Cost-effective filtering of unreliable proximity detection results based on BLE RSSI and IMU readings using smartphones

Abstract

Indoor environments are a major challenge in the domain of location-based services due to the inability to use GPS. Currently, Bluetooth Low Energy has been the most commonly used technology for such services due to its low cost, low power consumption, ubiquitous availability in smartphones and the dependence of the signal strength on the distance between devices. The article proposes a system that detects the proximity between static (anchors) and moving objects, evaluates the quality of this prediction and filters out the unreliable results based on custom metrics. We define three metrics: two matrics based on RSSI and Intertial Measurement Unit (IMU) readings and one joint metric. This way the filtering is based on both, the external information (RSSI) and the internal information (IMU). To process the IMU data, we use machine learning activity recognition models (we apply feature selection and compare three models and choose the best one—Gradient Boosted Decision Trees). The proposed system is flexible and can be easily customized. The great majority of operations can be conducted directly on smartphones. The solution is easy to implement, cost-efficient and can be deployed in real-life applications (MICE industry, museums, industry).

Research Progress of Wireless Positioning Methods Based on RSSI

“…where p denotes the measured value and p denotes the true value. The average error is the result of averaging the Euclidean distance between the estimated value and the actual value after multiple measurements, as shown in Equation (3). Both this and distance root mean square can effectively measure the validity of the algorithm:…”

“…As a mature positioning technology in an outdoor environment, the Global Navigation Satellite System (GNSS) has achieved sub-meter positioning accuracy, which can provide more accurate positioning services [2]. However, GNSS and the Global Positioning System (GPS) are still unable to obtain accurate positions in an indoor environment [3]. At the same time, people's increasingly frequent indoor activities create a stronger demand for wireless positioning technology [4].…”

RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems

“…We used an experimental setup and protocol similar to that of [17], as shown in Table 2. Data were collected in a room-scale setup that nearly exactly replicated the experimental approach in [17], which allowed the accurate exploration of the research gap to determine the localization improvement attained when taking device pose into account.…”

“…We used an experimental setup and protocol similar to that of [17], as shown in Table 2. Data were collected in a room-scale setup that nearly exactly replicated the experimental approach in [17], which allowed the accurate exploration of the research gap to determine the localization improvement attained when taking device pose into account. RSSI and accelerometer measurements were collected at the five locations shown in Figure 5 (in the remainder of the paper, these are referred to either as ZoneA, ZoneB, ZoneC, ZoneD, and ZoneE, or ZA, ZB, ZC, ZD, and ZE, respectively) in an indoor environment with six spatially distributed anchors for four different device-pose use cases (shown in Figure 6, labelled AtEar, InHand, InHandbag, and InPocket, or AE, IH, IB, and IP, respectively).…”

“…Notably, in a recent study [17], the researchers used machine learning algorithms on accelerometer measurements to detect whether a user is moving (dynamic) or standing relatively still (static); thus, they proposed reliability metrics for the localization on RSSI values received while the user is static and dynamic. As such, the authors assessed the relationship between the user behavior component in Figure 1 and the RSSI localization in the experimental setup of five zones in a large room.…”