System for Detection of Emergency Situations in Smart City Environments Employing Smartphones

García, Laura; Parra, Lorena; Taha, Miran; Lloret, Jaime

doi:10.1109/icacci.2018.8554654

Cited by 6 publications

(1 citation statement)

References 18 publications

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“…The energy consumption of the wearable sensor nodes in different configurations was evaluated. Using the data from the accelerometer of the smart phones, L Garcı´a et al 21 detected changes in the behavior of users considering the x/y/z axial accelerometer while the smart phone is in pocket and in hand. Considering the user's acceptability issue, L Montanini et al 22 presented a methodology for fall detection that relied on a pair of smart shoes that was equipped with force sensors and a triaxial accelerometer to detect a fall and notify it to a supervising system.…”

Section: Introductionmentioning

confidence: 99%

The real-time elderly fall posture identifying scheme with wearable sensors

Sun

et al. 2019

International Journal of Distributed Sensor Networks

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The accidental fall is the major risk for elderly especially under unsupervised states. It is necessary to real-time monitor fall postures for elderly. This paper proposes the fall posture identifying scheme with wearable sensors including MPU6050 and flexible graphene/rubber. MPU6050 is located at the waist to monitor the attitude of the body with triaxial accelerometer and gyroscope. The graphene/rubber sensors are located at the knees to monitor the moving actions of the legs. A real-time fall postures identifying algorithm is proposed by the integration of triaxial accelerometer, tilt angles, and the bending angles from the graphene/rubber sensors. A volunteer is engaged to emulate elderly physical behaviors in performing four activities of daily living and six fall postures. Four basic fall down postures can be identified with MPU6050. Integrated with graphene/rubber sensors, two more fall postures are correctly identified by the proposed scheme. Test results show that the accuracy for activities of daily living detection is 93.5% and that for fall posture identifying is 90%. After the fall postures are identified, the proposed system transmits the fall posture to the smart phone carried by the elderly via Bluetooth. Finally, the posture and location are transmitted to the specified mobile phone by short message.

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Section: Introductionmentioning

confidence: 99%