# Human Activity Recognition (HAR) in Healthcare
## 2 An Overview of Published Articles  
[PERSON] and [PERSON] (contribution 1) experimented with an innovative predictive model of human activities (HAR). They demonstrated that the sensor-based MLP mixer architecture enables competitive performance in vision-based tasks with lower computational costs than other deep learning techniques. The MLP mixer recently created by Google Brain [12] does not use convolutions or self-attention mechanisms, and instead consists entirely of MLPs. The authors compared the performance of the MLP mixer with the existing state-of-the-art literature:  
*Ensemble LSTM. *CNN-BiGRU. *AttenSense. *Multi-agent attention. *DeepConvLSTM. *Triple attention. *Self-Caution*CNN. *b-LSTM-S. The performance was 10.1% better in the Daphnet Gait dataset, 1% better in the PAMAP2 dataset, and 0.5% better in the Opportunity dataset. [PERSON] et al. (contribution 2) used the HAR approach to understand human behavior by analyzing data representative of domestic routines. Their study is oriented towards establishing a connection between the activities of daily living, the spaces in which they take place, and the times related to the performance of the activities in a given place. Research has shown that this information is helpful for healthcare professionals to assess the health status of patients, for family members to keep track of the habits of relatives, and for home designers to assess the architectural characteristics of home interiors for accessibility and movement of residents. The authors used the knowledge discovery database (KDD) approach with the data analyst variant as a key player in the knowledge discovery process [13]. The KDD approach is an interactive and iterative knowledge discovery process that identifies relationships between data that must be valid, new, potentially useful, and understandable. The analyst gains a greater understanding of the domestic routine with each process iteration. The parameters used for the evaluation are the sequence of places visited, times of day at which they are visited, and average duration of visits; the signals are acquired using PIR sensors connected to a Raspberry Pi4, placed inside each room of the house. Transitions between positions are detected by measuring the RSSI power of the Bluetooth signal emitted by a BLE device worn by the subject being monitored. The evaluation of the method was verified through workshops with seventeen multidisciplinary participants: architects, engineers, health professionals, and caregivers. The feedback obtained was positive, confirming the validity of the method adopted as a source of significant information on the status of the monitored subjects. In the third manuscript, [PERSON] et al. (contribution 3) proposed a new multiscale hierarchical adaptive network structure for HAR called HMA Conv-LSTM. In this model, there are:  
* a multi-scale hierarchical convolution module (HMC) that performs finer-grained feature extraction on the spatial information of feature vectors;
* an adaptive channel feature fusion module that can blend functionality at different scales, improving model efficiency and removing redundant information;
* a dynamic channel-selection module-LSTM based on the attention mechanism to extract time context information. This multi-scale convolution module uses convolutional cores of different scales for extracting and splicing multi-scale features in both sensory and temporal dimensions. This strengthens the network's ability to recognize features of different scales, improves its adaptability, and enhances its ability to characterize features. The diversity and duration of the actions detected by sensors placed on different body positions dictate longer sliding window sizes for segmentation. This sizing can result in some fine-grained subtle action processes being overlooked, thus affecting action recognition.