AI-Aided Individual Emergency Detection System in Edge-Internet of Things Environments
Abstract
:1. Introduction
2. Related Work
3. Edge-Based Individual Monitoring System
3.1. E-IMS Design
3.1.1. System Architecture and Components
3.1.2. Framework and Operational Steps
3.1.3. Detector and Discriminator of User Activities
3.1.4. Localization in User Areas
4. Machine-Learning-Aided and Edge-Based User Monitoring
4.1. Activity Sensing
4.2. Localization
5. Performance Evaluations
5.1. Experimental Design
5.2. Experimental Results
6. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
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References | Application Area | Type of Sensor Data | Device and Equipment | Type of Activity Sensing | Service Type |
---|---|---|---|---|---|
[12] | User activity sensing | Channel statement information | Wi-Fi AP, smartphone | Occupancy | Real-time |
[13] | Light sensor | Networked LED light bulb, machine learning | |||
[14] | Light sensor, accelerometer | Smartphone | Walking, running, etc. | Static | |
[15,16,17] | Accelerometer, gyroscope, magnetometer | Smartphone, machine learning | |||
[18] | Accelerometer, gyroscope, magnetometer, linear acceleration sensor, gravity sensor | Walking, running,sitting, standing,going up/down the stairs | |||
[19] | Magnetometer | Smartphone, machine learning | Car in/out for user | Real-time | |
[20,21,22] | Localization | Wi-Fi RSSI signals | Wi-Fi AP, smartphone, machine learning | Indoor location | Beacon frame-based positioning |
[10] | Wi-Fi AP, smartphone | Outdoor location | Probe request-based positioning | ||
[23,24,25] | Wi-Fi AP, smartphone, laptops, machine learning | Indoor location |
MLP | MLP with DAE | SVM | CNN | |||||
---|---|---|---|---|---|---|---|---|
Training | Test | Training | Test | Training | Test | Training | Test | |
Walk&Pocket | 99.58% | 98.47% | 91.48% | 89.50% | 88.32% | 88.70% | 89.45% | 89.76% |
Walk&Hand | 100% | 100% | 99.74% | 99.43% | 99.47% | 99.22% | 99.14% | 99.30% |
Walk&Use | 100% | 100% | 99.25% | 99.44% | 100% | 100% | 99.92% | 99.30% |
Run&Pocket | 100% | 98.47% | 90.21% | 90.43% | 85.25% | 84.64% | 84.30% | 84.48% |
Run&Hand | 100% | 99.73% | 98.14% | 98.58% | 99.04% | 99.29% | 98.31% | 98.45% |
Sit-Down&Hand | 95.64% | 95.57% | 88.39% | 88.63% | 96.38% | 95.90% | 93.86% | 94.05% |
Stand-Up&Hand | 100% | 100% | 100% | 100% | 99.98% | 99.96% | 99.64% | 99.60% |
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Yang, T.; Lee, S.-H.; Park, S. AI-Aided Individual Emergency Detection System in Edge-Internet of Things Environments. Electronics 2021, 10, 2374. https://doi.org/10.3390/electronics10192374
Yang T, Lee S-H, Park S. AI-Aided Individual Emergency Detection System in Edge-Internet of Things Environments. Electronics. 2021; 10(19):2374. https://doi.org/10.3390/electronics10192374
Chicago/Turabian StyleYang, Taehun, Sang-Hoon Lee, and Soochang Park. 2021. "AI-Aided Individual Emergency Detection System in Edge-Internet of Things Environments" Electronics 10, no. 19: 2374. https://doi.org/10.3390/electronics10192374