In this study, we have proposed a novel Feature Sequence representation for characterizing speech data from patients with Alzheimer’s disease in a neuropsychological test scenario. An Alzheimer Disease Assessment Engine based on a bidirectional recurrent neural network with gated recurrent unit is trained to carry out the classification. Moreover, we have shown that the Feature Sequence can be generated automatically with the help of the Feature Sequence Generator, which is based on a deep convolutional recurrent neural network trained with the connectionist temporal classification loss. Cross validating with a total of 120 samples, which half of them were from the cognitive healthy subjects and the others were from the Alzheimer’s disease subjects, an area under the receiver operating characteristic curve (AUROC) score of 0.838 is achieved.
User-driven Demand-side Management in Mixed-use Community under Dynamic Sharing Price Mechanism using Particle Swarm Optimization
Demand-side management ability in a residential area has been improved a lot under the enforcement of real-time pricing mechanism. In order to utilize the energy efficiency in a residential area and improve the energy quality in whole community. In this work, we aim to integrate the residential users and commercial users into one DSM system. By integrating them, we can utilize the energy more efficiency by sharing the surplus energy in a residential area to the commercial area. The result shows that, in the residential user, the cost is 44.24% than purl residential community on average. In addition, the commercial user also reduces the energy cost. The office user reduces 22.39% and the restaurant user reduces 13.66% on average.
A Supporting System for Quick Dementia Screening Using PIR Motion Sensor in Smart Home
We proposed a supporting system that can quickly estimate the likelihood for an elder of having dementia based on 2 to 4 hours monitoring of a behavioral test done by the elder. During the test, the elder only needs to perform certain activities selected from the Instrumental Activities of Daily Living (IADL) in a smart home environment, and their movement trajectories will be extracted from motion sensors and be analyzed to find potential correlation with the indoor wandering patterns. A machine learning algorithm is selected to carry out the classification task, based on our proposed features of the wandering patterns. Two datasets are employed for performance evaluation, where the first one is 232 elders including 7 dementias, whereas the second one is collected by ourselves from a senior center, which is 30 elders including 9 dementias. It turns out that the average precision and recall for the first dataset are both up to 98.3% with Area Under the ROC Curve (AUC-ROC) being 0.846, and those for the second dataset are 89.9% and 90.0% with AUC-ROC being 0.921.
Nonparametric Activity Recognition System in Smart Homes Based on Heterogeneous Sensor Data
We proposed an activity recognition (AR) system for the elder living independently in smart homes to achieve the concept of “aging in place.” The AR model adopted by the proposed system is powerful to recognize meaningful Activities of Daily Living (ADL) by integrating heterogeneous data from both ambient and on-body sensors. Moreover, the proposed system adopts a nonparametric approach, which requires much fewer efforts from humans. The average AR precision and recall rates of this proposed system are up to 98.7% and 99.0%, which indicates its feasibility of deployment in a real-life home environment for monitoring users' ADL with promising performance.
Demand-side Management in Residential Community Realizing Sharing Economy with Bidirectional Plug-in Electric Vehicle and Renewable Energy
In smart grids, demand-side management is one of the important function since it reduce the total electricity cost of each customer. On the view point of a community, we design a fairness strategy to share Plug-in Electric vehicles' battery with neighbors to reduce the total electricity cost and peak to average ratio. In our problem formulation, each home is assumed to be connected to a renewable energy resource, be equipped with an energy storage device, and have an optional Plug-in Electric vehicles with the vehicle to grid ability, and the formulation is in terms of a multi-objective optimization cooperative game to facilitate power sharing among negihbors. In the results, if Plug-in Electric vehicles come with a lower battery state, system reduce 43.3% of electricity cost.