An aging population is increasingly prevalent in both developed and developing countries, raising a series of social challenges and economic burdens. In particular, more elderly people are staying alone at home than are living with people who can take care of them. Therefore, assisted living (AL) and health-care monitoring (HM) can be critical issues in this era of human-centered artificial intelligence (AI). In this context, we aim to provide an encompassing review summarizing the state-of-the-art works combining AI and the Internet of Things (IoT) to help the elderly live easier and better. We systematically and comprehensively compare paradigms in terms of methodologies and application scenarios. The pros and cons among these technologies are discussed in detail. Then, we summarize current achievements and indicate their limitations. Finally, perspectives on highly promising future work are presented.

Depression is a serious and common psychiatric disease characterized by emotional and cognitive dysfunction. In addition, the rates of clinical diagnosis and treatment for depression are low. Therefore, the accurate recognition of depression is important for its effective treatment. Electroencephalogram (EEG) signals, which can objectively reflect the inner states of human brains, are regarded as promising physiological tools that can enable effective and efficient clinical depression diagnosis and recognition. However, one of the challenges regarding EEG-based depression recognition involves sufficiently optimizing the spatial information derived from the multichannel space of EEG signals. Consequently, we propose an adaptive channel fusion method via improved focal loss (FL) functions for depression recognition based on EEG signals to effectively address this challenge. In this method, we propose two improved FL functions that can enhance the separability of hard examples by upweighting their losses as optimization objectives and can optimize the channel weights by a proposed adaptive channel fusion framework. The experimental results obtained on two EEG datasets show that the developed channel fusion method can achieve improved classification performance. The learned channel weights include the individual characteristics of each EEG epoch, which can effectively optimize the spatial information of each EEG epoch via the channel fusion method. In addition, the proposed method performs better than the state-of-the-art channel fusion methods.

Depression is a severe psychiatric illness that causes emotional and cognitive impairment and has a considerable impact on patients’ thoughts, behaviors, feelings and well-being. Moreover, methods for recognizing and treating depression are lacking in clinical practice. Electroencephalogram (EEG) signals, which objectively reflect the internal workings of the brain, is a promising and objective tool for recognizing and diagnosing of depression and enhancing clinical effects. However, previous EEG feature extraction methods have not performed well when exploring the intrinsic characteristics of highly complex and non-stationary EEG signals. To address this issue, we propose a regularization parameter-based improved intrinsic feature extraction method of EEG signals via empirical mode decomposition (EMD), which mines the intrinsic patterns in EEG signals, for depression recognition. Furthermore, our method can effectively solve the problem that EMD fails to extract intrinsic features. In this method, we first select an appropriate regularization parameter to generate the regularization matrix. Next, we calculate the sum of the matrix products of the IMFs and the regularization matrix and leverage the inverse of this matrix to extract the intrinsic features. The classification results of our method on four EEG datasets reached 0.8750, 0.8850, 0.8485 and 0.7768, respectively. In addition, compared with the iEMD method, our method requires less computational costs. These results support our claim that our method can effectively strengthen the depression recognition performance, and our method outperforms state-of-the-art feature extraction approaches.