Abstract

Current positioning systems are primarily based on the Global Positioning System (GPS). Although the GPS is accurate within 10 m, it is mainly used for outdoor positioning services (Location-Based Service; LBS). However, since satellite signals cannot penetrate buildings, indoor positioning has always been a blind spot for satellite signals. As indoor positioning applications are extensive with high commercial values, they have created a competitive niche in the market. Existing indoor positioning technologies are unable to achieve less than 10 cm accuracy except for the Ultra Wide Band (UWB) technology. On the other hand, the Bluetooth protocol achieves an accuracy of 1 to 2 m. In this work, we use Bluetooth wireless signals to build a novel indoor positioning framework to avoid the high manufacturing costs involved in the UWB technology. The Bluetooth signals are combined with the results from artificial intelligence algorithms to improve accuracy. During laboratory indoor location tracking, the accuracy rate is 96%, which provides effective indoor tracking for the movement of people.

Highlights

  • 1.1 Research MotivationCurrently, indoor positioning is not as widely used in daily life as outdoor positioning, and fewer service types are provided

  • If indoor positioning technology can be combined with other technologies such as physiological information acquisition technology, home medical care or home lifestyle identification can be performed to achieve the goal of ubiquitous care

  • This paper proposes a novel indoor positioning system using artificial intelligence algorithms and various neural network models for learning a low-resource, high-accuracy algorithm

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Summary

Research Motivation

Indoor positioning is not as widely used in daily life as outdoor positioning, and fewer service types are provided. By combining indoor positioning technology with physiological information, the patient’s behavior, such as watching TV on the sofa, going to bed in the bedroom, or staying in the bathroom for overly long, can be speculated. Employees’ current locations can be provided to managers through indoor positioning services to determine whether or not the employees have entered restricted areas and violated safety regulations. Other services such as indoor environment monitoring, indoor disaster protection, rescue management [1,2], can all be accomplished with the advancement of indoor positioning technology

Research Purposes
Literature Review
Research Method
Multi-Layer Perceptron (MLP)
Recurrent Neural Network (RNN)
Activation Function
Rectified Linear Unit (ReLU) According to
Hyperbolic Tangent Function
Sigmoid Function The result calculated according to
Hardware Architecture
Wearing Device
Voltage Detection
The Watchdog Timer (WDT)
Low-Power Sending Packet
Indoor Positioning System Process
The Information Receiver The information receiver uses the Raspberry
The Indoor Positioning Model
Acquire Training Materials
Model Architecture
Multi-Layer Perceptron Positioning Model
Recurrent Neural Network Positioning Model
Acquire Indoor Positioning Model Test Data
The Indoor Positioning Model Accuracy and Resource Consumption
The MLP Indoor Positioning Model Accuracy and Resource Consumption
The RNN Indoor Positioning Model Accuracy and Resource Consumption
The Indoor Positioning Model Comparison
Findings
Conclusion
Full Text
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