Design and Usability of a Smart Home Sensor Data User Interface for a Clinical and Research Audience

Smart Home System bertujuan memaksimalkan pengawasan, pemantauan, keamanan dan sebagainya. sistem ini terintegrasi dari telekomunikasi dan sistem pengendali dari mikrokontroller, sehingga tercipta Internet Of Things. Pada Penelitian ini dilakukan perancangan sistem Smart Home, dengan sistem client-server berbasis NodeMCU ESP8266 v3 dengan user interface Telegram Messenger yang melakukan komunikasi data melalui wireless. Tahapan perancangan terdiri dari perancangan server, interface, serta sistem kendali Smart Home nya. Hasil akhir pengujian tersebut dapat disimpulkan Aplikasi Telegram Messenger sangat cocok untuk pengontrol dan monitoring Smart Home jarak jauh, berdasarkan Jarak yang diukur dari 1,7 km sampai 151 km area beda wilayah didapatkan delay rata-rata 20,66 detik, Pada pengujian kinerja Quality of Service dalam sistem komunikasi data ini, berdasarakan standarisasi paramater hasil pengujian bekerja dengan sangat baik. Pada pengujian nilai RSSI indoor didapat bahwa kekuatan komunikasi wireless lebih baik dibanding outdoor, sehingga RSSI nya lebih kuat. Nilai RSSI yang tertinggi berada pada -28 dBm dan yang terkecil pada -88 dBm. Berdasarkan pengujian terhadap obstacle, dengan karakteristik redaman yang berbeda - beda dari tiap obstacle nya menghasilkan pengaruh terhadap RSSI dari sinyal wirelessnya. Obstacle RSSI terkuat dihasilkan oleh pintu kayu dengan nilai -33dbm dBm , serta RSSI terkecil pada obstacle 2 bangunan rumah dengan nilai -78 dBm.

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Experiences of Designing a Speech User Interface for a Smart Home Environment

Usability of Speech Dialog Systems - Signals and Commmunication Technologies ◽

10.1007/978-3-540-78343-5_2 ◽

2008 ◽

pp. 41-66 ◽

Cited By ~ 3

Author(s):

Jan Krebber ◽

Rosa Pegam

Keyword(s):

User Interface ◽

Home Environment ◽

Smart Home ◽

Smart Home Environment ◽

Speech User Interface

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Temporal-Sound based User Interface for Smart Home

10.5121/csit.2021.112107 ◽

2021 ◽

Author(s):

Kido Tani ◽

Nobuyuki Umezu

Keyword(s):

Machine Learning ◽

User Interface ◽

Smart Home ◽

Classification Learning ◽

Activity Classification ◽

Smart Watch ◽

Acceleration Data ◽

Physical Controls ◽

The Difference

We propose a gesture-based interface to control a smart home. Our system replaces existing physical controls with our temporal sound commands using accelerometer. In our preliminary experiments, we recorded the sounds generated by six different gestures (knocking the desk, mouse clicking, and clapping) and converted them into spectrogram images. Classification learning was performed on these images using a CNN. Due to the difference between the microphones used, the classification results are not successful for most of the data. We then recorded acceleration values, instead of sounds, using a smart watch. 5 types of motions were performed in our experiments to execute activity classification on these acceleration data using a machine learning library named Core ML provided by Apple Inc.. These results still have much room to be improved.

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Anomaly Detection Using Temporal Data Mining in a Smart Home Environment

Methods of Information in Medicine ◽

10.3414/me9103 ◽

2008 ◽

Vol 47 (01) ◽

pp. 70-75 ◽

Cited By ~ 54

Author(s):

V. Jakkula ◽

D. J. Cook

Keyword(s):

Machine Learning ◽

Smart Home ◽

Learning Algorithms ◽

Synthetic Data ◽

Machine Learning Algorithms ◽

Sensor Data ◽

Temporal Data Mining ◽

Activity Data ◽

Real Activity ◽

Smart Home Environment

Summary Objectives: To many people, home is a sanctuary. With the maturing of smart home technologies, many people with cognitive and physical disabilities can lead independent lives in their own homes for extended periods of time. In this paper, we investigate the design of machine learning algorithms that support this goal. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a smart home, and that the results can be used to perform automated health monitoring and to detect anomalies. Methods: Specifically, our algorithms draw upon the temporal nature of sensor data collected in a smart home to build a model of expected activities and to detect unexpected, and possibly health-critical, events in the home. Results: We validate our algorithms using synthetic data and real activity data collected from volunteers in an automated smart environment. Conclusions: The results from our experiments support our hypothesis that a model can be learned from observed smart home data and used to report anomalies, as they occur, in a smart home.

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