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Reference | Year | Dataset used | Sensors used | Sensor placement | Methodology | Observed performance |
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[21] | 2009 | Generated from experiments | Accelerometer, gyroscope | Chest, thigh | Three-step algorithm based on activity intensity analysis, posture analysis, and transition analysis, based on signals reported by accelerometer and gyroscope | Sensitivity = 91% Specificity = 92% |
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[18] | 2011 | Generated from experiments | 3D accelerometer | Not specified | Algorithm based on first differences and first derivatives of sum of accelerometer readings along X, Y, and Z directions | Algorithm is reliable, simple, and real time |
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[19] | 2015 | Generated from experiments | Accelerometer | Waist | Quaternion algorithm using sum acceleration and angle information | Better sensitivity and specificity than threshold-based algorithms |
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[20] | 2015 | Generated from experiments | Accelerometer, HRV sensor | Not specified | Analysis of signals from accelerometer for movement detection and HRV sensor for stress detection | Accuracy = 96% to 100% (depending on the type of movement) |
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[25] | 2017 | Generated from experiments | 3-Axis accelerometer, 3-axis gyroscope, 3-axes magnetometer | Shoulder, waist, and foot | Threshold-based method, applied to acceleration and Euler’s angle (yaw, pitch, and roll), run on a mobile phone | Accuracy = 100% Specificity = 91.1% (shoulder), 100% (waist), 78.5% (foot) Sensitivity = 100% (for all three placements) |
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[27] | 2017 | Generated from experiments | G-force sensor | Smartphone | 3-Phase detection based on thresholds to identify falls and smartphone drops | Specificity = 72% when compared to the specificity of 31% with 2-phase threshold-based algorithm |
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[23] | 2017 | Generated from experiments | MEMS accelerometers, RF signals | Waist + network of fixed motes within the home | Signal analysis based on threshold-based methods | Not specified |
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