Monitoring Human Movement in Building Using Bluetooth Low Energy

Hoa, Jeffrey; Soewito, Benfano

doi:10.21512/commit.v12i2.4963

Cited by 5 publications

(11 citation statements)

References 14 publications

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“…For example, the U-wear network platform is used as a more secure communication system than radiofrequency [ 60 ]. Bluetooth low energy is used to obtain the coordinates of object locations using a BLE beacon inside the building [ 61 , 62 ]. For example, Apple’s iBeacons provide location-based information and services to iPhones and iOS devices [ 63 ].…”

Section: Resultsmentioning

confidence: 99%

An Overview of Sensors, Design and Healthcare Challenges in Smart Homes: Future Design Questions

Nthubu

2021

Healthcare

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The ageing population increases the demand for customized home care. As a result, sensing technologies are finding their way into the home environment. However, challenges associated with how users interact with sensors and data are not well-researched, particularly from a design perspective. This review explores the literature on important research projects around sensors, design and smart healthcare in smart homes, and highlights challenges for design research. A PRISMA protocol-based screening procedure is adopted to identify relevant articles (n = 180) on the subject of sensors, design and smart healthcare. The exploration and analysis of papers are performed using hierarchical charts, force-directed layouts and ‘bedraggled daisy’ Venn diagrams. The results show that much work has been carried out in developing sensors for smart home care. Less attention is focused on addressing challenges posed by sensors in homes, such as data accessibility, privacy, comfort, security and accuracy, and how design research might solve these challenges. This review raises key design research questions, particularly in working with sensors in smart home environments.

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Section: Resultsmentioning

confidence: 99%

An Overview of Sensors, Design and Healthcare Challenges in Smart Homes: Future Design Questions

Nthubu

2021

Healthcare

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“…13 No. 3 August 2021 https://doi.org/10.20895/infotel.v13i3.600 which located on (x = 8, y = 0), and AP3 which located on (x = 13, y = 6). The access point as transmitter transmits a Received Signal Strength Indicator (RSSI) and captured by the smartphone as receiver.…”

Section: A Location and Equipment Setupmentioning

confidence: 99%

“…The design of the user interface in LabVIEW is based on the floor plan in Fig. 1 and trilateration techniqur in (3). There are two main programs, firstly is to determine the user's position, and secondly is to navigate the user to the desired room.…”

Section: E Programming In Labviewmentioning

confidence: 99%

“…Especially if we are in a public building that has many rooms and space also has minimal guidance, such as Museum [2], Hospital or Mall. Indoor positioning also serves to monitor human movement inside the building [3]. It can track the whereabouts of Alzheimer's patients linked with application in the smartphone to notify the caregiver [4].…”

Section: Introductionmentioning

confidence: 99%

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Improved RSSI-based path-loss model for indoor positioning and navigation in LabVIEW using trilateration

Farahiyah

Reza

2021

J.INFOTEL

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Indoor positioning and navigation now contribute in many applications to track and direct people inside the building. The popular trilateration technique is utilized to detect user’s position through three access point of Bluetooth low energy. However, received signal from Bluetooth has insignificancy due to the noise, multipath, fading or other radio propagation. A study of received signal characteristics in specific indoor locations must be considered to predict and improve the accuracy of estimation. In this case, the adjustment of raw received signal readings is essential. we extracted linear regression model by compare between raw and analytical value of received signal power. Then, utilizing the corrected received signal, finding the best suitable path loss exponent model is required in order to minimize position estimation error. The last step is applying the additional model and the chosen path-loss on LabVIEW as a mean to visualize position and navigation system. The result yield that the new model gives lower error on 2 out of 3 access points. The corresponding path loss exponent n = 2.1 is selected to comply with the indoor environment in this case. The lowest RMSE yields 1.24 and considered as a good level of accuracy. The Navigation system worked well providing route to the desired location in the Laboratory.

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“…Within the past ten years, the researchers published IPS topics, or more generally stated the indoor localization, especially on the technology perspective (Xiao et al, 2016); i.e., radio-based, vision, optical-based, and methods techniques such as range-based and range-free (Palipana et al, 2017;Rahman et al, 2020). Some radio technologies that very popular, including Wi-Fi, Bluetooth low energy (BLE) (Hoa & Soewito, 2018), RFID (L. Yang et al, 2015), UWB (Zwirello et al, 2012), ZigBee (Chuenurajit et al, 2012), and many others, have been proven as indoor localization system. It shows that the proposed technology and methods can cover some applications.…”

Section: Introductionmentioning

confidence: 99%

Random Forest-based Fingerprinting Technique for Device-free Indoor Localization System

Suroso

Rupaksi²,

Krisnawan

et al. 2021

IJoCED

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The device-free indoor localization (DFIL) research is gaining attention due to the popularity of location-based service (LBS)-based advertisement. In DFIL, a user or an object does not need to bring any device to be localized. In this paper, we propose the Wi-Fi-based DFIL and the random forest algorithm for the fingerprint-based technique. The simple parameter commonly used in indoor localization is the Received Signal Strength Indicator (RSSI). We apply the fingerprint technique because of its reliability to handle the RSSI fluctuation and time-varying effect in a static indoor environment. We conducted an actual measurement campaign to observe the DFIL's implementation visibility. The DFIL system works by comparing the database fingerprint in an empty open office with the database in which a person is inside the measurement area without bringing any devices. Thus, we have the device-free RSSI database for fingerprint technique from both empty rooms and RSSI affected by a person inside the room. We validated the random forest algorithm results by comparing them with the k-nearest neighbor (kNN) and artificial neural network (ANN). The results show that our proposed system's accuracy is better than kNN and ANN with a mean error of 0.63 m than kNN with 0.80 m and ANN with 1.01 m. Meanwhile, the precision of the random forest is 0.63 m, whereas kNN and ANN are 0.67 m and 0.80 m, showing that the random forest performed better. We concluded that our simple DFIL system is visible to apply with acceptable accuracy performance.

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Monitoring Human Movement in Building Using Bluetooth Low Energy

Cited by 5 publications

References 14 publications

An Overview of Sensors, Design and Healthcare Challenges in Smart Homes: Future Design Questions

An Overview of Sensors, Design and Healthcare Challenges in Smart Homes: Future Design Questions

Improved RSSI-based path-loss model for indoor positioning and navigation in LabVIEW using trilateration

Random Forest-based Fingerprinting Technique for Device-free Indoor Localization System

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