Table 3.

Non-exhaustive List of Algorithms used for multi-sensor data modelling

Algorithm	Task	Performance	Category	References
Kernel Ensemble Random Forest classifier with 40 estimators, 8 features at depth of 15	Heart disease prediction using daily activity data from multiple sensors	98% accuracy on testing data	Medical Data	[18]
Convolutional Neural Networks	Fault diagnosis in a planetary gearbox from multi-sensor data	93% to 99% accuracy on testing data	Machine Design	[58]
Long Short-Term Memory Artificial Neural Network	Real-time identification of foot contact and foot off by analyzing gait pattern in children	~ 95% with maximum delay of 3 s in real time	Human Motion Analysis	[59]
TimeNet Pre-trained Deep Recurrent Neural Network	Generalized time series classification across multiple datasets	The average accuracy observed was 83% on various datasets	Generalized solution for series analysis across various domains	[60]
Choquet Integral + Hidden Markov Chain Models	Multivariate Time Series Anomaly Detection across various data sets	Between 90 to 99% depending on the chosen dataset	Anomaly detection	[61]
Convolutional Neural Networks	Real-Time Skeletal Posture estimation using mm-wave radar	Localization error of 3.2 cm for X, 2.7 for y and 7.5 for z	Human Motion Capture	[39]
Principle Component Analysis + Toeplitz Inverse-Covariance Clustering	Multivariate Time series analysis for identification of recurring events in smart manufacturing	Performs best across multiple performance matrices (F1, Precision, Rand Index, etc.)	Automatic Event Detection	[62]
K-nearest neighbors	Method for Recognition of the Physical Activity of Human Being Using a Wearable Accelerometer	78.9% accuracy	Activity Recognition	[63]
Support Vector Machines	Fall detection on mobile phones using features from a five-phase mode	Recall 90% and precision 95.7%	Activity Recognition /Fall detection	[64]
Artificial neural networks	An alternative to traditional fall detection methods	Sensitivity 0.984 Specificity 0.986	Activity Recognition /Fall detection	[65]
Bayesian sequential analysis and Multilayer Perceptron	Contamination Event Detection with Multivariate Time-Series Data in Agricultural Water Monitoring	Average detection rate > 80%	Water Contamination Event Detection	[66]
Fisher's Linear Discriminant	Detecting Stress During Real-World Driving Tasks Using Physiological Sensors	Accuracy of 97.4%	Stress Level Detection	[67]
Correlation-based feature selection with random forest classifier with random forest classifier	Automated epileptic seizure detection	Average accuracy of 98.45%	Medical Diagnosis	[68]