# Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
## 2 Related Work
Both datasets contain time series of tri-axial accelerometer data during six physical activities with a frequency of 20 Hz, of which the four ones are common (Walking, Jogging, Sitting, Standing), while the other two activities are different for the WISDM dataset (Upstairs and Downstairs) and for the Actitracker dataset (Stairs and Lying Down). There is no pre-selection of training data and test data, so there is a lack of consistency in reporting the results of the implementations that use these datasets. For the WISDM dataset, all previous works developed user-dependent solutions, except for [35; 45; 46], who trained their models on the data of subjects different than the ones on which the models were tested. [PERSON] et al. [45] proposed using a combination of hand-crafted features and random forest or dropout classifiers on top of them and achieved 83.46% and 85.36%, respectively, using the leave-one-out testing technique. [PERSON] et al. [46] proposed an architecture of two cascaded CNNs, performed a seven-fold user-independent cross-validation, where five subjects were left out as a testing dataset and the rest were the training dataset, and reported an average f1-score of 84.6%. [PERSON] et al. [35] selected the first 26 subjects for their training set and tested the performance on the remaining 10 users, achieving 93.32% and 90.42% accuracy for an interval size of 200 and 50 data points, respectively. [PERSON] et al. [47] evaluated WISDM only and achieved 98.23% accuracy by using deep belief networks and hidden Markov models, but there was no mention of how the dataset was split into training and testing. [PERSON] et al. [48] trained an LSTM network to classify raw accelerometer data windows, but they reported an accuracy of 88.6% on an unspecified 80%/20% train/test split of the dataset. [PERSON] et al. [49] used a similar random 80%/20% train/test split, and they trained a combination of an LSTM and an RNN and achieved an accuracy of 93.8%. [PERSON] et al. [50] proposed a Personalized Recurrent Neural Network (PerRNN), which is a modification of the LSTM architecture, and achieved 96.44% accuracy on a personalized, i.e., user-dependent, 75%/25% dataset split. Another LSTM architecture was proposed by [51], which achieved 92.1% accuracy on an unspecified testing dataset split. [PERSON] et al. [52] trained a CNN model on randomly selected 70% of the dataset and evaluated on the remaining 30% of the dataset, reporting an accuracy score of 91.97%. Another CNN model was proposed by [PERSON] et al. [53], which scored 96.88% accuracy on a 10-fold user-dependent cross-validation of the WISDM dataset. As for the Actitracker dataset, which includes more subjects in the data collection, the problem persisted since [PERSON] et al. [54] scored 86.6% accuracy, but did not explain which part of the dataset was used for testing, while [PERSON] et al. [55], who achieved an impressive 92.22% accuracy, did not take the subjects into account and divided the dataset randomly, thus not excluding the data of subjects who were included in the training set. The seminal work of [PERSON] et al. [56; 57] achieved high performance on both datasets, 98.6% on WISDM and 92.7% on Actitracker, using spectral domain pre-processing, deep learning, and shallow learning, but the 10-fold cross-validation was again user-dependent. [PERSON] et al. [58] transformed the input window for every channel into an image and passed these through a dilated convolutional neural network. They evaluated in both a user-dependent fashion against [PERSON] et al. [56] and a user-independent fashion against [PERSON] et al. [35] on WISDM, achieving comparable performance. Important factors regarding the dataset and the implementation are the sampling frequency and the window sizes used to create the time-series input. Most solutions use a sampling rate between 20 Hz and 50 Hz, while datasets sampled at 50 Hz include the MHEALTH dataset [19; 20] and the UCI HAR dataset [42]. The custom dataset's frequency collected by [PERSON] et al.