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- W2967448307 abstract "Recent advances in home automation have provided convenience for individuals with their daily tasks. Despite the added convenience to an individual, those afflicted with specific motor disabilities cannot take advantage of these advances. This paper presents a home automation system, based on surface electromyography (sEMG) readings from the occipitalis region, to control smart devices with an aim towards scalability. The system can be potentially utilized by individuals with a language disability or speech impairment such as the symptoms caused by bulbar onset amyotrophic lateral sclerosis (ALS). The system utilizes an Ultracortex Mark IV headset, an OpenBCI Cyton board, a computer with the Processing IDE, and an Amazon Alexa connected to smart home devices. Code run in the Processing IDE provides an on-screen control panel keypad to represent various smart devices, a text-to-speech implementation for interfacing with the Amazon Alexa, and functions that compute and classify data received from the Cyton board. Three subjects tested the system using five smart devices: a smart bulb, three smart plugs, and a smart lock. The results yielded an 84% average accuracy from 75 trials without a machine learning algorithm and a 96% average accuracy from 75 trials with the K-Nearest Neighbors algorithm." @default.
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- W2967448307 date "2018-11-01" @default.
- W2967448307 modified "2023-10-05" @default.
- W2967448307 title "Scalable Smart Home Interface using Occipitalis sEMG Detection and Classification" @default.
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- W2967448307 doi "https://doi.org/10.1109/uemcon.2018.8796615" @default.
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