Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments

A reliable Indoor Positioning System (IPS) is a crucial part of the Ambient-Assisted Living (AAL) concept. The use of Wi-Fi fingerprinting techniques to determine the location of the user, based on the Received Signal Strength Indication (RSSI) mapping, avoids the need to deploy a dedicated positioning infrastructure but comes with its own issues. Heterogeneity of devices and RSSI variability in space and time due to environment changing conditions pose a challenge to positioning systems based on this technique. The primary purpose of this research is to examine the viability of leveraging other sensors in aiding the positioning system to provide more accurate predictions. In particular, the experiments presented in this work show that Inertial Motion Units (IMU), which are present by default in smart devices such as smartphones or smartwatches, can increase the performance of Indoor Positioning Systems in AAL environments. Furthermore, this paper assesses a set of techniques to predict the future performance of the positioning system based on the training data, as well as complementary strategies such as data scaling and the use of consecutive Wi-Fi scanning to further improve the reliability of the IPS predictions. This research shows that a robust positioning estimation can be derived from such strategies.

Download Full-text

An Indoor Positioning System Based on Wearables for Ambient-Assisted Living

Sensors ◽

10.3390/s17010036 ◽

2016 ◽

Vol 17 (12) ◽

pp. 36 ◽

Cited By ~ 20

Author(s):

Óscar Belmonte-Fernández ◽

Adrian Puertas-Cabedo ◽

Joaquín Torres-Sospedra ◽

Raúl Montoliu-Colás ◽

Sergi Trilles-Oliver

Keyword(s):

Assisted Living ◽

Ambient Assisted Living ◽

Indoor Positioning ◽

Positioning System ◽

Indoor Positioning System

Download Full-text

Ambient Assisted Living Systems: The Scope of Research and Development

Journal of Science and Technology Issue on Information and Communications Technology ◽

10.31130/jst.2017.46 ◽

2017 ◽

Vol 3 (2) ◽

pp. 21

Author(s):

Ashish D Patel ◽

Jigarkumar H. Shah

Keyword(s):

Assisted Living ◽

Independent Living ◽

Intelligent Systems ◽

New Technologies ◽

Ambient Assisted Living ◽

Smart Devices ◽

Economic Changes ◽

Aged Population ◽

The People ◽

Living Person

The aged population of the world is increasing by a large factor due to the availability of medical and other facilities. As the number grows rapidly, requirements of this segment of age (65+) are increasing rapidly as well as the percentage of aged persons living alone is also increasing with the same rate due to the inevitable socio-economic changes. This situation demands the solution of many problems like loneliness, chronic conditions, social interaction, transportation, day-to-day life and many more for independent living person. A large part of aged population may not be able to interact directly with new technologies. This sought some serious development towards the use of intelligent systems i.e. smart devices which helps the people with their inability to use the available as well future solutions. Ambient Assisted Living (AAL) is the answer to these problems. In this paper, issues related to AAL systems are studied. Study of challenges and limitations of this comparatively new field will help the designers to remove the barriers of AAL systems.

Download Full-text

High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽

10.3390/s21113701 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3701

Author(s):

Ju-Hyeon Seong ◽

Soo-Hwan Lee ◽

Won-Yeol Kim ◽

Dong-Hoan Seo

Keyword(s):

High Precision ◽

Distance Estimation ◽

Sampling Period ◽

Multipath Fading ◽

Indoor Positioning ◽

Positioning System ◽

Positioning Systems ◽

Positioning Errors ◽

Location Determination ◽

Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

Download Full-text

Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning

Applied Sciences ◽

10.3390/app11156805 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6805

Author(s):

Khaoula Mannay ◽

Jesús Ureña ◽

Álvaro Hernández ◽

José M. Villadangos ◽

Mohsen Machhout ◽

...

Keyword(s):

Kalman Filter ◽

Experimental Tests ◽

Indoor Positioning ◽

Location Based Services ◽

Smart Devices ◽

Final Position ◽

Positioning Systems ◽

Local Positioning System ◽

Tightly Coupled ◽

Position Estimate

Indoor positioning systems have become a feasible solution for the current development of multiple location-based services and applications. They often consist of deploying a certain set of beacons in the environment to create a coverage volume, wherein some receivers, such as robots, drones or smart devices, can move while estimating their own position. Their final accuracy and performance mainly depend on several factors: the workspace size and its nature, the technologies involved (Wi-Fi, ultrasound, light, RF), etc. This work evaluates a 3D ultrasonic local positioning system (3D-ULPS) based on three independent ULPSs installed at specific positions to cover almost all the workspace and position mobile ultrasonic receivers in the environment. Because the proposal deals with numerous ultrasonic emitters, it is possible to determine different time differences of arrival (TDOA) between them and the receiver. In that context, the selection of a suitable fusion method to merge all this information into a final position estimate is a key aspect of the proposal. A linear Kalman filter (LKF) and an adaptive Kalman filter (AKF) are proposed in that regard for a loosely coupled approach, where the positions obtained from each ULPS are merged together. On the other hand, as a tightly coupled method, an extended Kalman filter (EKF) is also applied to merge the raw measurements from all the ULPSs into a final position estimate. Simulations and experimental tests were carried out and validated both approaches, thus providing average errors in the centimetre range for the EKF version, in contrast to errors up to the meter range from the independent (not merged) ULPSs.

Download Full-text

Sensing Framework for the Internet of Actors in the Value Co-Creation Process with a Beacon-Attachable Indoor Positioning System

Sensors ◽

10.3390/s21010083 ◽

2020 ◽

Vol 21 (1) ◽

pp. 83

Author(s):

Keiichi Zempo ◽

Taiga Arai ◽

Takuya Aoki ◽

Yukihiko Okada

Keyword(s):

Radio Wave ◽

Indoor Positioning ◽

Measurement Range ◽

Pulse Interval ◽

Retail Stores ◽

Positioning System ◽

Radio Waves ◽

Positioning Systems ◽

Environment Recognition ◽

Indoor Positioning System

To evaluate and improve the value of a service, it is important to measure not only the outcomes, but also the process of the service. Value co-creation (VCC) is not limited to outcomes, especially in interpersonal services based on interactions between actors. In this paper, a sensing framework for a VCC process in retail stores is proposed by improving an environment recognition based indoor positioning system with high positioning performance in a metal shelf environment. The conventional indoor positioning systems use radio waves; therefore, errors are caused by reflection, absorption, and interference from metal shelves. An improvement in positioning performance was achieved in the proposed method by using an IR (infrared) slit and IR light, which avoids such errors. The system was designed to recognize many and unspecified people based on the environment recognition method that the receivers had installed, in the service environment. In addition, sensor networking was also conducted by adding a function to transmit payload and identification simultaneously to the beacons that were attached to positioning objects. The effectiveness of the proposed method was verified by installing it not only in an experimental environment with ideal conditions, but posteriorly, the system was tested in real conditions, in a retail store. In our experimental setup, in a comparison with equal element numbers, positioning identification was possible within an error of 96.2 mm in a static environment in contrast to the radio wave based method where an average positioning error of approximately 648 mm was measured using the radio wave based method (Bluetooth low-energy fingerprinting technique). Moreover, when multiple beacons were used simultaneously in our system within the measurement range of one receiver, the appropriate setting of the pulse interval and jitter rate was implemented by simulation. Additionally, it was confirmed that, in a real scenario, it is possible to measure the changes in movement and positional relationships between people. This result shows the feasibility of measuring and evaluating the VCC process in retail stores, although it was difficult to measure the interaction between actors.

Download Full-text

Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Electronics ◽

10.3390/electronics10010002 ◽

2020 ◽

Vol 10 (1) ◽

pp. 2

Author(s):

Alwin Poulose ◽

Dong Seog Han

Keyword(s):

Deep Learning ◽

Signal Strength ◽

Indoor Positioning ◽

Learning Model ◽

Signal Interference ◽

Positioning System ◽

Heat Maps ◽

Positioning Systems ◽

Localization Performance ◽

Deep Learning Model

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

Download Full-text

Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

Applied Sciences ◽

10.3390/app9061048 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1048 ◽

Cited By ~ 8

Author(s):

Huy Tran ◽

Cheolkeun Ha

Keyword(s):

Machine Learning ◽

Visible Light ◽

Noise Reduction ◽

Indoor Positioning ◽

Computational Time ◽

Dual Function ◽

Positioning System ◽

Positioning Accuracy ◽

Positioning Systems ◽

Machine Learning Classification

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

Download Full-text

An Indoor Positioning System Using Multiple Methods and Tools

International Journal of Information Communication Technology and Applications ◽

10.17972/ijicta20184134 ◽

2018 ◽

Vol 4 (1) ◽

pp. 11-22

Author(s):

Michael Adeyeye Oshin ◽

Nobaene Sehloho

Keyword(s):

Wireless Mesh Network ◽

Ad Hoc ◽

Indoor Positioning ◽

Mesh Network ◽

Positioning System ◽

Tracking Systems ◽

Positioning Systems ◽

Wireless Mesh ◽

Positioning Method ◽

Indoor Positioning System

With many different studies showing a growing demand for the development of indoor positioning systems, numerous positioning and tracking methods and tools are available for which can be used for mobile devices. Therefore, an interest is more on development of indoor positioning and tracking systems that are accurate and effective. Presented and proposed in this work, is an indoor positioning system. As opposed to an Ad-hoc Positioning System (APS), it uses a Wireless Mesh Network (WMN). The system makes use of an already existing Wi-Fi infrastructure technology. Moreover, the approach tests the positioning of a node with its neighbours in a mesh network using multi-hopping functionality. The positioning measurements used were the ICMP echos, RSSI and RTS/CTS requests and responses. The positioning method used was the trilateral technique, in combination with the idea of the fingerprinting method. Through research and experimentation, this study developed a system which shows potential as a positioning system with an error of about 2 m to 3 m. The hybridisation of the method proves an enhancement in the system though improvements are still required.

Download Full-text

A robust mobile robot indoor positioning system based on Wi-Fi

International Journal of Advanced Robotic Systems ◽

10.1177/1729881419896660 ◽

2020 ◽

Vol 17 (1) ◽

pp. 172988141989666 ◽

Cited By ~ 2

Author(s):

Wei Cui ◽

Qingde Liu ◽

Linhan Zhang ◽

Haixia Wang ◽

Xiao Lu ◽

...

Keyword(s):

Mobile Robot ◽

Principal Component ◽

Indoor Positioning ◽

Positioning System ◽

Infrared Sensors ◽

Positioning Systems ◽

Indoor Mobile Robot ◽

Robot Positioning ◽

Learning Machine ◽

Noisy Measurements

Recently, most of the existing mobile robot indoor positioning systems (IPSs) use infrared sensors, cameras, and other extra infrastructures. They usually suffer from high cost and special hardware implementation. In order to address the above problems, this article proposes a Wi-Fi-based indoor mobile robot positioning system and designs and develops a robot positioning platform based on the commercial Wi-Fi devices, such as Wi-Fi routers. Furthermore, a robust principal component analysis-based extreme learning machine algorithm is proposed to address the issue of noisy measurements in IPSs. Real-world robot indoor positioning experiments are extensively carried out and the results verify the effectiveness and superiority of the proposed system.

Download Full-text