基于加速度时域特征的实时人体行为模式识别

摘要： 针对高精度的实时人体行为模式识别，提出了一种基于加速度时域特征的行为模式识别算法.本算法选取时域特征作为唯一特征量，通过简化特征提取运算实现行为的实时识别，获得了高精度结果.通过在Android智能手机平台进行测试，每项动作识别正确率均可达80%以上.该算法相对于现有算法实时精度有明显提高，在手持终端领域具有较好的应用前景.

Real-time Human Activity Pattern Recognition Based on Time Domain Features of Acceleration

Abstract： An algorithm of activity pattern recognition based on the time domain feature of acceleration was proposed for real time human activity pattern recognition with high accuracy. Time-domain analysis was used as the only method for extracting features. Computation of feature extraction was simplified to achieve real time recognition of activity, and the ideal result with high accuracy was acquired at the same time. The data tests on Android smart phone indicate that the average accuracy of real time activity recognition is above 80%. The result proves that this algorithm has a much more high accuracy of real time recognition compared with the existing algorithms, which has great application prospect in hand-held terminal areas.