Precision Enhancement and Performance Evaluation of a CORDIC-Based Tilting Angle Identification Algorithm for Three-Axis Accelerometers

Abstract

In this paper, we analyze the precision of the tilting angles transferred from a 3-axis accelerometer with the previously proposed CORDIC-based tilting angle identification algorithm. We found out two issues of the original 2D-CORDIC operations which would increase the errors of the calculated angles. Hence, we propose the solutions to further enhance the precision of tilting angles calculated in the algorithm. We also evaluate the performance of our algorithm on 5 different platforms with different processor cores ranging from low-end micro-controllers to high-end desktop CPU. The results indicate that the algorithm is extremely suitable for implementation on low cost and low power micro-controllers to perform the real-time and in-line tilting angle computations for its low computation complexity and low memory requirements. As the results, precise tilting angles can be obtained in realtime with a low cost embedded system to be applied in various applications including medical care systems.

Development of a Wearable Instrumented Vest for Posture Monitoring and System Usability Verification Based on the Technology Acceptance Model

“…For example, accelerometer information is transformed by a patented low-complexity CORDIC-based tilting angle transformation algorithm [16]. The algorithm is based on CORDIC angle-accumulation (or vectoring mode) operations [17], which were improved for precision enhancement [18]. Therefore, the microcontroller benefits from the use of pure integer iterative add/sub/shift operations in the algorithm and efficiently converts raw data from the three-axis accelerometers to tilting angles [19].…”

Field Programmable Gate Array (FPGA) Respiratory Monitoring System Using a Flow Microsensor and an Accelerometer

“…But it suffers from thermal drift, inexact computation results and lack of reprogrammability. The digital implementation offers a good precision of computing, true parallel implementation, and powerful software development tools [15], [21], [23]. It is important to note that ANNs require parallel computation.…”

“…The BMA 180 was used to compute the tilt angle using the CORDIC algorithm (COrdinate Rotation DIgital Computer) and the inverse tangent function [22], [23]. Two basic CORDIC modes are used to compute different functions: the rotation mode and the vectoring mode [19].…”

“…The neurons in the hidden layer of the ANN model use the sigmoid activation function which has been computed with the CORDIC algorithm [19]. This algorithm proposed by Volder [20] is a simple and efficient method to calculate the trigonometric and hyperbolic function through coordinate transformation [21]- [23]. It has been used in both the temperature compensation of the flow sensor and the body position measurement.…”

Forward-flexed posture detection for the early Parkinson'S disease symptom

“…Therefore, a low complexity CORDIC-based algorithm [3] was proposed previously to enable a real-time transformation of the tilting angle from the raw data of accelerometer in such system [4]. Through detailed performance analysis and improvement of the original CORDIC operations, the algorithm can reach the accuracy of 0.1 degree within 8 iterative operations of CORDIC and the execution time is 3.83 times faster than using mathematical libraries on a low power, low cost microcontroller [5].…”