Instituto de Engenharia de Sistemas e Computadores-Microsystems and Nanotechnologies, 1000-019 Lisbon, Portugal.
Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal.
Sensors (Basel). 2020 Sep 19;20(18):5376. doi: 10.3390/s20185376.
Back and spine-related issues are frequent maladies that most people have or will experience during their lifetime. A common and sensible observation that can be made is regarding the posture of an individual. We present a new approach that combines accelerometer, gyroscope, and magnetometer sensor data in combination with permanent magnets assembled as a wearable device capable of real-time spine posture monitoring. An independent calibration of the device is required for each user. The sensor data is processed by a probabilistic classification algorithm that compares the real-time data with the calibration result, verifying whether the data point lies within regions of confidence defined by a computed threshold. An incorrect posture classification is considered if both accelerometer and magnetometer classify the posture as incorrect. A pilot trial was performed in a single adult test subject. The combination of the magnets and magnetometer greatly improved the posture classification accuracy (89%) over the accuracy obtained when only accelerometer data were used (47%). The validation of this method was based on image analysis.
背部和脊柱相关问题是大多数人在一生中都会经历的常见疾病。一个常见且合理的观察结果是关于个体的姿势。我们提出了一种新的方法，将加速度计、陀螺仪和磁力计传感器数据与作为可穿戴设备组装的永磁体相结合，该设备能够实时监测脊柱姿势。每个用户都需要对设备进行独立校准。传感器数据由概率分类算法处理，该算法将实时数据与校准结果进行比较，验证数据点是否位于计算阈值定义的置信区域内。如果加速度计和磁力计都将姿势分类为不正确，则认为姿势分类不正确。在单个成年测试对象中进行了初步试验。与仅使用加速度计数据（47%）相比，磁铁和磁力计的组合大大提高了姿势分类的准确性（89%）。该方法的验证基于图像分析。
