背景:使用智能手机传感器的人类活动识别(HAR)遇到两个主要问题:传感器的取向和放置。传感器方向和传感器放置问题是指由于传感器改变方向和放置而导致的特定活动的传感器信号变化。从原始传感器信号中提取方向和位置不变特征是解决这些问题的简单解决方案。使用很少的启发式特征而不是众多的时域和频域特征在这种方法中提供了更多的简单性。启发式特征是对传感器取向和放置具有非常小影响的特征。在这项研究中,我们使用1D-CNN-LSTM模型对由超过1200万个样本组成的数据集,评估了四个简单启发式特征在解决传感器取向和放置问题方面的有效性.
方法:我们从42名参与者中收集了六种常见日常活动的数据:说谎,坐着,散步,并以3-代谢等效任务(MET)运行,来自智能手机的单个加速度计传感器的5-MET和7-MET。我们对智能手机的三个位置进行了研究:口袋,背包和手。我们从加速度计数据中提取了简单的启发式特征,并使用它们来训练和测试1D-CNN-LSTM模型,以评估它们在解决传感器方向和放置问题方面的有效性。
结果:我们进行了位置内和位置间评估。在位置内评估中,我们使用来自相同智能手机位置的数据来训练和测试模型,然而,在职位间评估中,训练和测试数据来自不同的智能手机位置.对于位置内评估,我们获得了70-73%的准确率;对于位置间的情况,准确率在59%到69%之间。此外,我们进行了参与者特异性和活动特异性分析.
结论:我们发现简单的启发式特征在解决定向问题方面相当有效。随着进一步发展,例如将启发式特征与其他消除放置问题的方法融合在一起,我们也可以实现一个更好的结果比我们使用启发式功能的传感器放置问题的结果。此外,我们发现启发式特征在识别高强度活动方面更有效。
BACKGROUND: Human activity Recognition (HAR) using smartphone sensors suffers from two major problems: sensor orientation and placement. Sensor orientation and sensor placement problems refer to the variation in sensor signal for a particular activity due to sensors\' altering orientation and placement. Extracting orientation and position invariant features from raw sensor signals is a simple solution for tackling these problems. Using few heuristic features rather than numerous time-domain and frequency-domain features offers more simplicity in this approach. The heuristic features are features which have very minimal effects of sensor orientation and placement. In this study, we evaluated the effectiveness of four simple heuristic features in solving the sensor orientation and placement problems using a 1D-CNN-LSTM model for a data set consisting of over 12 million samples.
METHODS: We accumulated data from 42 participants for six common daily activities: Lying, Sitting, Walking, and Running at 3-Metabolic Equivalent of Tasks (METs), 5-METs and 7-METs from a single accelerometer sensor of a smartphone. We conducted our study for three smartphone positions: Pocket, Backpack and Hand. We extracted simple heuristic features from the accelerometer data and used them to train and test a 1D-CNN-LSTM model to evaluate their effectiveness in solving sensor orientation and placement problems.
RESULTS: We performed intra-position and inter-position evaluations. In intra-position evaluation, we trained and tested the model using data from the same smartphone position, whereas, in inter-position evaluation, the training and test data was from different smartphone positions. For intra-position evaluation, we acquired 70-73% accuracy; for inter-position cases, the accuracies ranged between 59 and 69%. Moreover, we performed participant-specific and activity-specific analyses.
CONCLUSIONS: We found that the simple heuristic features are considerably effective in solving orientation problems. With further development, such as fusing the heuristic features with other methods that eliminate placement issues, we can also achieve a better result than the outcome we achieved using the heuristic features for the sensor placement problem. In addition, we found the heuristic features to be more effective in recognizing high-intensity activities.