Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments.

Personalized emotion recognition provides an individual training model for each target user in order to mitigate the accuracy problem when using general training models collected from multiple users. Existing personalized speech emotion recognition research has a cold-start problem that requires a large amount of emotionally-balanced data samples from the target user when creating the personalized training model. Such research is difficult to apply in real environments due to the difficulty of collecting numerous target user speech data with emotionally-balanced label samples. Therefore, we propose the Robust Personalized Emotion Recognition Framework with the Adaptive Data Boosting Algorithm to solve the cold-start problem. The proposed framework incrementally provides a customized training model for the target user by reinforcing the dataset by combining the acquired target user speech with speech from other users, followed by applying SMOTE (Synthetic Minority Over-sampling Technique)-based data augmentation. The proposed method proved to be adaptive across a small number of target user datasets and emotionally-imbalanced data environments through iterative experiments using the IEMOCAP (Interactive Emotional Dyadic Motion Capture) database.

Distance-Constraint k-Nearest Neighbor Searching in Mobile Sensor Networks.

The κ-Nearest Neighbors ( κNN) query is an important spatial query in mobile sensor networks. In this work we extend κNN to include a distance constraint, calling it a l-distant κ-nearest-neighbors (l-κNN) query, which finds the κ sensor nodes nearest to a query point that are also at or greater distance from each other. The query results indicate the objects nearest to the area of interest that are scattered from each other by at least distance l. The l-κNN query can be used in most κNN applications for the case of well distributed query results. To process an l-κNN query, we must discover all sets of κNN sensor nodes and then find all pairs of sensor nodes in each set that are separated by at least a distance l. Given the limited battery and computing power of sensor nodes, this l-κNN query processing is problematically expensive in terms of energy consumption. In this paper, we propose a greedy approach for l-κNN query processing in mobile sensor networks. The key idea of the proposed approach is to divide the search space into subspaces whose all sides are l. By selecting κ sensor nodes from the other subspaces near the query point, we guarantee accurate query results for l-κNN. In our experiments, we show that the proposed method exhibits superior performance compared with a post-processing based method using the κNN query in terms of energy efficiency, query latency, and accuracy.

A framework for supervising lifestyle diseases using long-term activity monitoring.

Activity monitoring of a person for a long-term would be helpful for controlling lifestyle associated diseases. Such diseases are often linked with the way a person lives. An unhealthy and irregular standard of living influences the risk of such diseases in the later part of one's life. The symptoms and the initial signs of these diseases are common to the people with irregular lifestyle. In this paper, we propose a novel healthcare framework to manage lifestyle diseases using long-term activity monitoring. The framework recognizes the user's activities with the help of the sensed data in runtime and reports the irregular and unhealthy activity patterns to a doctor and a caregiver. The proposed framework is a hierarchical structure that consists of three modules: activity recognition, activity pattern generation and lifestyle disease prediction. We show that it is possible to assess the possibility of lifestyle diseases from the sensor data. We also show the viability of the proposed framework.