Speakers: Zelun Zhang & Thomas Oshin
Title (Zelun Zhang): Fine-Grained Transportation Mode Recognition Using Mobile Phones and Foot Force Sensors
Abstract:
Transportation or travel mode recognition plays an important role in enabling us to derive transportation profiles, e.g., to assess how eco-friendly our travel is, and to adapt travel information services such as maps to the travel mode. However, current methods have two key limitations: low transportation mode recognition accuracy and coarse-grained transportation mode recognition capability. In this paper, we propose a new method which leverages a set of wearable foot force sensors in combination with the use of a mobile phone’s GPS (FF+GPS) to address these limitations. The transportation modes recognized include walking, cycling, bus passenger, car passenger, and car driver. The novelty of our approach is that it provides a more fine-grained transportation mode recognition capability in terms of reliably differentiating bus passenger, car passenger and car driver for the first time. Result shows that compared to a typical accelerometer-based method with an average accuracy of 70%, the FF+GPS based method achieves a substantial improvement with an average accuracy of 95% when evaluated using ten individuals.
Title (Thomas Oshin): A Framework for Energy-Efficient Real-Time Human Mobility State Profiling Using Smartphones.
Abstract:
Smartphone sensors such as the accelerometer have the ability to capture real-time contextual information. I will discuss my research into a probabilistic algorithm that can overcome the variations in the embedded smartphone accelerometer readings and allow human movements to be more accurately identified. The main focus is the challenge of using real-time smartphone accelerometer sensor data to better optimize the management of the energy consumption needed for location determination and mobility profiling. Using solely the embedded smartphone accelerometer the framework can within 2 seconds identify the user mobility state. Results show that the smart mobility sensing framework can achieve energy-savings of up to 70% in typical circumstances.
Date: 5th December, 2012.
Time: 14.00-15.00 hrs
Venue: QMUL Maths:1.03