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| Reference | Year | Dataset used | Sensors/dataset used | Sensor placement (if wearable system) | Methodology | Observed performance |
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| [30] | 2011 | UCI dataset | 3-Axes accelerometer, 2-axis gyroscope | Chest, thigh | Comparison of ML algorithms for fall detection using single node and two nodes | Accuracy of classification = 99.8%, with 2 nodes—one on the waist and one on the knee
Naïve-Bayes classifier gave best results |
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| [34] | 2012 | Generated from experiments | Accelerometer | Mobile phone | Comparison of SVM, SMLR, Naive Bayes, decision trees, kNN, and regularized logistic regression for fall detection | Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall (trips, left lateral, slips, right lateral) with 99% accuracy. Naïve-Bayes reported least accuracy |
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| [29] | 2014 | Generated from experiments | Accelerometer, gyroscope, magnetometer | 6 different positions on the body | Comparison of k-NN classifier, LSM, SVM, BDM, DTW, and ANNs algorithms | k-NN classifier and LSM gave above 99% for sensitivity, specificity, and accuracy |
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| [22] | 2014 | Generated from experiments | Accelerometer | Mobile phone | Accelerometer data from wearable sensors to generate alarms for falls, combined with context recognition using sensors in an apartment, for inferring regular ADLs, using Bayesian networks | Provides statistical information regarding the fall risk probability for a subject |
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| [48] | 2015 | Publicly available activity recognition dataset | Accelerometer, gyroscope | Smartphone | Comparison of Naive Bayes classifier, decision trees, random forests, classifiers based on ensemble learning (random committee), and lazy learning (IBk) algorithms for activity detection | Naive Bayes classifier performs reasonably well for a large dataset, with 79% accuracy, and it is fastest in terms of building the model taking only.5.76 seconds
Random forests are better in terms of both accuracy and model building time, with 96.3% accuracy and 14.65 seconds model building time. k-Means clustering performs poorly with 60% classification accuracy and 582 seconds model building time |
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| [47] | 2016 | Generated from experiments | 3-Axes accelerometer | Not specified | Comparison of decision tree, decision tree ensemble, kNN, neural networks, MLP algorithms for soft fall detection | Decision tree ensemble was able to detect soft falls at more than 0.9 AUC |
| [31] | 2016 | MobiFall dataset | Accelerometer, gyroscope | User’s trouser pocket | Comparison of Naïve-Bayes, LSM, ANN, SVM, kNN algorithms for fall detection | k-NN, ANN, SVM had the best accuracy—results for kNN:
Accuracy = 87.5
Sensitivity = 90.70
Specificity = 83.78 |
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| [26] | 2016 | Generated from experiments | 3-Axis accelerometer | Smartwatch | Threshold-based analysis of acceleration | Accuracy = 96.01% |
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| [40] | 2017 | Generated from experiments | Accelerometer, gyroscope | Vest | Kalman filter for noise reduction, sliding window, and Bayes network classifier for fall detection | With Kalman filter
Accuracy = 95.67%,
Sensitivity = 99.0%
Specificity = 95.0% |
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| [38] | 2017 | Generated from experiments | 3D accelerometer | Smartphone | Combination of threshold-based and ML-based algorithms—K-Star, Naive Bayes, J48 | Energy saving = 62% compared with ML-only techniques
Sensitivity = 77% (thresholding only), 82% (ML only), 86% (hybrid)
Specificity = 99.8% (thresholding only), 98% (ML only), 99.5% (hybrid)
Accuracy = 88.4% (thresholding only), 90% (ML only), 92.75% (hybrid) |
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| [46] | 2017 | Generated from experiments | 3-Axes accelerometer | Waist | Combination of threshold-based and knowledge-based approach based on SVM to detect a fall event | Using a knowledge-based algorithm:
Sensitivity = 99.79%
Specificity = 98.74%
Precision = 99.05%
Accuracy = 99.33% |
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| [49] | 2017 | Generated from experiments | 3-Axes accelerometer | Smartwatch | Spectrum analysis, combined with GA-SVM, SVM, and C4.5 classifiers | GA-SVM gave best results with
Accuracy = 94.1%
Sensitivity = 94.6%
Specificity = 93.6% |
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| [50] | 2017 | MobiFall dataset | 3-Axes accelerometer | Not specified | Comparison of multilevel fuzzy min-max neural network, MLP, KNN, SVM, PCA for fall detection | Multilevel fuzzy min-max neural network gave best results with
Sensitivity = 97.29%
Specificity = 98.70% |
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| [37] | 2017 | FARSEEING dataset | 3-Axes accelerometer | 5 locations on the upper body - neck, chest, waist, right side, and left side | Sensor orientation calibration algorithm to resolve issues arising out of misplaced sensor locations and misaligned sensor orientations, HMM classifiers | Sensitivity = 99.2% (experimental dataset), 100% (real-world fall dataset) |
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| [11] | 2017 | Generated from experiments | 3-Axes accelerometer | Chest | LWT-based frequency domain analysis and SVM-based time domain analysis of RMS of acceleration | Accuracy = 100%
Sensitivity = 100%
Specificity = 100% |
| [32] | 2017 | Generated from experiments | 3-Axis accelerometer, 3-axis gyroscope | Waist | Backpropagation neural network (BPNN) for fall detection | Accuracy = 98.182%
Precision = 98.33%
Sensitivity = 95.161%
Specificity = 99.367% |
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| [39] | 2010 | Generated from experiments | Accelerometer | Chest, thigh | Naïve-Bayes, SVM, OneR, C4.5 (J48), neural networks | Naïve-Bayes gave best results
Accuracy = 100%
Sensitivity = 87.5% |
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| [43] | 2016 | Generated from experiments | Accelerometer | Different parts of the body | Bayesian framework for feature selection, Naïve-Bayes, C4.5 | Better accuracy with improved classification than Naïve-Bayes and C4.5 |
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| [33] | 2016 | Generated from experiments | 3D accelerometer | Chest | SVM, kNN, complex tree algorithms applied on data generated by accelerometers | Accuracy and precision of SVM were the highest
Recall was highest for complex tree |
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| [44] | 2017 | Generated from experiments | Accelerometer (MobiAct dataset) | Not applicable | ENN + kNN (where ENN was applied to remove outliers), ANN, SVM, and J48 | For ENN + kNN:
Sensitivity = 95.52%
Specificity = 97.07%
Precision = 91.83% |
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| [41] | 2018 | Generated from experiments | Triaxial gyroscope | Waist | Decision tree | Accuracy = 99.52%
Precision = 99.3%
Recall = 99.5% |
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| [45] | 2018 | Cogent dataset, SisFall dataset | 3D accelerometer, 3D gyroscope-Cogent dataset
Accelerometer, gyroscope-SisFall dataset | Chest, waist | Event-ML, classification and regression tree (CART), kNN, logistic regression, SVM | Better precision and F-scores with Event-ML than FOSW and FNSW-based approaches |
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| [42] | 2019 | Public datasets | Accelerometer, gyroscope | Chest, thigh | ANN, kNN, QSVM, ensemble bagged tree (EBT) | Extraction of new features from acceleration and angular velocity improved the accuracy of all 4 classifiers. Accuracy of EBT was highest (97.7%) |
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| [51] | 2019 | SisFall dataset | Accelerometer, gyroscope | Waist | kNN, SVM, random forest | Accuracy for fall detection was the highest for kNN (99.8%). Accuracy for recognizing fall activities was the highest for random forest (96.82%) |
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| [52] | 2018 | SisFall dataset, generated from experiments | Accelerometer | Chest/thigh, waist | SVM, kNN, Naïve-Bayes, decision tree | Accuracy and sensitivity of SVM were the highest (97.6% and 98.3%, respectively) for both datasets |
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| [63] | 2018 | UMA dataset | Accelerometer, gyroscope, magnetometer | Wrist, waist, chest, ankle | kNN, Naïve-Bayes, SVM, ANN, decision tree | Without risk categorization: 81% for decision tree
With risk categorization: 85% for decision tree |
| [56] | 2019 | Public datasets | Accelerometer | Not specified | CNN-based models for feature extraction | Highest accuracy reported = 99.86% |
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| [57] | 2018 | SisFall dataset-original and manually labelled | Accelerometer | Not specified | RNN | Highest accuracy reported for fall detection: 83.68% (before manual labelling), 98.33% (after manual labelling) |
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| [36] | 2018 | Generated from experiments | Accelerometer, gyroscope, magnetometer | Near the waist | kNN | Accuracy = 99.4% |
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| [16] | 2018 | Generated from experiments | Accelerometer | Waist | Decision tree | Accuracy = 91.67%
Precision = 93.75% |
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| [54] | 2018 | SisFall dataset | Accelerometer | Waist | RNN with LSTM | Highest accuracy (after hyperparameter optimization) = 97.16% |
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| [53] | 2017 | Generated from experiments | Accelerometer, gyroscope, proximity sensor, compass | Right, left, and front pockets | SVM, decision tree, kNN, discriminant analysis | Highest accuracy = 99% for SVM |
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| [59] | 2018 | Generated from experiments | Depth camera, accelerometer | Waist | CNN | Accuracy of fall detection = 100% |
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| [55] | 2017 | Public datasets | Accelerometer | Not specified | CNN-based analysis on time series accelerometer data converted to images | Accuracy = 92.3% |
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| [58] | 2017 | Generated from experiments | Accelerometer, radar, depth camera | Wrist | Ensemble subspace discriminant, linear discriminant, kNN, SVM | Overall accuracy of ensemble classifier was the highest, after fusion of radar, accelerometer, and camera = 91.3%. This is an improvement of 11.2% compared to radar-only and 16.9% compared to accelerometer-only results |
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| [62] | 2018 | Generated from experiments | Accelerometer, gyroscope, magnetometer | Hip | SVM, random forest | Without sensor fusion:
Accelerometer precision = 86.23%
Accelerometer recall = 87.46%
With sensor fusion: precision = 94.78%, recall = 94.37%, with random forest |
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