AI-Assisted\nAutomated Identification of Human Activities\nUsing Ficus religiosa Leaf-Based Triboelectric\nNanogenerator

Movement monitoring and effective identification of different\nactions\nare the keys that help in fitness services, health status, clinical\nstudies, etc. In this technological era, Internet of Things (IoT)\ntechnologies, including smart wireless devices and sensors, are very\neffectively used for monitoring human activities, but the demand for\nsustainable and green power sources is a crucial issue with these\ndevices. Triboelectric nanogenerators (TENG) are proven to be promising\napplications in these devices because they harvest energy from the\nsurrounding environment and eliminate the use of batteries as power\nsources. As a green energy source, this study emphasizes the fabrication\nof biodegradable materials-based TENGs, which are eco-friendly and\nare related to clean and green energy as per the UN’s sustainable\ndevelopment goals SDG 7 (affordable and clean energy). In the present\nwork, a natural Ficus religiosa leaf\n(FRL) of the F. religiosa tree is used\nin designing and fabricating a TENG (FRL-TENG). Also, an approach\nis discussed to compare the performance of FRL-TENG with TENGs fabricated\nfrom other waste biodegradable materials such as garlic tunic, onion\ntunic, and eggshell membrane (ESM). During the experimental study,\nit is observed that the FRL-based TENG produced maximum voltage in\ncomparison to other material combinations selected in this study.\nThe generated electric output from these TENG combinations is also\nused to power an array of tens of green-light-emitting diodes (LEDs).\nFurthermore, this paper also proposes the use of FRL-TENG as a wearable\nsensor to collect information and monitor the physical activities\nof the user, viz., walk, jump, and run. To recognize the movement\nstatus, the FRL-TENG sensor is integrated with an extra randomized\ntree-based machine learning model for accurately distinguishing the\nuser’s three activities with an accuracy of 96%. The work showcases\nan innovative approach to encourage customized uses of TENG sensors\nin human motion monitoring and permits the development of intelligent,\nself-powered systems for new applications.