Curone Davide, Bertolotti Gian Mario, Cristiani Andrea, Secco Emanuele Lindo, Magenes Giovanni
European Centre for Training and Research in Earthquake Engineering, Pavia 27100, Italy.
IEEE Trans Inf Technol Biomed. 2010 Jul;14(4):1098-105. doi: 10.1109/TITB.2010.2050696. Epub 2010 May 18.
Assessment of human activity and posture with triaxial accelerometers provides insightful information about the functional ability: classification of human activities in rehabilitation and elderly surveillance contexts has been already proposed in the literature. In the meanwhile, recent technological advances allow developing miniaturized wearable devices, integrated within garments, which may extend this assessment to novel tasks, such as real-time remote surveillance of workers and emergency operators intervening in harsh environments. We present an algorithm for human posture and activity-level detection, based on the real-time processing of the signals produced by one wearable triaxial accelerometer. The algorithm is independent of the sensor orientation with respect to the body. Furthermore, it associates to its outputs a "reliability" value, representing the classification quality, in order to launch reliable alarms only when effective dangerous conditions are detected. The system was tested on a customized device to estimate the computational resources needed for real-time functioning. Results exhibit an overall 96.2% accuracy when classifying both static and dynamic activities.
文献中已提出在康复和老年人监测环境中对人类活动进行分类。与此同时，最近的技术进步使得能够开发集成在服装内的小型可穿戴设备，这可能将这种评估扩展到新的任务，例如对在恶劣环境中工作的工人和应急人员进行实时远程监测。我们提出了一种基于一个可穿戴三轴加速度计产生的信号的实时处理的人体姿势和活动水平检测算法。该算法与传感器相对于身体的方向无关。此外，它为其输出关联一个表示分类质量的“可靠性”值，以便仅在检测到有效的危险情况时才发出可靠的警报。该系统在定制设备上进行了测试，以估计实时运行所需的计算资源。在对静态和动态活动进行分类时，结果显示总体准确率为96.2%。
