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] |