Principal component analysis (PCA) is a dimensionality reduction method that has identified significant differences in older adults’ motion analysis previously not detected by the discrete exploration of biomechanical variables. This systematic review aims to synthesize the current evidence regarding PCA use in the study of movement in older adults (kinematics and kinetics), summarizing the tasks and biomechanical variables studied. From the search results, 1685 studies were retrieved, and 19 studies were included for review. Most of the included studies evaluated gait or quiet standing. The main variables considered included spatiotemporal parameters, range of motion, and ground reaction forces. A limited number of studies analyzed other tasks. Further research should focus on the PCA application in tasks other than gait to understand older adults’ movement characteristics that have not been identified by discrete analysis.

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The great social development of the last few decades has led more and more to free time becoming an essential aspect of daily life. As such, there is the need to maximize free time trying to enjoy it as much as possible and spending it in places with positive atmospheres that result in positive sentiments. In that vein, using Machine Learning models, this project aims to create a time series prediction model capable of predicting which sentiment a given place cause on the people attending it over the next few hours. The predictions take into account the weather, whether or not an event is happening in that place, and the history of sentiment in that place over the course of the previous year. The extensive results on dataset illustrate that Long Short-Term Memory model achieves the state-of-the-art results over all models. For example, in multivariate model, the accuracy performance is 80.51% when it is applied on the LinkNYC Kiosk dataset.

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