# MLPs Are All You Need for Human Activity Recognition
## 6 Results  
The Mixer is compared with the following state-of-the-art architectures:  
* **Ensemble LSTMs [32]:** combines multiple LSTMs using ensemble techniques to produce a single LSTM.
* **CNN-BiGRU [37]:** CNN connected with a biGRU.
* **AttenSense [22]:** a CNN and GRU are combined using an attention mechanism to learn spatial and temporal patterns.
* **Multi-Agent Attention [38]:** combines multi-agent collaboration with attention-based selection.
* **DeepConvLSTM [35]:** combines an LSTM to learn temporal information with a CNN to learn spatial features.
* **BLSTM-RNN [33]:** a bi-LSTM, with its weights and activation functions binarized.
* **Triple Attention [39]:** a ResNet, using a triple-attention mechanism.
* **Self-Attention [40]:** a self-attention-based model without any recurrent architectures.
* **CNN [18]:** a CNN with three layers and max pooling.
* **b-LSTM-S [18]:** bidirectional LSTM that uses future training data.  
Table 4 shows the performance comparison between the Mixer and existing state-of-the-art literature. Table 4 shows that the MLP-Mixer performs better than previous techniques in the Opportunity Locomotion, PAMAP2, and Daphnet Gait datasets. Despite the model's shortcomings in the Opportunity Gestures dataset, it is still competitive with most of the previously developed methods. Sliding window techniques were used in all the previous techniques, with only the sliding window lengths and overlaps differing. Although the Mixer beats the previous techniques in Opportunity Locomotion, mostprevious work that used the Opportunity dataset for performance evaluation only focused on the gesture classification task while disregarding the locomotion task.  
The sliding window lengths used were similar to or larger than previous techniques, allowing the model to capture more information from each interval. Therefore, it can be concluded that the MLP-Mixer model can learn the spatial and temporal dynamics of the sensor data more effectively than the previous models. The Mixer performs better than existing attention and convolution-based models in PAMAP2. The macro-score of the Mixer is slightly higher (0.97) than the triple-attention model [39] (0.96) and significantly higher than the best convolution-based model [18] (0.937), and it performed better than the state-of-the-art by 1%. In the daphnet-gait dataset, the model also performed better than convolution and recurrent models, producing a macro-score of 0.842 compared to 0.741. It performed better than the state-of-the-art by 10.1%. However, the existing literature using the Daphnet Gait focuses more on future prediction [41; 42; 43] instead of recognition and uses different evaluation metrics; therefore, it cannot be directly compared to the Mixer. In the Opportunity Gestures, the Mixer remains competitive but does not perform better than the b-LSTM-S. The opportunity dataset was particularly challenging for the MLP-Mixer, due to shorter activities combined with a larger sliding window necessary for the image to be split into patches. As a result, there were several activities in the training sliding window, making it more difficult for the Mixer to learn and harder for it to predict activities in the test sliding window. The b-LSTM-S performed 1.7% better than the Mixer in this dataset.