Behavior Modeling for a Beacon-Based Indoor Location System

Bilbao-Jayo, Aritz; Almeida, Aitor; Sergi, Ilaria; Montanaro, Teodoro; Fasano, Luca; Emaldi, Mikel; Patrono, Luigi

doi:10.3390/s21144839

Cited by 20 publications

(11 citation statements)

References 42 publications

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“…To determine the distance between the Smartwatch from the Bluetooth Beacons installed in a room using the strength of the RSSI signal the following relation was used [ 46 ]:

where

is established by the device manufacturer and set during its configuration and

is the fluctuation of the power of the received signal directly affecting the value of the calculated distance.…”

Section: Evaluation Resultsmentioning

confidence: 99%

Identifying and Monitoring the Daily Routine of Seniors Living at Home

Chifu

Pop

Demjen

et al. 2022

Sensors

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As the population in the Western world is rapidly aging, the remote monitoring solutions integrated into the living environment of seniors have the potential to reduce the care burden helping them to self-manage problems associated with old age. The daily routine is considered a useful tool for addressing age-related problems having additional benefits for seniors like reduced stress and anxiety, increased feeling of safety and security. In this paper, we propose a solution for identifying the daily routines of seniors using the monitored activities of daily living and for inferring deviations from the routines that may require caregivers’ interventions. A Markov model-based method is defined to identify the daily routines, while entropy rate and cosine functions are used to measure and assess the similarity between the daily monitored activities in a day and the inferred routine. A distributed monitoring system was developed that uses Beacons and trilateration techniques for monitoring the activities of older adults. The results are promising, the proposed techniques can identify the daily routines with confidence concerning the activity duration of 0.98 and the sequence of activities in the interval of [0.0794, 0.0829]. Regarding deviation identification, our method obtains 0.88 as the best sensitivity value with an average precision of 0.95.

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“…To determine the distance between the Smartwatch from the Bluetooth Beacons installed in a room using the strength of the RSSI signal the following relation was used [ 46 ]:

where

is established by the device manufacturer and set during its configuration and

is the fluctuation of the power of the received signal directly affecting the value of the calculated distance.…”

Section: Evaluation Resultsmentioning

confidence: 99%

Identifying and Monitoring the Daily Routine of Seniors Living at Home

Chifu

Pop

Demjen

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…As already discussed in one of our previous works [39] and as shown in Figure 3, BLE Beacon technology usually leverages on two main components: the BLE Beacons and one or more monitoring devices, typically, smartphones or smartwatches. Therefore, normally, some beacons are installed in strategic well-mapped points of the monitored area.…”

Section: Positioning Algorithms With the Beacon Ble Technologymentioning

confidence: 99%

“…where the calibratedRSSI parameter is the RSSI of the Beacon measured at 1 m distance, RSSI is instead the currently measured RSSI and pathLossParameter is the path-loss adjustment parameter. More details about such parameters and how it is calculated can be found in our previous works [39].…”

Section: Positioning Algorithms With the Beacon Ble Technologymentioning

confidence: 99%

An IoT-Aware Smart System Exploiting the Electromagnetic Behavior of UHF-RFID Tags to Improve Worker Safety in Outdoor Environments

et al. 2022

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Recently, different solutions leveraging Internet of Things (IoT) technologies have been adopted to avoid accidents in agricultural working environments. As an example, heavy vehicles, e.g., tractors or excavators, have been upgraded with remote controls. Nonetheless, the community continues to encourage discussions on safety issues. In this framework, a localization system installed on remote-controlled farm machines (RCFM) can help in preventing fatal accidents and reduce collision risks. This paper presents an innovative system that exploits passive UHF-RFID technology supported by commercial BLE Beacons for monitoring and preventing accidents that may occur when ground-workers in RCFM collaborate in outdoor agricultural working areas. To this aim, a modular architecture is proposed to locate workers, obstacles and machines and guarantees the security of RCFM movements by using specific notifications for ground-workers prompt interventions. Its main characteristics are presented with its main positioning features based on passive UHF-RFID technology. An experimental campaign discusses its performance and determines the best configuration of the UHF-RFID tags installed on workers and obstacles. Finally, system validation demonstrates the reliability of the main components and the usefulness of the proposed architecture for worker safety.

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“…Nonetheless, most of these duties base their functionalities on the monitoring of both the environment and the users themselves and sometimes fail simply because they cannot interpret the next actions of the user (e.g., a system could suggest to avoid a road that seems to be full but it is exactly the destination of all the cars that are occupying and so it could cause a delay that would have been avoided by predicting the destination of the occupants). These reasons motivate the research conducted to predict users' behavior and recognize their current and future activities [8][9][10][11][12]. As demonstrated by the various works presented in the literature, thanks to such predictions it is possible to enhance a multitude of existing services, such as improving logistics in different domains [13][14][15], reimagine and dynamically repurpose built environment or customize services [16,17], prevent and manage dangerous situations [18][19][20][21][22], but also their provision.…”

Section: Related Workmentioning

confidence: 99%

Location Based Indoor and Outdoor Lightweight Activity Recognition System

et al. 2022

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In intelligent environments one of the most relevant information that can be gathered about users is their location. Their position can be easily captured without the need for a large infrastructure through devices such as smartphones or smartwatches that we easily carry around in our daily life, providing new opportunities and services in the field of pervasive computing and sensing. Location data can be very useful to infer additional information in some cases such as elderly or sick care, where inferring additional information such as the activities or types of activities they perform can provide daily indicators about their behavior and habits. To do so, we present a system able to infer user activities in indoor and outdoor environments using Global Positioning System (GPS) data together with open data sources such as OpenStreetMaps (OSM) to analyse the user’s daily activities, requiring a minimal infrastructure.

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Behavior Modeling for a Beacon-Based Indoor Location System

Cited by 20 publications

References 42 publications

Identifying and Monitoring the Daily Routine of Seniors Living at Home

Identifying and Monitoring the Daily Routine of Seniors Living at Home

An IoT-Aware Smart System Exploiting the Electromagnetic Behavior of UHF-RFID Tags to Improve Worker Safety in Outdoor Environments

Location Based Indoor and Outdoor Lightweight Activity Recognition System

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