Supervised machine learning based behavioral pattern recognition for autonomous non-invasive smart switch actuation using a Fingerbot system

Anas, Siti Aisyah and Mohd Azlini, Nur Amirah Syuhada and Jaafar, Anuar and Mohd Kasim, Noor Shahida and Darsono, Abd Majid and Mohd Yusof, Haziezol Helmi (2026) Supervised machine learning based behavioral pattern recognition for autonomous non-invasive smart switch actuation using a Fingerbot system. International Journal of Research and Innovation in Social Science (IJRISS), X (VI). pp. 1671-1679. ISSN 2454-6186

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Abstract

The proliferation of smart home technologies has intensified demand for adaptive, low-cost automation solutions deployable without altering existing electrical infrastructure. This paper presents an AI-driven Fingerbot, a noninvasive electromechanical actuator that physically operates conventional wall switches through a micro servo motor governed by an ESP32-C3 microcontroller. Unlike solutions that require electrical rewiring or hardware replacement, the device mounts externally on standard switches and automates their operation through learned behavioral patterns. Three supervised classifiers; Logistic Regression, Decision Tree, and Random Forest were trained on 1,440 time-stamped user interaction records capturing hour, minute, day of week, and weekend indicator features to predict binary switch states. The Decision Tree classifier achieved 99.7% accuracy, 99.6% precision, and 99.4% recall. Random Forest recorded 97.1% accuracy with the highest precision of 99.8%, while Logistic Regression served as a linear baseline at 87.9% accuracy. Response time evaluation across three control modes showed that physical switch interaction averaged 311.67 ms (maximum 400 ms), Wi-Fi virtual control averaged 2,597.33 ms (maximum 3,750 ms), and mobile hotspot control averaged 3,257.67 ms (maximum 5,750 ms). The embedded AI activation logic commenced switch-on at approximately between 18:35 to 18:45 and enforced mandatory deactivation at 23:59 to minimize unnecessary energy consumption. Monthly energy monitoring of a 25 W test load yielded figures ranging from 121.80 kWh to 143.53 kWh across three consecutive months. The results confirm that integrating lightweight machine learning with low-cost actuator hardware constitutes a practical and cost-effective pathway for retrofit smart home automation.

Item Type:	Article
Uncontrolled Keywords:	Smart home automation, Fingerbot actuator, Supervised machine learning, IoT, ESP32-C3, Behavioral pattern learning
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	13 Jul 2026 07:30
Last Modified:	13 Jul 2026 07:30
URI:	http://eprints.utem.edu.my/id/eprint/30206
Statistic Details:	View Download Statistic

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