Research Article
Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano
Table 1
Fall detection techniques.
| Author | Datasets | No. of subjects (age) | Sensor | Algorithms |
| Saleh and Jeannès [28] | Simulated | 23 (19–30), 15 (60–75) | Accelerometer (waist) | SVM | Zitouni et al. [29] | Simulated | 6 (N/A) | Accelerometer (sole) | Threshold | Wu et al. [30] | Public (simulated) | 42 (N/A), 36 (N/A) | Accelerometer (chest and thigh) | Decision tree | Huang et al. [31] | Simulated | 12 (19–29) | Vibration | HMM | Tian et al. [32] | Simulated | 140 (N/A) | FMCW radio | CNN | Wang et al. [33] | Simulated | N/A | WiFi | SVM, Random Forests | Kerdjidj et al. [34] | Simulated | 17 (N/A) | Accelerometer, gyroscope | Compressive sensing | Queralta et al. [35] | Public (simulated) | 57 (20–47) | Accelerometer, gyroscope, magnetometer | LTSM | Han et al. [36] | Simulated | N/A | Web camera | CNN | Kong et al. [37] | Public | Public | Camera (surveillance) | CNN | Ko et al. [38] | Simulated | N/A | Camera (smartphone) | Rao-Blackwellized particle filtering | Shojaei-Hashemi et al. [39] | Public (simulated) | 40 (10–15) | Kinect | LSTM | Min et al. [40] | Public (simulated) | 4 (N/A), 11 (22–39) | Kinect | SVM | Ozcan et al. [41] | Simulated | 10 (24–31) | Web camera | Relative-entropy-based |
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