Device-Free Passive Human Counting with Bluetooth Low Energy Beacons

Münch, Maximilian; Schleif, Frank-Michael

doi:10.1007/978-3-030-20518-8_66

Cited by 9 publications

(18 citation statements)

References 24 publications

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“…The Presence Detection (PD) data set is taken from [14] and deals with device-free presence detection via Bluetooth Low Energy sensors in lecture halls. The data are given as 83 time series in 96 dimensions in 4 classes.…”

Section: Methodsmentioning

confidence: 99%

Multi-perspective embedding for non-metric time series classification

Münch¹,

Heilig²,

Schleif³

2021

ESANN 2021 Proceedings

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The interest in time series analysis is rapidly increasing, providing new challenges for machine learning. Over many decades, Dynamic Time Warping (DTW) is referred to as the de facto standard distance measure for time series and the tool of choice when analyzing such data. Nevertheless, DTW has two major drawbacks: (a) it is non-metric and therefore hard to handle by standard machine learning techniques, and (b) it is not well suited for multi-dimensional time series. For this purpose, we propose a multi-perspective embedding of the time series into a complex-valued vector space and the evaluation by a model that is able to handle complex-valued data. The approach is evaluated on various multi-dimensional time series data and with different classifier techniques.

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Section: Methodsmentioning

confidence: 99%

Multi-perspective embedding for non-metric time series classification

Münch¹,

Heilig²,

Schleif³

2021

ESANN 2021 Proceedings

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“…In machine learning, information is now often spread across different heterogeneous formats and classical techniques are insufficient [17,12]. Frequently, deep learning and embedding techniques can be employed to generate vectorial representations, but they require huge amounts of training data, dedicated deep learning models and have extensive computational costs [17].…”

Section: Learning From Multiple Indefinite Kernel Functionsmentioning

confidence: 99%

“…The FlowCyto data set 2 is based on 612 FL3-A DNA flow cytometer histograms from breast cancer tissues in 256 resolution, divided into two classes for our binary classification setup. 3 The Presence Detection (PD) data set 2 analyzes the occupancy of lecture halls based on multiple wireless Bluetooth Low Energy signals [12]. The Tox21 challenge 4 was the computational analysis about toxic effects of substances on body regions such as stress response (SR) or effects on nuclear receptors (NR).…”

Section: Datasetsmentioning

confidence: 99%

Adaptive multi-modal positive semi-definite and indefinite kernel fusion for binary classification

Münch¹,

Raab²,

Heilig³

et al. 2022

ESANN 2022 Proceedings

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Data and information are nowadays frequently available in multiple modalities like different sensor signals, textual descriptions, graph structures, and other formats. The maximum information from these heterogeneous representations can be obtained by fusing the various modalities by specific embeddings or proximity measures. Current approaches are widely limited in the fusion model and the applied measures, especially when the given data is non-vectorial. We propose a model to learn the spectral properties of the different inner product representations in a joined optimization problem. The approach is evaluated on various multimodal data and compared to modern multiple-kernel learning and baseline techniques. * MM and MR are supported by the Bavarian HighTech agenda and the Würzburg Center for Artificial Intelligence and Robotics (CAIRO). Additionally, we thank Dr. Benjamin Paaßen for the invaluable discussions about this research topic during a fantastic boating trip.

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“…Alternatively, Münch et al. introduced an approach that wields Bluetooth low‐energy (BLE) beacons to passively count person numbers in a room [18]. They also used the RSSI information collected via BLE beacons that are affected by a human body, which is a similar concept to the RFID system.…”

Section: Related Workmentioning

confidence: 99%

“…The system uses RFID antennas to read RSSI information reflected by many RFID tags sticking to walls in a room to detect and localise a person in the room who may block and reflect some signals between the antennas and tags. Alternatively, Münch et al introduced an approach that wields Bluetooth low-energy (BLE) beacons to passively count person numbers in a room [18]. They also used the RSSI information collected via BLE beacons that are affected by a human body, which is a similar concept to the RFID system.…”

Section: Related Workmentioning

confidence: 99%

DFC: Device‐free human counting through WiFi fine‐grained subcarrier information

et al. 2021

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A device‐free human counting (DFC) algorithm that uses fine‐grained subcarrier information from WiFi devices, called channel state information (CSI), to count the number of people in indoor environments is proposed. The DFC algorithm extracts the features of average attenuation and variation of CSI amplitudes caused by human motions, and puts the features into a training process to improve the counting accuracy. Through a bootstrapping process, the DFC can estimate the number of people standing in the middle of a WiFi link by constructing a probability model with the CSI signals at a receiver side. With this human counting capability, the DFC can support the efficient monitoring and automatic control of electrical devices (e.g. air conditioner, heater, bulb, and beam projector) indoors. Through a real implementation and experiments, it is shown that the DFC algorithm outperforms the state‐of‐the‐art DFC algorithm based on RSSI in indoor environments with human mobility. For a dynamic‐target case in a meeting room, for example, DFC can predict the number of people in an indoor space with an accuracy about 98% at best.

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Device-Free Passive Human Counting with Bluetooth Low Energy Beacons

Cited by 9 publications

References 24 publications

Multi-perspective embedding for non-metric time series classification

Multi-perspective embedding for non-metric time series classification

Adaptive multi-modal positive semi-definite and indefinite kernel fusion for binary classification

DFC: Device‐free human counting through WiFi fine‐grained subcarrier information

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