Abstract
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.
Highlights
With the rapid progress of Internet of Things (IoT) techniques, location is critical information for many fields and location-based services (LBSs) are widespread and prevalent in people’s daily lives [1,2,3]
Based on the above facts, location-aware services have been playing an important role in a variety of fields, and it will continue to do so by integrating the ambient sensing and artificial intelligence (AI) techniques
A cost-effective, light-weight, compact sized, and energy-efficient wearable indoor localization module with the corresponding algorithms are a desirable solution for various indoor localization purposes
Summary
With the rapid progress of Internet of Things (IoT) techniques, location is critical information for many fields and location-based services (LBSs) are widespread and prevalent in people’s daily lives [1,2,3]. Based on the above facts, location-aware services have been playing an important role in a variety of fields, and it will continue to do so by integrating the ambient sensing and artificial intelligence (AI) techniques. Indoor localization techniques have attracted increasing attention in many applications, which are named indoor location-based services (ILBSs), including elderly care, robot navigation, sports motion analysis, rehabilitation medicine, smart buildings, etc. A cost-effective, light-weight, compact sized, and energy-efficient wearable indoor localization module with the corresponding algorithms are a desirable solution for various indoor localization purposes
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