Naoki Iketani

Human Activity Recognizer for Mobile Devices with Multiple Sensors

This paper presents a novel human activity recognizer used to estimate a users activities with sensors on a widespread consumer mobile device. Our recognizer can estimate a users means of migration by using a combination of an acceleration sensor and a GPS. We evaluate the accuracy rate of the estimation with cellular phones carried freely and the result is 90.6 percent even in the case of intermittent working mode for lower power consumption.

BDI Model-Based Crowd Simulation

We present a new approach for crowd simulation in which a BDI (Belief-Desire-Intention) model is introduced that makes it possible for a character in a simulated environment to work adaptively. Our approach allows the character to realize realistic behavior by adapting its action with the sensed information in a changing environment. We implemented a demo system simulating the BDI model-based NPCs that extinguishes a forest fire with a 3D game engine, Source Engine. We measured the performance to evaluate the scalability and the bottleneck of the system.

Acceleration Noise Correction for Transfer Inference Using Accelerometers on Mobile Devices

The acceleration noise generated in the ordinary usage of mobile devices (e.g. when “taking out” the devices or operating them) interferes with estimation of the means of migration using accelerometers on the devices. We developed a correction method for the noise generated when users take out devices, change their posture, and operate the devices. The method uses the changes of acceleration and the operation events acquired from the operating systems of the mobile devices to detect the period of noises. The result of evaluation shows that the method using the acceleration changes improves the precision of the context inference approximately 5 %, and the method using the operation events corrects the inference mistaking resting for boarding.