Abstract
This work implements a fall and posture detection system exploiting low cost sensors and applying machine learning to aid people in need both at indoor and outdoor. This intelligent system is able to identify fall with and without recovery within a stipulated period of time. In case of fall without recovery, an alert message along with date, time and location of fall is sent to relative/caregiver. This feature ensures real time assistance to avoid any criticality due to delay. In addition to this, an immediate last posture before the fall is also notified to identify the proneness of a person towards fall from a specific posture. This may aid clinical persons to take appropriate measures to prevent the future fall. The system is also able to take care of an unresponsive device after a fall (if any). We have designed and implemented this intelligent live fall with posture detection system, exploiting the sensors in micro processor unit (MPU) 6050 combined with low cost ESP 8266 micro-controller unit (MCU) using WiFi connectivity. The kinematic sensor data is collected at a rate of 40 Hz using accelerometer and gyroscope.The result shows that the system can identify the location and posture of the subject on regular interval along with the date and time of fall (if any). The emergency help system is aided with an audio-visual warning at the raspberry Pi based monitoring station along with a facility of sending the distress SMS.The system can operate either in manual or in auto mode. The dataset is prepared from local people of varied age groups (between 10 and 70 years) of both the genders.The system is tested randomly on 10 volunteers with an overall detection accuracy upto 98%.
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Bhattacharjee, P., Biswas, S. Smart walking assistant (SWA) for elderly care using an intelligent realtime hybrid model. Evolving Systems 13, 265–279 (2022). https://doi.org/10.1007/s12530-021-09382-5
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DOI: https://doi.org/10.1007/s12530-021-09382-5