Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer’s disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.

In the fast-paced rhythm of modern life, the regular practice of physical exercise emerges as a source of vitality and well-being. However, it is not always feasible to practice on a gym and/or pay a personal trainer to assure a correct pose on calisthenic exercises. This article aims to investigate technological and scientific advancements in the field of computer vision to enhance exercise detection and execution, with the goal of creating a system that can be used autonomously by users. To achieve this, an introduction to the fundamental concepts inherent to the subject is provided, followed by an analysis of different technological approaches, identifying their strengths and weaknesses. Finally, the various challenges and limitations of these technologies are discussed, which remain open to be solved and explored in future research endeavors.

Human Activity Recognition using smartphone sensors supports applications in health monitoring, context-aware computing, and activity assessment. This study evaluates a fused accelerometer, magnetometer, and gyroscope representation for smartphone-based Human Activity Recognition. A balanced processed benchmark containing 10,000 instances was represented using 45 handcrafted statistical and inter-peak-distance features extracted from the three sensor-magnitude signals. Eight classical machine-learning classifiers were evaluated in Orange Data Mining using stratified five-fold cross-validation at the window level. Classification Accuracy ranged from 98.66% for k-Nearest Neighbours to 100.00% for AdaBoost under the adopted benchmark. Fold-level uncertainty estimates and inferential model-comparison tests were unavailable; therefore, no statistically significant superiority among classifiers is claimed. The confusion matrices contain aggregated out-of-fold predictions generated during cross-validation. An audit of the processed matrix identified 1,046 unique feature-label rows among the 10,000 rows, indicating extensive exact repetition that may further increase optimism in row-level cross-validation estimates. Exploratory Random Forest importance scores and feature-distribution analyses show that several gyroscope-derived descriptors are highly ranked within the fused feature space. However, because separate sensor-subset models were not evaluated, the incremental performance attributable specifically to gyroscope inclusion cannot be quantified. The reported results characterise feature separability within the processed dataset and evaluation protocol. They do not establish generalisation to unseen participants or independent recording sessions because participant and session identifiers were unavailable in the processed feature matrix. Hardware latency, memory use, and energy consumption were not measured. The framework should therefore be considered a lightweight benchmark and a candidate for future deployment evaluation rather than a validated real-time mobile implementation.