Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors

Luo, Xiaomu; Guan, Qiuju; Tan, Huoyuan; Gao, Liwen; Wang, Zhengfei; Luo, Xiaoyan

doi:10.3390/s17081738

Cited by 55 publications

(32 citation statements)

References 41 publications

(40 reference statements)

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“…; and (3) the position of a user can be determined since the user might be blocking some ultrasound sensors [1], etc. There are previous studies [11,12,48] focusing on activity detection using ambient sensing. The systems in these cases do not have any identifying information about the person in the smart space, since: (1) the user does not carry any wearable; (2) the system does not rely on active user participation; and (3) the system does not depend on multimedia data.…”

Section: Activity Detectionmentioning

confidence: 99%

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Singh

Akşanlı

2019

JSAN

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This paper presents a framework to accurately and non-intrusively detect the number of people in an environment and track their positions. Different from most of the previous studies, our system setup uses only ambient thermal sensors with low-resolution, using no multimedia resources or wearable sensors. This preserves user privacy in the environment, and requires no active participation by the users, causing no discomfort. We first develop multiple methods to estimate the number of people in the environment. Our experiments demonstrate that algorithm selection is very important, but with careful selection, we can obtain up to 100% accuracy when detecting user presence. In addition, we prove that sensor placement plays a crucial role in the system performance, where placing the sensor on the room ceiling yields to the best results. After accurately finding the number of people in the environment, we perform position tracking on the collected ambient data, which are thermal images of the space where there are multiple people. We consider position tracking as static activity detection, where the user's position does not change while performing activities, such as sitting, standing, etc. We perform efficient pre-processing on the data, including normalization and resizing, and then feed the data into well-known machine learning methods. We tested the efficiency of our framework (including the hardware and software setup) by detecting four static activities. Our results show that we can achieved up to 97.5% accuracy when detecting these static activities, with up to 100% class-wise precision and recall rates. Our framework can be very beneficial to several applications such as health-care, surveillance, and home automation, without causing any discomfort or privacy issues for the users.

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Section: Activity Detectionmentioning

confidence: 99%

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Singh

Akşanlı

2019

JSAN

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“…Some research has also included pyroelectric infrared (PIR) sensors. Luo et al [96] used a lattice-like sensor, in order to track an agent through the environment, at the same time performing activity recognition. The study motivated the use of PIR sensors, by noting that they are relatively infrastructure-free, and are easy and cheap to deploy.…”

Section: Other Modalitiesmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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“…A simple and straight forward one would be video capture which is often frowned upon and not desirable for obvious privacy concerns. In the context of home automation, motion sensors (PIR sensors) are the most used data sources for activity tracking indoors [22]. These can also come packaged with temperature, light, humidity and other types of sensors in general [23].…”

Section: Related Workmentioning

confidence: 99%

Sleep Activity Recognition Using Binary Motion Sensors

El-Khadiri

Corona²,

Rose³

et al. 2018

2018 IEEE 30th International Conference on Tools With Artificial Intelligence (ICTAI)

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Early detection of frailty signs is important for the senior population that prefers to keep living in their homes instead of moving to a nursing home. Sleep quality is a good predictor for frailty monitoring. Thus we are interested in tracking sleep parameters like sleep wake patterns to predict and detect potential sleep disturbances of the monitored senior residents. We use an unsupervised inference method based on actigraphy data generated by ambient motion sensors scattered around the senior's apartment. This enables our monitoring solution to be flexible and robust to the different types of housings it can equip while still attaining accuracy of 0.94 for sleep period estimates.

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Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors

Cited by 55 publications

References 41 publications

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Sensor Modalities and Fusion for Robust Indoor Localisation

Sleep Activity Recognition Using Binary Motion Sensors

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