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The public health implications of growing numbers of older adults at risk for dementia places pressure on identifying dementia at its earliest stages so as to develop proactive management plans. The prodromal dementia phase commonly identified as mild cognitive impairment is an important target for this early detection of impending dementia amenable to treatment. In this paper, we propose a method for home-based automatic detection of mild cognitive impairment in older adults through continuous monitoring via unobtrusive sensing technologies. Our method is composed of two main stages: a training stage and a test stage. For training, room activity distributions are estimated for each subject using a time frame of ω weeks, and then affinity propagation is employed to cluster the activity distributions and to extract exemplars to represent the different emerging clusters. For testing, room activity distributions belonging to a test subject with unknown cognitive status are compared to the extracted exemplars and get assigned the labels of the exemplars that result in the smallest normalized Kullbak–Leibler divergence. The labels of the activity distributions are then used to determine the cognitive status of the test subject. Using the sensor and clinical data pertaining to 85 homes with single occupants, we were able to automatically detect mild cognitive impairment in older adults with an F0.5 score of 0.856. Also, we were able to detect the non-amnestic sub-type of mild cognitive impairment in older adults with an F0.5 score of 0.958.

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The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal behaviors observed during activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we investigate a variety of deep learning models such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM and Autoencoder-CNN-LSTM for identifying and accurately predicting the abnormal behaviors of elderly people. The temporal information and spatial sequences collected over time are used to generate models, which can be fitted to the training data and the fitted model can be used to make a prediction. We present an experimental evaluation of these models performance in identifying and predicting elderly persons abnormal behaviors in smart homes, via extensive testing on two public data sets, taking into account different models architectures and tuning the hyperparameters for each model. The performance evaluation is focused on accuracy measure.However, dependent persons are often exposed to problems of various types that may cause them to perform activities of daily living incorrectly. Detecting abnormal behaviors is thus of great importance for dependent people, in order to ensure that activities are performed correctly without errors [5]. This will also ensure their safety and well-being.Detecting an anomaly in a person's activities of daily living (ADL) is usually done by detecting nonconformities with their usual ADL patterns. Various authors have used classical machine learning algorithms to achieve this [6,7]. Tele-health care requires systems with high accuracy, low computational time and minimal user intervention because data are becoming larger and more complex [8]. Deep learning architectures provide a way to automatically extract useful and meaningful spatial and temporal features from raw data without the need for data labeling, which is time consuming, complex and error prone [9,10]. This makes deep learning models easily generalizable to different contexts. LSTM is a powerful deep learning model for sequence prediction and anomaly detection in sequential data [11]. The LSTM model is able to extract temporal features with long-term relationships. CNN has powerful spatial feature extraction capabilities and the ability to detect abnormality, which is considered as a classification problem. The advantage in the use of CNN with LSTM is to combine their capabilities in terms of spatial and temporal features extraction. Autoencoder learns a compressed input representation, which is recommended for the high dimensional data collected from smart home. Therefore, the motivation to select our deep learning models is due to their high performance accuracy in terms of abnormal behavior detection in different research areas [12,13].This propert...