Nez Alexis, Fradet Laetitia, Laguillaumie Pierre, Monnet Tony, Lacouture Patrick
PPrime Institute, CNRS - University of Poitiers - ENSMA, UPR 3346, Robotics, Biomechanics, Sport and Health, Futuroscope, France.
Med Eng Phys. 2016 Nov;38(11):1289-1299. doi: 10.1016/j.medengphy.2016.08.004. Epub 2016 Aug 31.
In the fields of medicine and biomechanics, MEMS accelerometers are increasingly used to perform activity recognition by directly measuring acceleration; to calculate speed and position by numerical integration of the signal; or to estimate the orientation of body parts in combination with gyroscopes. For some of these applications, a highly accurate estimation of the acceleration is required. Many authors suggest improving result accuracy by updating sensor calibration parameters. Yet navigating the vast array of published calibration methods can be confusing. In this context, this paper reviews and evaluates the main measurement models and calibration methods. It also gives useful recommendations for better selection of a calibration process with regard to a specific application, which boils down to a compromise between accuracy, required installation, algorithm complexity, and time.
在医学和生物力学领域,微机电系统(MEMS)加速度计越来越多地被用于通过直接测量加速度来进行活动识别;通过信号的数值积分来计算速度和位置;或者与陀螺仪结合来估计身体部位的方向。对于其中一些应用,需要对加速度进行高精度估计。许多作者建议通过更新传感器校准参数来提高结果的准确性。然而,在众多已发表的校准方法中进行选择可能会令人困惑。在此背景下,本文回顾并评估了主要的测量模型和校准方法。它还针对特定应用给出了关于更好地选择校准过程的有用建议,这归根结底是在精度、所需安装、算法复杂性和时间之间进行权衡。