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Human motion detection is getting considerable attention in the field ofArtificial Intelligence (AI) driven healthcare systems. Human motion can beused to provide remote healthcare solutions for vulnerable people byidentifying particular movements such as falls, gait and breathing disorders.This can allow people to live more independent lifestyles and still have thesafety of being monitored if more direct care is needed. At present wearabledevices can provide real time monitoring by deploying equipment on a person'sbody. However, putting devices on a person's body all the time make ituncomfortable and the elderly tends to forget it to wear as well in addition tothe insecurity of being tracked all the time. This paper demonstrates how humanmotions can be detected in quasi-real-time scenario using a non-invasivemethod. Patterns in the wireless signals presents particular human body motionsas each movement induces a unique change in the wireless medium. These changescan be used to identify particular body motions. This work produces a datasetthat contains patterns of radio wave signals obtained using software definedradios (SDRs) to establish if a subject is standing up or sitting down as atest case. The dataset was used to create a machine learning model, which wasused in a developed application to provide a quasi-real-time classification ofstanding or sitting state. The machine learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross validation. Abenchmark dataset of wearable devices was compared to the proposed dataset andresults showed the proposed dataset to have similar accuracy of nearly 90 %.The machine learning models developed in this paper are tested for twoactivities but the developed system is designed and applicable for detectingand differentiating x number of activities.

An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare