Although many elderly monitoring approaches either focus on activity or acoustic analysis, very limited work integrates both modalities within a unified framework for emergency detection. This paper presents a multi-modal method that combines skeleton-based activity with acoustic analysis to enhance the detection of medical emergencies in elderly individuals. A comprehensive pipeline is introduced, encompassing data preprocessing, feature extraction, multi-modal fusion, and emergency classification. Deep neural networks are leveraged to extract meaningful representations from both skeleton and acoustic data, while an advanced attention-based model refines the fusion process by prioritizing the most relevant features. The proposed method is validated through extensive experiments on specialized datasets for geriatric monitoring, demonstrating its effectiveness in accurately identifying abnormal activities and sounds associated with emergencies. Notably, the incorporation of attention mechanisms significantly improves detection reliability. This work contributes to the development of real-time continuous monitoring systems, promoting early intervention and reducing dependence on acute care.

Drug abuse and addiction problems are one of the most serious health, social, and psychological problems facing the world. Many international studies indicate that the start of drug abuse occurs mostly in adolescence, which is the period that young people spend in schools, institutes, and universities. Drugs in the student community have become a scourge that raises increasing concern, whether among families or educators, over the fate of school children and educational attainment. Regarding their behaviors, an addicted student often exhibits abnormal behaviors such as permanent lethargy, anxiety, tremors, and aggressive behavior toward others. Moreover, to obtain drugs, the addicted student becomes compelled to resort to various means and ways, and they gradually become criminal addicts. To this endeavor, a detector of abnormal behaviors in schools has become a necessity. In this paper, we built an automatic system able to analyze and detect abnormal behaviors of addicted students and inform the educational staff and parents to know how to manage and treat them. On a technical level, we used deep learning and the recent computer vision techniques in the suggested solution due to their contributions to human behavior and emotion recognition fields. The best‐recorded result (97.5%) is obtained with fused handcrafted features based on skeleton joints and deep features extracted with the MobileNet pretrained model and forwarded to a deep proposed network based on two TimeDistributed layers, one BiLSTM layer, and several Dense layers.

Gathering a dataset is essential in machine learning to develop new approaches and test their effectiveness. Despite their importance, it is difficult to find available datasets that meet researcher requirements, especially for the healthcare domain. This paper presents a new visual dataset of the top frequent elderly emergencies (heart attack, stroke, and Chronic Obstructive Pulmonary Disease (COPD) exacerbation). It is designed for the evaluation of different computational computer vision monitoring applications and human activity analysis. It can also be used for abnormal activity detection. This dataset covers the most frequent symptoms of each emergency. Each symptom is presented by different scenarios validated by a medical emergency expert. This dataset is recorded using the Kinect camera. Its samples are performed only in-depth and skeleton data to preserve the privacy of participants. Also, it is evaluated using methods based on Kinect skeleton data.

As life expectancy increases, people can live a longer life. However, ageing is associated with various health problems. As a result, older people are vulnerable to emergencies. To ensure the security of older people, it is necessary to analyse abnormal activities. Accordingly, vision sensors serve in elderly care and assisted living. Hence, this study allows the researchers to better understand the field of elderly emergency detection through abnormal activity analysis. In this work, we investigate and compare existing visual approaches and their performance. In addition, we explore available and interesting datasets. Moreover, we discuss existing approaches and datasets to improve real-world applications. We also present challenges to be tackled for future studies in monitoring applications.

Computer Vision besides Deep Learning techniques has served many human activity recognition applications, especially those related to safety, and security purposes. However, autistic children's security has not gotten the high interest of many Computer Vision researchers, despite the autistic children are often exposed to danger regarding their behaviors. High-grade autistic children often get involved in the Meltdown Crisis state where their behaviors in this state turn aggressive, and the children become out of control. In this research, we aim to present a monitoring system able to predict the Meltdown Crisis state early and alert the children's parents or caregivers before getting into more challenging scenarios. Towards this end, the proposed system was built upon a CNN-LSTM structure to learn spatial and temporal characteristics of stereotypical movements of autistic children at the Meltdown Crisis state onset, which is known as a Pre-Meltdown Crisis state. The vision part of the proposed architecture was constructed based on a VGG16 network pre-trained on the ImageNet dataset. The final choices regarding data preparation, model configuration, and training settings were tuned and set empirically to end with a 98% recall and F1 Score, while the best loss value achieved was 0.034. For evaluation, we used the MeltdownCrisis dataset that contains realistic scenarios of autistic children's activities in the Pre-Meltdown Crisis state and Normal state, where the data of the Normal state was used as a negative class.

Falls are considered one of the most severe health problems, especially among older people with physical disabili-ties. To ensure the security of the elderly, it is necessary to predict fall before it happens. This paper proposes a computer vision method for fall prediction among physically disabled elderly. Within our approach, we propose a novel implementation of Encoder-Decoder ConvLSTM (Convolutional Long Short-Term Memory). This work includes three parts: Data Acquisition, Data Preprocessing, and Data Analysis. Starting by acquiring skeleton streams using the Kinect camera. Then, applying preprocessing techniques: extracting skeleton features and selecting key frames. Finally, the analysis step consists of predicting the next frames and classifying them. In case of a predicted fall, an alert will be launched. To evaluate our approach, we use the FallFree dataset that covers all fall types and scenarios of people using canes and others without any mobility aid. Our method achieves an accuracy of 99.64%.