Indoor Trajectory Reconstruction Using Mobile Devices

Susanti, Risca Mukti; Adhinugraha, Kiki Maulana; Alamri, Sultan; Barolli, Leonard; Taniar, David

doi:10.1109/aina.2018.00086

Cited by 5 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, other works focus on building a data structure for spatial objects to satisfy the variety of indoor queries (Alamri et al, 2020). The most popular spatial data structures are Euclidean space, spatial road network, and cellular space (Susanti et al, 2018). Cellular space refers to indoor spaces that contain space-related objects.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Indoor Framework

Alamri

2022

International Journal of Web Services Research

Self Cite

View full text Add to dashboard Cite

The tracking of spatial objects in indoor location-based services is becoming increasingly important for many applications. However, much research has focused only on querying and indexing in indoor spaces without considering the indoor variations. Therefore, this paper presents an indoor framework which includes data structures of indoor environments comprised of various building features and multiple floors. Moreover, the indoor framework includes indoor navigation and routing for both directed and undirected indoor environments, indoor density which takes into account the room capacity, and movement trajectories in single and multi-floor structures. Using synthetic data, the authors conducted extensive experiments to evaluate the proposed framework. The results show that this indoor framework can be implemented efficiently and effectively.

show abstract

Section: Related Work and Backgroundmentioning

confidence: 99%

Indoor Framework

Alamri

2022

International Journal of Web Services Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…RSSI (Received Signal Strength Indication) is the most widely used localization metric, with no hardware-specific requirements. In the indoor controlled environment, previous works attempt to localize users through RSSI-based distance measurements [6], [7]. On the other side, in outdoor scenarios, there has been an attempt to use machine learning techniques to lower the distance-estimation error [8].…”

Section: Introductionmentioning

confidence: 99%

Do WiFi Probe-Requests Reveal Your Trajectory?

Mishra

Viana

Achir

2023

2023 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

In this paper, we propose the first framework that introduces the concept of the user's bounded trajectory. We propose to leverage the signal strength of users' public WiFi probe requests collected from measurements of multiple deployed WiFi sniffers. First, we investigate and characterize errors in RSSI-based radial-distance (between the user and each sniffer) estimation. Then, we approximate such radial distances leverage and deduce bounds associated with a user's position. Finally, we infer a user's bounded trajectory using the spatiotemporal bounds of users' locations over time. We guarantee the bounds to enclose a user in space and time, with 95% confidence and a 10% margin of error. Using real-world and large-scale synthetic datasets under heterogeneous contexts and wireless conditions, we infer trajectories with bounds' width of less than 10m in 70% of cases with users' inclusiveness close to 100%.

show abstract

“…Indoor positioning systems (IPS) enable the provision of several location-based services such as home monitoring, rehabilitation, navigation for blind and visual impaired people, and finding and rescuing people/firefighters in emergencies. IPSs can be divided into two approaches: infrastructure-based and infrastructure-free [1,2]. Infrastructure-based IPS require the deployment of devices in the indoor environment to calculate the position of the person.…”

Section: Introductionmentioning

confidence: 99%

“…Infrastructure-free IPS do not need the deployment of devices and mainly use dead-reckoning algorithms. Those systems are called inertial pedestrian dead-reckoning (IPDR) because they use body movement information measured by inertial measurement units (IMU) to estimate a person's position changes based on a previously estimated or known position [2]. The sum of these changes of position allows the reconstruction of the person's trajectory [2].…”

Section: Introductionmentioning

confidence: 99%

Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System

Ceron

Martindale

López

et al. 2020

Sensors

View full text Add to dashboard Cite

The evaluation of trajectory reconstruction of the human body obtained by foot-mounted Inertial Pedestrian Dead-Reckoning (IPDR) methods has usually been carried out in controlled environments, with very few participants and limited to walking. In this study, a pipeline for trajectory reconstruction using a foot-mounted IPDR system is proposed and evaluated in two large datasets containing activities that involve walking, jogging, and running, as well as movements such as side and backward strides, sitting, and standing. First, stride segmentation is addressed using a multi-subsequence Dynamic Time Warping method. Then, detection of Toe-Off and Mid-Stance is performed by using two new algorithms. Finally, stride length and orientation estimation are performed using a Zero Velocity Update algorithm empowered by a complementary Kalman filter. As a result, the Toe-Off detection algorithm reached an F-score between 90% and 100% for activities that do not involve stopping, and between 71% and 78% otherwise. Resulting return position errors were in the range of 0.5% to 8.8% for non-stopping activities and 8.8% to 27.4% otherwise. The proposed pipeline is able to reconstruct indoor trajectories of people performing activities that involve walking, jogging, running, side and backward walking, sitting, and standing.

show abstract

Indoor Trajectory Reconstruction Using Mobile Devices

Cited by 5 publications

References 23 publications

Indoor Framework

Indoor Framework

Do WiFi Probe-Requests Reveal Your Trajectory?

Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System

Contact Info

Product

Resources

About