# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 5 Utilization of AI Analysis in the IoMT
### Model Selection and Training
#### 5.4.4 Role of ML Models  
The IoMT relies heavily on sensor data analysis, utilizing a network of embedded sensors across devices and environments to collect data on physical parameters such as temperature, humidity, light, sound, and motion. These data undergo analysis using various ML and statistical techniques to extract meaningful insights and patterns. Models such as time series analysis, anomaly detection, and classification algorithms are employed to detect abnormal events, predict trends, and recognize user activities. These insights drive automated actions and personalized services, thereby seamlessly enhancing user experiences. As depicted in Figure 5, popular models for sensor data analysis can be broadly classified into two approaches, namely, ML-based approach and DL-based approach. Statistical models, including measures such as the mean, median, and standard deviation, provide fundamental tools for understanding sensor data distribution and characteristics, while ML-based approach such as LR explore relationships between variables to aid in predictive modeling. The k-NN approach [140] classifies or regresses sensor data based on data point similarity, which is useful for tasks such as anomaly detection and predicting sensor readings based on nearby instances. SVMs [141; 142] offer robust classification and regression capabilities. DT and RF [141] provide interpretable models suitable for complex data. Neural networks, including RNN and LSTM, uncover intricate patterns in sensor data using DL. Popular LSTM variants [143] include vanilla LSTM, bidirectional LSTM, stacked LSTM, peephole LSTM, multivariate LSTM, and attention-based LSTM. Similarly, popular RNN variants [144] encompass vanilla, bidirectional, univariate multi-to-one (Uniform), univariate many-to-many (Uni-MM), multivariate many-to-one (Multi-MMO), and multivariate many-to-many (Multi-MM) RNNs. Time series models such as autoregressive integrated moving averages (ARIMA) [145] and hidden Markov models (HMMs) [146] excel in analyzing sequential data for forecasting and anomaly detection.  
Among of the popular DL-based approaches are Radial Basis Function Neural Networks (RBFNNs), which leverage radial basis functions in their hidden layers to effectively handle function approximation and pattern recognition tasks across domains [147]. Autoencoders (AE) are unsupervised neural network architectures used for learning efficient representations of input data through minimizing reconstruction error, and find applications in dimensionality reduction, data denoising, and anomaly detection. Popular variants include stacked AE and Denoising AE. Boltzmann Machines (BM) are stochastic RNNs featuring binary-valued neurons and bidirectional information flow. Trained using contrastive divergence, BMs find use in feature learning, dimensionality reduction, and generative modeling [148]. Various sensor data analysis models are depicted in Figure 5.  
Figure 5: A pictorial representation of popular models used for sensor data analysis.