibrations (MV) by an(More)
In this paper we present a method to enable any smart Wearable to sense vital data in resting states. These resting states (e.g. sleeping, sitting calmly, etc.) imply the presence of low-amplitude body-motions. Our approach relies on seismocardiography (SCG), which only requires a built-in accelerometer. Compared to commonly applied technologies, such as(More)
In this paper we examine the feasibility of Human Activity Recognition (HAR) based on head mounted sensors, both as stand-alone sensors and as part of a wearable multi-sensory network. To prove the feasibility of such setting, an interactive online HAR-system has been implemented to enable for multi-sensory activity recognition while making use of a(More)
It has been shown that in various fields of social life, people tend to seek opportunities to measure their daily activities, bodily behaviors, and health related parameters. These kinds of activity tracking should be accomplished comfortably, unobtrusively and implicitly. Tracking behavior can be important for certain user groups, such as the growing(More)
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