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This paper proposes a novel Wi-Fi based indoor localization system. Specially designed Wi-Fi beacons are set up to detect the real time signal strengths of Wi-Fi access points and send this data to a server. By establishing the distances along flat planes between beacons and a mobile tag, the location of the mobile tag is estimated by finding the most likely intersection between the planes corresponding...
This paper presents our recent work on the analyses of smart phone sensor data collected for the human activity recognition (HAR), with the objective to develop more accurate activity recognition systems independent of smart phone models. We identify the multi-device scenario and present the impairments of different smartphone embedded sensor models on HAR applications. Outlier removal, interpolation,...
This paper presents our recent work on human activity detection based on smart phone sensors and incremental clustering algorithms. The proposed unsupervised (clustering) activity detection scheme works in an incremental manner, which contains two stages. In the first stage, streamed sensor data will be processed. A single-pass clustering algorithm is used in order to generate pre-clustered results...
This paper presents our recent work on human activity detection based on smart phone embedded sensors and learning algorithms. The proposed human activity detection system recognizes human activities including walking, running, and sitting. While walking and running can be recorded as daily fitness activities, falling will also be detected as anomalous situations and alerting messages can be sent...
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