# Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
###### Abstract
Few studies have been performed related to understanding the association between daily life activities and sports performances, such as imageCLEFlifelog--SPLL task (Sport Performance Lifelog). In [11], the authors introduce an interesting multimodal lifelog dataset that includes individuals' dietary habits, sports participation, and health status with the aim of studying human behavior via their daily activities. This dataset contains different factors, including food pictures, heart rates, sleep qualification, exercises, and feelings. The primary motivation of this study is to combine information on various aspects of daily activities, including exercises and nutrition, to recognize human activities and extract daily behavior patterns with the aim of discovering five representative models for understanding human behavior. Although the dataset contains some information relating to sports, the authors do not aim to analyze how nutrition and physical activities can impact sports activities. In contrast to the mentioned methods and datasets, PMData [12] aims to collect lifelog data from ordinary people who want better health by exercising independently without personal trainers. These people could be considered amateur athletes. The motivation of the person who created this dataset is to provide a playground for those who want to create a tool that can support amateur athletes to gain better training performance by monitoring their daily routines. This motivation is expressed to the public via the ImageCLEFlifelog 2020 challenge [10], where participants are required to predict the change in running speed and weight after monitoring the amateur athlete's dataset for a while. Recent multimodal lifelog datasets have succeeded in collecting diverse data types that reflect daily human activities. The ambition of these datasets is to collect as much data as possible. Unfortunately, this leads to significant challenges in data analysis and prediction model construction, such as high-dimensional data, computational complexity workload, data samplings, and redundant data. Although lifelog data help to serve individual demands (e.g., searching, recommendation, activity/behavior prediction), the community needs more than that: a prediction model that can work for a large-scale group of people, and that model can easily and quickly adapt to a new coming individual who joins the community. In other words, given a set of lifelog data collected from a group of diverse people, can we build a model that can work well for both groups and individuals? Moreover, can we explain the reason or causality of the model's output? To meet these challenges and discover valuable and interesting insights, we propose a new method to achieve the following:* Find different optimal subsets of data types from multimodal lifelog datasets that can help to reduce the computational complexity of the system;
* Discover daily nutrition and activity patterns that significantly impact exercise outcomes including endurance, stamina, and weight loss; and
* Predict exercise outcomes based on daily nutrition and activities, both for a group and an individual. The main contributions of our work are as follows:
* We apply the periodic-frequent pattern mining technique [13] to discover subsets of factors that appear with a high periodic-frequent score throughout the dataset. We convert nutrition and physical activity data into a transactional table by converting continuous data into discrete data using fuzzy logic. We hypothesize that these subsets can characterize a particular group of people who share the same common points that do not appear in other groups. * We estimate the portion of healthy and unhealthy foods from food images and treat them as numeric data. The data can enrich the nutrition factor besides food-logs reported by people. Estimating a portion of healthy food could overcome the challenge of precisely calculating calories from food images since object detection and semantic segmentation algorithms currently work better than image-to-calories approaches.