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Article | Year | Basis | Deployed sensors | Deployed algorithm | Evaluation | Performance |
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Felisberto et al. [42] | 2014 | Movement monitoring, accident detection based on sensor fusion | Accelerometer, gyroscope, magnetometer | Fuzzy logic + extended Kalman filter, direct cosine matrix (DCM), control algorithm | Movement state, Orientation state experiment with precollected data | Passing average: 84% |
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Doukas and Maglogiannis [43] | 2008 | Fall detection based on movement/sound data | Accelerometer, microphones | Support Vector Machine (SVM) | 2 volunteers: (a) Simple walk (b) Walk and fall (c) Walk and run | All fall events successfully detected Run events: 96.72% |
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Bianchi et al. [44] | 2010 | Falls event detection with barometric pressure and triaxial accelerometer | Accelerometer, air pressure sensor | Heuristically trained decision tree classifier | 20 healthy volunteers: falls/ADL simulation | Accuracy: 96.9% Sensitivity: 97.5% Specificity: 96.5% |
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Lustrek et al. [45] | 2011 | Fall detection with accelerometer and location sensor | Accelerometer, location tags | Rule-based reasoning | 10 healthy volunteers, specific scenario | Methods utilized both context/accelerometer. Accuracy increase: 40% |
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Yi et al. [46] | 2014 | Wearable sensor data fusion for fall detection | Temperature, accelerometer ECG sensor | Data is processed individually and combined into alert message | No evaluation provided | Human postures successfully recognized. Full evaluation is not performed |
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Tolkiehn et al. [47] | 2011 | Fall detection with accelerometer and barometric pressure sensor | Accelerometer, barometric pressure sensor | Feature extraction, thresholding combination | 12 healthy volunteers ADL/fall simulation, 297 data sequences | Fall identification accuracy: 94.12% |
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Greene et al. [48] | 2012 | Falls risk estimation through multisensor assessment of standing balance | Pressure sensor (platform), body-worn inertial sensor | SVM | 120 community dwelling older adults | Classification accuracy: 71.52% |
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