Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing
利用智能手机加速度计数据，通过机器学习算法在远程服务器上识别坐、站、躺、走、慢跑等日常活动，测试了十种分类器，kNN和kStar算法准确率达99.01%。
Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the smartphone wirelessly. The smartphone was placed in the subject's trouser pocket while data was gathered. A large sample set was used to train the classifiers and then a test set was used to verify the algorithm accuracies. Ten different classifier algorithm configurations were evaluated to determine which performed best overall, as well as, which algorithms performed best for specific activity classes. Based on the results obtained, very accurate predictions could be made for offline activity recognition. The kNN and kStar algorithms both obtained an overall accuracy of 99.01%.