Munoz-Organero Mario, Lotfi Ahmad
Telematics Engineering Department, Universidad Carlos III de Madrid, Avda de la Universidad, 30, E-28911 Leganés, Madrid, Spain.
School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.
Sensors (Basel). 2016 Sep 9;16(9):1464. doi: 10.3390/s16091464.
Human activity recognition algorithms based on information obtained from wearable sensors are successfully applied in detecting many basic activities. Identified activities with time-stationary features are characterised inside a predefined temporal window by using different machine learning algorithms on extracted features from the measured data. Better accuracy, precision and recall levels could be achieved by combining the information from different sensors. However, detecting short and sporadic human movements, gestures and actions is still a challenging task. In this paper, a novel algorithm to detect human basic movements from wearable measured data is proposed and evaluated. The proposed algorithm is designed to minimise computational requirements while achieving acceptable accuracy levels based on characterising some particular points in the temporal series obtained from a single sensor. The underlying idea is that this algorithm would be implemented in the sensor device in order to pre-process the sensed data stream before sending the information to a central point combining the information from different sensors to improve accuracy levels. Intra- and inter-person validation is used for two particular cases: single step detection and fall detection and classification using a single tri-axial accelerometer. Relevant results for the above cases and pertinent conclusions are also presented.
基于可穿戴传感器所获信息的人类活动识别算法已成功应用于多种基本活动的检测。对于具有时间平稳特征的已识别活动，通过对测量数据提取的特征运用不同机器学习算法，在预定义的时间窗口内进行特征描述。通过整合来自不同传感器的信息，可以实现更高的准确率、精确率和召回率。然而，检测短暂且零星的人类动作、手势和行为仍是一项具有挑战性的任务。本文提出并评估了一种从可穿戴测量数据中检测人类基本动作的新颖算法。所提出的算法旨在将计算需求降至最低，同时基于对从单个传感器获取的时间序列中的某些特定点进行特征描述，实现可接受的准确率水平。其基本思想是，该算法将在传感器设备中实施，以便在将信息发送到结合来自不同传感器的信息以提高准确率水平的中心点之前，对感测到的数据流进行预处理。针对两种特定情况进行了个体内和个体间验证：使用单个三轴加速度计进行单步检测以及跌倒检测与分类。还给出了上述情况的相关结果及恰当结论。
