Elderly population (over the age of 60) is predicted to be 1.2 billion by 2025. Most of the elderly people would like to stay alone in their own house due to the high eldercare cost and privacy invasion. Unobtrusive activity recognition is the most preferred solution for monitoring daily activities of the elderly people living alone rather than the camera and wearable devices based systems. Thus, we propose an unobtrusive activity recognition classifier using Deep Convolutional Neural Network (DCNN) and anonymous binary sensors (PIR sensors and door sensors). The real world long-term fully annotated Aruba open dataset collected by binary sensors has been employed for the training and evaluation of the classifier. First, ten basic daily activities, namely: Bed_to_Toilet, Eating, Meal_Preparation, Relax, Sleeping, Work, Housekeeping, Wash_Dishes, Enter_Home, and Leave_Home are segmented with different sliding window sizes, and then converted into binary activity images. Next, the activity images are employed for training the DCNN classifiers with different parameters. Finally, the trained classifiers are evaluated with the 10-fold cross validation method, and the results show that the best DCNN classifier gives 0.79 and 0.951 of F1-score for all 10 activities and eight activities (excluding Leave_Home and Wash_Dishes), respectively.

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Current environmental concerns have led to a search of more environmentally friendly manufacturing methods; thus, natural fibers have gained attention in the 3D printing industry to be used as bio-filters along with thermoplastics. The utilization of natural fibers is very convenient as they are easily available, cost-effective, eco-friendly, and biodegradable. Using natural fibers rather than synthetic fibers in the production of the 3D printing filaments will reduce gas emissions associated with the production of the synthetic fibers that would add to the current pollution problem. As a matter of fact, natural fibers have a reinforcing effect on plastics. This review analyzes how the properties of the different polymers vary when natural fibers processed to produce filaments for 3D Printing are added. The results of using natural fibers for 3D Printing are presented in this study and appeared to be satisfactory, while a few studies have reported some issues.

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Single resident life style is increasing among the elderly due to the issues of elderly care cost and privacy invasion. However, the single life style cannot be maintained if they have dementia. Thus, the early detection of dementia is crucial. Systems with wearable devices or cameras are not preferred choice for the long-term monitoring. Main intention of this study is to propose Deep Convolutional Neural Network classifier (DCNN) for indoor travel patterns of elderly people living alone using open dataset collected by device-free non-privacy invasive binary (passive infrared) sensor data. Travel patterns are classified as direct, pacing, lapping, or random according to Martino-Saltzman (MS) model. MS travel pattern is highly related with person's cognitive state, thus can be used to detect early stage of dementia. We have utilized an open dataset that was presented by Center for Advanced Studies in Adaptive Systems (CASAS) project, Washington State University. The dataset was collected by monitoring a cognitively normal elderly person by wireless passive infrared sensors for 21 months. First, 117320 travel episodes are extracted from the dataset and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing dataset. Finally, DCNN performance was compared with seven other classical machinelearning classifiers. The Random Forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching. Index Terms-non-privacy invasive, deep learning, device-free, assistive technology, smart house, travel pattern, elder care. I. INTRODUCTION CCORDING to statistics, the number of people who live alone at home [1]-[6] and the number of single-resident houses [6] are increasing worldwide, and the global elderly population (over 60 years) is estimated to be 1.2 billion T his study was partially supported by Ministry of Science and Technology of the Republic of China (Taiwan) under the Contract No.

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