# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Accuracy Improvement in Body Sensors  
Body sensors are indispensable for monitoring individual health and providing crucial feedback for overall wellbeing. However, achieving accurate measurements through wearable sensors is often hampered by deviations in accuracy during the automatic collection of health vitals stemming from the various factors outlined in the challenges section of Table 2. The application of AI offers powerful solutions to address these challenges, enhancing the accuracy of body sensors through a range of advanced techniques tailored to each specific sensor type. However, prior to the application of AI algorithms, a common requirement is data preprocessing to address the issue of noise prevalent in sensor data, which is a challenge for all sensors. For instance, in heart rate monitors, noise impact can be mitigated using noise removal or outlier detection algorithms such as first-order low-pass filters and wavelet transformation algorithms [23] or the spectral filter algorithm for motion artifact and pulse reconstruction (SpaMA) [24]. Two-stage algorithms can also be employed for noise artifact elimination and spectral analysis [24]. Similarly, readings from blood sugar monitoring devices such as PPGs are influenced by the testing site and environmental conditions. These issues can be mitigated through filtering techniques such as the Butterworth filter, which operates within a frequency range of 0.5-8 Hz [25]. In CBGM, noise removal approaches include adaptive iterative filtering and fast discrete lifting-based wavelet transform (LWT) [26] as well as multi-filtering augmentation [27]. Pulse oximetry sensor data often utilize adaptive filtering techniques [28], while accelerometer and gyroscope sensors benefit from Butterworth high-pass filtering [29], complementary filters, and Kalman filters [30] for error assessment and enhanced accuracy. Artifacts can be effectively removed from EEG sensors using graph signal processing [31]. Motion artifacts can be reduced using analytical software tools [32], enhanced peak and valley detection algorithms [33], or through Fourier transformation [34]. Among the most widely used ML algorithms for overcoming accuracy limitations in body sensors are Bayesian models such as Gaussian naive Bayes (NB), probabilistic approaches, and nonparametric methods such as exemplar-based techniques, kernel methods, decision trees (DT), random forests (RF), bagging, and boosting. Other commonly employed algorithms are logistic regression (LoR), linear regression (LR), linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and support vector machine (SVM) [35; 36; 37; 38; 39]. Additionally, advanced signal processing techniques such as ANOVA, chi-square, mutual information, and ReliefF are utilized to enhance accuracy [40]. In the case of blood gas sensors, accuracy can be increased through SMOTEENN sampling [41], kernel principal component analysis (KPCA), and adaptive boosting [38]. Additionally, the accuracy of respiratory rate monitoring can be enhanced through motion video magnification using the Hermite transform and an artificial hydrocarbon network (AHN) following classification of frames using Bayesian-optimized AHN [42]. Furthermore, DL approaches are often applied to enhance accuracy. For instance, one-dimensional (1D) DL models such as GlucoNet are used for accurate blood glucose level measurement [25], while intraocular pressure sensors benefit from automated feature extraction using 1D CNNs [43]. DL-based algorithms, including deep neural networks (DNNs), graph neural networks (GNNs), deep belief networks (DBNs), generative adversarial networks (GANs), transformers, recurrent neural networks (RNNs), autoencoders, attention mechanisms, and CapsNets, as well as reinforcement learning (RL), are options for further improving accuracy [35; 44]. Additionally, the accuracy of CBGM can be enhanced using the multilayer perceptron (MLP) approach.