Extraction of Daily Life Log Measured by Smart Phone Sensors Using Neural Computing

AbstractThis paper deals with the information extraction of daily life log measured by smart phone sensors. Two types of neural computing are applied for estimating the human activities based on the time series of the measured data. Acceleration, angular velocity, and movement distance are measured by the smart phone sensors and stored as the entries of the daily life log together with the activity information and timestamp. First, growing neural gas performs clustering on the data. Then, spiking neural network is applied to estimate the activity. Experiments are performed for verifying the effectiveness of the proposed method

Hybrid evolutionary neuro-fuzzy approach based on mutual adaptation for human gesture recognition

One of the most important techniques in human-robot communication is gesture recognition. If robots can read intentions from human gestures, the communication process will be smoother and more natural. Processing for gesture recognition typically consists of two parts: Feature extraction and gesture classification. In most works, these are independently designed and evaluated by their own criteria. This paper proposes a hybrid approach based on mutual adaptation for human gesture recognition. We use a neuro-fuzzy system (NFS) for the classification of human gesture and apply an evolution strategy for parameter tuning and pruning of membership functions. Experimental results indicate the effectiveness of mutual adaptation in terms of the generalization