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Internet of Things (IoT) enabled architecture-based devices are becoming accessible worldwide irrespective of the area. But functional settings depend on Internet facilities. In this context, the Healthcare industry took a step forward to automate Human Activity Recognition related concepts using IoT and Machine learning methods. This research used a Nodemcu ESP8266 device to track and communicate human activities acquired using ADXL345 accelerometer sensors. Three volunteers participated in this research, and data were acquired using two accelerometer sensors placed on the hand, wrist, and ankle. Data shared to the cloud- thingspeak.com. Acquired data were analyzed and trained with the Random Forest algorithm and tested with the data, achieving 100% accuracy. This model can be helpful in various applications like elderly patient monitoring, I.C.U., dementia, Alzheimer's, etc.
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