FLoc: Device-free passive indoor localization in complex environments

Chen, Wenqiang; Guan, Maoning; Wang, Lu; Ruby, Rukhsana; Wu, Kaishun

doi:10.1109/icc.2017.7997098

Cited by 28 publications

(3 citation statements)

References 10 publications

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“…10. Everyday objects tested in the evaluation: (0) camera (1) small Whisky ( 2) perfume ( 3) umbrella ( 4) Coca-Cola ( 5) sunglasses ( 6) a package of tissue (7) insect spray (8) water bottle (9) toy ball (10) sunscreen spray (11) mobile phone (12) glass cup (13) yellow duck ( 14) dumbbell ( 15) microphone ( 16) Bulbasaur ( 17) skipping rope ( 18) tapeline ( 19) baseball player statue.…”

Section: Study 1: Pre-trained Modelmentioning

confidence: 99%

ViObject

Chen,

Lin,

Peng

et al. 2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Knowing the object grabbed by a hand can offer essential contextual information for interaction between the human and the physical world. This paper presents a novel system, ViObject, for passive object recognition that uses accelerometer and gyroscope sensor data from commodity smartwatches to identify untagged everyday objects. The system relies on the vibrations caused by grabbing objects and does not require additional hardware or human effort. ViObject's ability to recognize objects passively can have important implications for a wide range of applications, from smart home automation to healthcare and assistive technologies. In this paper, we present the design and implementation of ViObject, to address challenges such as motion interference, different object-touching positions, different grasp speeds/pressure, and model customization to new users and new objects. We evaluate the system's performance using a dataset of 20 objects from 20 participants and show that ViObject achieves an average accuracy of 86.4%. We also customize models for new users and new objects, achieving an average accuracy of 90.1%. Overall, ViObject demonstrates a novel technology concept of passive object recognition using commodity smartwatches and opens up new avenues for research and innovation in this area.

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Section: Study 1: Pre-trained Modelmentioning

confidence: 99%

ViObject

Chen,

Lin,

Peng

et al. 2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…At present, the modeling methods for indoor complex environments mainly include geometric modeling, topology modeling, and grid modeling [10][11][12][13]. This paper mainly investigates and analyzes the time-varying extended applications of these methods.…”

Section: Related Workmentioning

confidence: 99%

A Digital Grid Model for Complex Time-Varying Environments in Civil Engineering Buildings

Zhang

2023

Remote Sensing

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The indoor environment is typically a complex time-varying environment. At present, the problem of indoor modeling is still a hot research topic for scholars at home and abroad. This paper primarily studies indoor time-varying space. On the basis of the Beidou grid framework and time coding model, in the first scenario, a local space subdivision framework based on Beidou is proposed. The necessity of local space subdivision framework is analyzed. In the second scenario, based on the time coding model needle, a local temporal subdivision model, more suitable for a short time domain, is proposed. Then, for the spatial modeling of an indoor time-varying environment, an indoor time-varying mesh frame based on global subdivision, local space subdivision, and local time subdivision is proposed. Using this framework, the indoor environment is represented by the space–time grid, and the basic storage data structure is designed. Finally, the experiment of local subdivision coding in the indoor space–time grid, indoor space–time grid modeling, and an organization experiment is carried out using real data and simulation data. The experimental results verify the feasibility and correctness of the encoding and decoding algorithm of local subdivision encoding in space–time encoding and the calculation algorithm of the space–time relationship. The experimental results also verify the multi-space organization and the management ability of the indoor space–time grid model.

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“…These methods often rely on dense deployment or particular sensing requirements (e.g., line-of-sight). Researchers have been explored an alternative sensing modality through footstepinduced floor vibrations [5,12,13,16,18,19]. They demonstrate sub-meter level localization when a single person walks through the sensing area, or when at most two people walks in a small scope without considering the situation vibration signals overlap severely [16].…”

Section: Introductionmentioning

confidence: 99%

Device-free Multiple People Localization through Floor Vibration

Shi

Mirshekari

Fagert

et al. 2019

Proceedings of the 1st ACM International Workshop on Device-Free Human Sensing

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Structural vibration-based human sensing provides an alternative approach for device-free human monitoring, which is used for healthcare, space and energy usage management, etc. Prior work on this approach mainly focused on one person walking scenarios, which limits their widespread application. The challenge with multiple walkers is that the observed vibration response is a mixture of each walker's footstep-induced response, and it is difficult to identify 1) how many concurrent walkers are present, and 2) the timing of their footstep impacts on the floor. As a result, the extraction of detailed location information for each walker is erroneous. To address this challenge, we propose a structure-informed vibration signal characterization method to enable the detection and localization of overlapping vibration signals induced by multiple concurrent walkers. The intuition is that, due to the randomness in people's behavior, their footsteps do not impact the floor exactly at the same time and overlap partially. We decompose the signal to a non-fundamental frequency band which contains the heel strike onset information. With this decomposed signal, we can identify the number of walkers and use the initial peak information to localize each person independently. We conducted real-world experiments with up to three concurrent walkers and our system achieved a detection rate of up to 90% and an average localization error of 0.65m (2.9X baseline improvement). CCS CONCEPTS • Human-centered computing → Ubiquitous and mobile computing.

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FLoc: Device-free passive indoor localization in complex environments

Cited by 28 publications

References 10 publications

ViObject

ViObject

A Digital Grid Model for Complex Time-Varying Environments in Civil Engineering Buildings

Device-free Multiple People Localization through Floor Vibration

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