Cost Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU
Wearable indoor localization can now find applications in a wide spectrum of fields, including the care of children and the elderly, sports motion analysis, rehabilitation medicine, robotics navigation, etc. Conventional inertial measurement unit (IMU) based position estimation and radio signal indoor localization methods based on WiFi, Bluetooth, ultra wide band (UWB) and radio frequency identification (RFID) all have their limitations regarding cost, accuracy, or usability, and a combination of the techniques has been considered a promising way to improve the accuracy. This investigation aims to provide a cost effective wearable sensing solution with data fusion algorithms for indoor localization and real time motion analysis. The main contributions of this investigation are: (1) the design of a wireless, battery powered, and light weight wearable sensing device integrating a low cost UWB module DWM1000 and micro electromechanical system (MEMS) IMU MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and attitude calculation, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman filter based time domain iterative compensations; and (4) evaluation of the developed sensor module by comparing it with UWB and IMU only solutions. The test results demonstrate that the average error of the integrated module reached 7.58 cm for an arbitrary walking path, which outperformed the IMU and UWB only localization solutions. The module could recognize lateral roll rotations during normal walking, which could be potentially used for abnormal gait recognition.
Sensors