A Data-Driven Movement Model for Single Cellphone-Based Indoor Positioning

Abstract

Abstract-Indoor localization is a promising area with applications in in-home monitoring and tracking. Fingerprinting and propagation model-based WiFi localization techniques have limited spatial resolution because of grid or graph-based representations. An alternative is to incorporate dynamics models based on real-time sensing of human movement and fuse these with WiFi measurements. We present a data-driven dynamic model that tracks the inherent periodicity in walking and converts this representation into velocity. This model however is prone to drift. We correct this drift with WiFi measurements to obtain a combined position estimate. Our approach records and fuses human body movement with WiFi positioning using a single mobile phone. We characterize the movement model to obtain an estimate of error predictions. The movement model showed a best case average RMS prediction error of .25 m/s. We also present a preliminary study characterizing combined system performance across straight line and L-shaped trajectories. The framework showed lower errors across the L-shaped trajectories (mean error ≈ 4.8 m using movement sensing versus a mean error ≈ 6 m without movement sensing) because of the ability assess the validity of a WiFi measurement. Higher errors were observed across the straight line trajectory due to imprecise trajectories.

Towards a natural walking monitor for pulmonary patients using simple smart phones

“…Normally, raw accelerometer characteristics are taken from the frequency domain to train the models. Models have been trained and assessed on healthy patients using linear regression [20], Bayesian regression [30], Gaussian regression [29], support vector machines [16,21] and neural networks [25]. Each of these works train models with input windows ranging from 1 to 10 seconds and sampling frequencies ranging from 33Hz to 250 Hz.…”

WI-FI Based Indoor Positioning Using Fingerprinting Methods (KNN Algorithm) in Real Environment

“…Unless cheap and convenient techniques such as Wi-Fi can be developed and comprehensive indoor localization continues to be a challenge [4]. According to Vathsangam et al, [5], one of the suitable and cost effective candidate's techniques is using existing Wireless Received Signal Strength (RSS)-based indoor positioning methods. Wi-Fi location determination consists of two primary methods, signal strength propagation models and fingerprinting techniques.…”

“…On the other hand if the value of K is larger, then there is higher bias, so choosing the proper K depends on the data. The approach applied and the result for K=2, 3,5,7,11,13,17,19 Figure 8. The distance between estimated position and real position in testing dataset was calculated for each value of K, and then the average of error was calculated as a Mean Square Error (MSE) for various value of K. The MSE is calculated by:…”