Performing indoor localization with electric potential sensing

Fu, Biying; Kirchbuchner, Florian; Wilmsdorff, Julian von; Große-Puppendahl, Tobias; Braun, Andreas; Kuijper, Arjan

doi:10.1007/s12652-018-0879-z

Cited by 25 publications

(13 citation statements)

References 31 publications

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“…Remote and embedded installation for this sensing technology have been developed mainly for indoor localization purposes, such as in the works [16], [17]. In the project Platypus, Grosse-Puppendahl [17] showed that by installing four ceiling mounted electric potential sensors covering an area of 2 m x 2.5 m, they were able to track people in a nearly empty office room around 16 m 2 with a mean localization error of below 16 cm.…”

Section: ) Electrostatic Sensormentioning

confidence: 99%

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

et al. 2020

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Sensors are devices that quantify the physical aspects of the world around us. This ability is important to gain knowledge about human activities. Human Activity recognition plays an import role in people's everyday life. In order to solve many human-centered problems, such as health care, and individual assistance, the need to infer various simple to complex human activities is prominent. Therefore, having a well defined categorization of sensing technology is essential for the systematic design of human activity recognition systems. By extending the sensor categorization proposed by White, we survey the most prominent research works that utilize different sensing technologies for human activity recognition tasks. To the best of our knowledge, there is no thorough sensor-driven survey that considers all sensor categories in the domain of human activity recognition with respect to the sampled physical properties, including a detailed comparison across sensor categories. Thus, our contribution is to close this gap by providing an insight into the state-of-the-art developments. We identify the limitations with respect to the hardware and software characteristics of each sensor category and draw comparisons based on benchmark features retrieved from the research works introduced in this survey. Finally, we conclude with general remarks and provide future research directions for human activity recognition within the presented sensor categorization. INDEX TERMS Sensor categorization, human activity recognition, public databases for human activity recognition, physical sensors, sensor benchmark.

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Section: ) Electrostatic Sensormentioning

confidence: 99%

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

et al. 2020

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“…The second paper, "Performing indoor localization with electric potential sensing" by Biying Fu et al (2018), presents a system that passively and precisely localizes people when walking indoor spaces. The innovation of the work is on prototyping a token-free smart floor based on passive electric potential sensing able to achieve high spatial accuracy with low-cost materials which allows to cover large areas for relatively low material costs.…”

Section: Contribution Of Special Issuementioning

confidence: 99%

“…The third paper, "An Experimental Overview on Electric Field Sensing" by Wilmsdorff et al (2018), explores the potential of electric field sensing technology beyond Fu's work (Fu et al 2018) on indoor localisation to broader and wider use cases and domains, such as for indoor and outdoor applications. A prototype of the technology was evaluated using five interesting experiments: (1) transforming a whiteboard into an interactive touch sensor; (2) making a door sensor detect when people enter or leave a room; (3) making a street sensor distinguishing different traffic vehicles such as trucks, cars or bicycles; (4) using a gesture recognition sensor (in the line of those available in smartwatches) refining classifications of movement directions in a two-dimensional space; and (5) making a mobile (wearable) sensor worn on the body of a person to demonstrate sensing potential when a person is in motion, seating, typing on keyboard, etc.…”

Section: Contribution Of Special Issuementioning

confidence: 99%

Special issue on “Recent advances in ambient intelligence towards a smart and human-centered internet of things”

Maña

Koshutanski²

2019

J Ambient Intell Human Comput

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“…Therefore, the ambient electric field created by the radiation from the AC powerlines (ever-present in buildings) is altered by the presence of a human target. This change can be measured with Electric Potential Sensors (EPS) and used for both identification of subjects [39] as well positioning of them [40][41][42][43]. Unfortunately, such opportunistic, passive electric sensing is vulnerable to ambient electrical field noise and interference.…”

Section: Introductionmentioning

confidence: 99%

CapLoc: Capacitive Sensing Floor for Device-Free Localization and Fall Detection

et al. 2020

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Passive indoor positioning, also known as Device-Free Localization (DFL), has applications such as occupancy sensing, human-computer interaction, fall detection, and many other location-based services in smart buildings. Vision-, infrared-, wireless-based DFL solutions have been widely explored in recent years. They are characterized by respective strengths and weaknesses in terms of the desired accuracy, feasibility in various real-world scenarios, etc. Passive positioning by tracking the footsteps on the floor has been put forward as one of the promising options. This article introduces CapLoc, a floor-based DFL solution that can localize a subject in real-time using capacitive sensing. Experimental results with three individuals walking 39 paths on the CapLoc show that it can detect and localize a single target's footsteps accurately with a median localization error of 0.026 m. The potential for fall detection is also shown with the outlines of various poses of the subject lying upon the floor.

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Performing indoor localization with electric potential sensing

Cited by 25 publications

References 31 publications

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

Special issue on “Recent advances in ambient intelligence towards a smart and human-centered internet of things”

CapLoc: Capacitive Sensing Floor for Device-Free Localization and Fall Detection

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