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With the rapid development and widespread adoption of wearable technology, a new type of lifelog data is being collected and used in numerous studies. We refer to these data as informative lifelog which usually contain GPS, images, videos, text, etc. GPS trajectory data in lifelogs is typically categorized into continuous and discrete trajectories. Finding a point of interest (POI) from discrete trajectories is a challenging task to do and has caught little attention so far. This paper suggests an LP-DBSCAN model for mining personal trajectories from discrete GPS trajectory data. It makes use of the hierarchical structure information implied in GPS trajectory data and it is suggested a variable-levels, variable-parameters clustering method (LP-DBSCAN) based on the DBSCAN algorithm to increase the precision of finding POI information. Finally, the Liu lifelog dataset is subjected to a systematic evaluation. In terms of GPS data that are not evenly distributed geographically, the experimental results demonstrated that the proposed algorithm could more accurately identify POI information and address the adverse effects caused by the global parameters of the traditional DBSCAN algorithm. [ABSTRACT FROM AUTHOR]

Photos and videos are generated frequently in our daily activities. With a huge collection of visual data, it is essential to provide an efficient and easy-to-use system for users to retrieve moments of interest for a wide variation of query types. This motivates us to develop and upgrade our solution for visual lifelog exploration. Our solution includes a web-based retrieval system and a series of best practices to assist users in using our system. This paper details the enhanced version of FIRST, our Flexible Interactive Retrieval SysTem for visual lifelog retrieval. Our system supports multiple modalities for interaction and query processing, including visual query by meta-data, text query, and visual information matching based on a joint embedding model, scene clustering based on visual and location information, flexible temporal event navigation, and query expansion with visual examples. With a flexible system architecture, our system can easily integrate new modules to enhance its functionality. We conduct a user study to analyze the behaviors of novice and expert users of our system for different query scenarios. Finally, we propose best practice guidelines for users of our system to efficiently find events or moments of interest. [ABSTRACT FROM AUTHOR]

Lifelogging was introduced as the process of passively capturing personal daily events via wearable devices. It ultimately creates a visual diary encoding every aspect of one's life with the aim of future sharing or recollecting. In this paper, we present LifeSeeker, a lifelog image retrieval system participating in the Lifelog Search Challenge (LSC) for 3 years, since 2019. Our objective is to support users to seek specific life moments using a combination of textual descriptions, spatial relationships, location information, and image similarities. In addition to the LSC challenge results, a further experiment was conducted in order to evaluate the power retrieval of our system on both expert and novice users. This experiment informed us about the effectiveness of the user's interaction with the system when involving non-experts. [ABSTRACT FROM AUTHOR]

There is a growing number of lifelogging retrieval systems that have been introduced in several lifelogging workshops and sessions. Across all systems at the LSC, which is an annual international challenge about lifelogging retrieval, our Myscéal is currently considered as the state-of-the-art. In this paper, we describe the system in detail and show how it has been upgraded through time since firstly introduced in 2020. In addition, we analyse Myscéal performance not only in the three lifelog retrieval competitions it participated in but also with additional user experiments. The result shows that the fast searching time of Myscéal is the system's most important feature that helps it get some significant advantages in competitions. On the other hand, the findings from user experiments indicate that Myscéal still needs some improvements for novice users who are unfamiliar with how to interact with the system. Moreover, the user study plays a vital role in the development of Myscéal as many updates of this system came from the feedback of the participating users. We also demonstrate the efficacy of Myscéal as a lifelog retrieval system to help the lifeloggers, who capture their daily life in images, recall memorable moments in their massive lifelog archives. [ABSTRACT FROM AUTHOR]

People usually forget the details of life experiences and encounter situations where they require to recall their past experiences. Therefore, lifelog retrieval turns out to be an emerging task in the AI community. Nowadays, people can record their life experiences by capturing images through wearable devices, writing blog posts, and so on. These personal big data stored in digital format can be considered lifelogs for retrieval. In this work, we focus on constructing a visual lifelog retrieval system that is able to efficiently find related images given the input textual queries. The core challenge of visual lifelog retrieval with textual queries comes from the semantic gap between visual and textual data. In this work, we propose LifeConcept, an interactive lifelog search system that is aimed at not only accelerating the retrieval process but also fetching more precise results. To reduce the semantic gap, we incorporate visual and textual concepts from images into our system utilizing pre-trained textual embeddings. Moreover, we propose a concept recommendation method enabling users to set up the related conditions for their requirements efficiently and search the desired images with appropriate query terms based on the suggestion. Experimental results show that textual concepts from images detected by CV models improve the retrieval results. We further employ annotators to label captions of images for investigating the difference between model-generated captions and human-labeled captions. The human-annotated dataset is released to facilitate future study of visual lifelog retrieval. Four research questions are discussed to explore the characteristic of models and humans interpreting the first-person images captured by wearable cameras. The impacts of model-generated captions and human-labeled captions in terms of visual lifelog retrieval are also included. [ABSTRACT FROM AUTHOR]

In social science, health care, digital therapeutics, etc., smartphone data have played important roles to infer users' daily lives. However, smartphone data collection systems could not be used effectively and widely because they did not exploit any Internet of Things (IoT) standards (e.g., oneM2M) and class labeling methods for machine learning (ML) services. Therefore, in this paper, we propose a novel Android IoT lifelog system complying with oneM2M standards to collect various lifelog data in smartphones and provide two manual and automated class labeling methods for inference of users' daily lives. The proposed system consists of an Android IoT client application, an oneM2M-compliant IoT server, and an ML server whose high-level functional architecture was carefully designed to be open, accessible, and internationally recognized in accordance with the oneM2M standards. In particular, we explain implementation details of activity diagrams for the Android IoT client application, the primary component of the proposed system. Experimental results verified that this application could work with the oneM2M-compliant IoT server normally and provide corresponding class labels properly. As an application of the proposed system, we also propose motion inference based on three multi-classML classifiers (i.e., k nearest neighbors, Naive Bayes, and support vector machine) which were created by using only motion and location data (i.e., acceleration force, gyroscope rate of rotation, and speed) and motion class labels (i.e., driving, cycling, running, walking, and stilling). When compared with confusion matrices of the ML classifiers, the k nearest neighbors classifier outperformed the other two overall. Furthermore, we evaluated its output quality by analyzing the receiver operating characteristic (ROC) curves with area under the curve (AUC) values. The AUC values of the ROC curves for all motion classes were more than 0.9, and the macro-average and micro-average ROC curves achieved very high AUC values of 0.96 and 0.99, respectively. [ABSTRACT FROM AUTHOR]

In the information age, the analysis of human behavioral patterns is of significant interest, and extensive data collection is crucial in the context of privacy protection. From 2011 to 2023, the authors collaborated with more than 20 volunteers to construct and collect 30,791 lifelog using mobile devices; these lifelog included a sizable amount of multi-type data containing behavioral activity information, temporal location information, textual description information, and media images. In this paper, we propose a DCP-BiLSTM (Description + Cluster + Position) And Bidirectional Long-Short Term Memory (BiLSTM) personal big data behavior classification model for the lifelog dataset, which uses text description information + GPS information classified by DB-SCAN + location information, it is validated on Liulifelog to better address the behavior prediction problem of lifelog data. Unlike the traditional text classification approach, which is based on a unique lifelog dataset, BiLSTM can combine bidirectional semantic variables comprising description information and location information into the DCP-BiLSTM model. Evaluation of the DCP-BiLSTM demonstrates that can increase the performance and accuracy of behavior prediction, and the BiLSTM that incorporates description and location information to build, not only identifies daily user activity but also corrects the bias caused by manual analysis's one-sidedness and has greater prediction performance. [ABSTRACT FROM AUTHOR]

The Lifelog Search Challenge (LSC) is an interactive benchmarking evaluation workshop for lifelog retrieval systems. The challenge was first organised in 2018 aiming to find the system that can quickly retrieve relevant lifelog images for a given semantic query. This paper provides an analysis of the performance of all 17 systems participating in the 4th LSC workshop held at the 2021 Annual ACM International Conference on Multimedia Retrieval (ICMR). LSC’21 was the largest effort at comparing different approaches to interactive lifelog retrieval systems seen thus far. Findings from the challenge suggest that many different interactive factors contribute to the success (or otherwise) of participating teams. In this paper, we provide an overview of the LSC’21 challenge, introduce each team’s approach and explore these factors in depth and offer clues on how to develop a high-performing interactive lifelog search engine.

To understand themultilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real-world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long-term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network-based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior usingmultimodal data accumulated throughout daily lives under natural conditions.

Human emotions and behaviors are reciprocal components that shape each other in everyday life. While the past research on each element has made use of various physiological sensors in many ways, their interactive relationship in the context of daily life has not yet been explored. In this work, we present a wearable affective life-log system (ALIS) that is robust as well as easy to use in daily life to accurately detect emotional changes and determine the cause-and-effect relationship between emotions and emotional situations in users’ lives. The proposed system records how a user feels in certain situations during long-term activities using physiological sensors. Based on the long-term monitoring, the system analyzes how the contexts of the user’s life affect his/her emotional changes and builds causal structures between emotions and observable behaviors in daily situations. Furthermore, we demonstrate that the proposed system enables us to build causal structures to find individual sources of mental relief suited to negative situations in school life. [ABSTRACT FROM AUTHOR]

Transportation Mode Detection (TMD) is an important task for the Intelligent Transportation System (ITS) and Lifelog. TMD, using smartphone built-in sensors, can be a low-cost and effective solution. In recent years, many studies have focused on TMD, yet they support a limited number of modes and do not consider similar transportation modes and holding places, limiting further applications. In this paper, we propose a new network framework to realize TMD, which combines structural and spatial interaction features, and considers the weights of multiple sensors' contributions, enabling the recognition of eight transportation modes with four similar transportation modes and four holding places. First, raw data is segmented and transformed into a spectrum image and then ResNet and Vision Transformers (Vit) are used to extract structural and spatial interaction features, respectively. To consider the contribution of different sensors, the weights of each sensor are recalibrated using an ECA module. Finally, Multi-Layer Perceptron (MLP) is introduced to fuse these two different kinds of features. The performance of the proposed method is evaluated on the public Sussex-Huawei Locomotion-Transportation (SHL) dataset, and is found to outperform the baselines by at least 10%. [ABSTRACT FROM AUTHOR]

To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real‐world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long‐term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network‐based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions. [ABSTRACT FROM AUTHOR]

Lifelogs are generated in our daily lives and contain useful information for health monitoring. Nowadays, one can easily obtain various lifelogs from a wearable device such as a smartwatch. These lifelogs could include noise and outliers. In general, the amount of noise and outliers is significantly smaller than that of normal data, resulting in class imbalance. To achieve good analytic accuracy, the noise and outliers should be filtered. Lifelogs have specific characteristics: low volatility and periodicity. It is very important to continuously analyze and manage them within a specific time. To solve the class imbalance problem of outliers in weight lifelog data, we propose a new outlier generation method that reflects the characteristics of body weight. This study compared the proposed method with the SMOTE-based data augmentation and the GAN-based data augmentation methods. Our results confirm that our proposed method for outlier detection was better than the SVM, XGBOOST, and CATBOOST algorithms. Through them, we can reduce the data imbalance level, improve data quality, and improve analytics accuracy. [ABSTRACT FROM AUTHOR]

Body mass index (BMI) plays a vital role in determining the health of middle-aged people, and a high BMI is associated with various chronic diseases. This study aims to identify important lifelog factors related to BMI. The sleep, gait, and body data of 47 middle-aged women and 71 middle-aged men were collected using smartwatches. Variables were derived to examine the relationships between these factors and BMI. The data were divided into groups according to height based on the definition of BMI as the most influential variable. The data were analyzed using regression and tree-based models: Ridge Regression, eXtreme Gradient Boosting (XGBoost), and Category Boosting (CatBoost). Moreover, the importance of the BMI variables was visualized and examined using the SHapley Additive Explanations Technique (SHAP). The results showed that total sleep time, average morning gait speed, and sleep efficiency significantly affected BMI. However, the variables with the most substantial effects differed among the height groups. This indicates that the factors most profoundly affecting BMI differ according to body characteristics, suggesting the possibility of developing efficient methods for personalized healthcare. [ABSTRACT FROM AUTHOR]

As smart speakers continue to proliferate, question answering (QA) by smart devices is being woven into our daily lives. This study assumes question answering related to daily life events detected by context recognition systems, such as activity recognition and indoor positioning systems, e.g., answering questions like “Did my grandma eat dinner?” and “How many times did my grandpa go to the toilet?” These questions can effectively support human memory-aids, locate lost items, and monitor human activities. However, training a question-answering model requires large amounts of labeled training data (i.e., questions, answers, and the time-series of real-world event triplets) collected in a target environment. In this paper, we propose a novel simulation to real QA (Sim2RealQA) framework that completely trains a QA model with QA datasets produced in a life simulator and use it for solving real-word QA problems without answer labels. Our proposed QA model can learn a general reasoning process for QA that is independent of environments and deal with diverse types of questions specific to question answering in real-world environments, e.g., counting the number of occurrences of a real-world event and enumerating the names of those who are performing an activity together. Experiments show that using life simulations is a promising approach for solving real-world QA problems when no real-world answer labels are available.

Purpose: We propose the Lifelog Bigdata Platform as a sustainable digital healthcare system based on individual-centric lifelog datasets and describe the standardization of lifelog and clinical data in its full-cycle management system. Materials and Methods: The Lifelog Bigdata Platform was developed by Yonsei Wonju Health System on the cloud to support digital healthcare and precision medicine. It consists of five core components: data acquisition system, de-identification of individual information, lifelog integration, analyzer, and service. We designed a gathering system into a dedicated virtual machine to save lifelog or clinical outcomes and established standard guidelines for maintaining the quality of gathering procedures. We used standard integration keys to integrate the lifelog and clinical data. Metadata were generated from the data warehouse after loading combined or fragmented data on it. We analyzed the de-identified lifelog and clinical data using the lifelog analyzer to prevent and manage acute and chronic diseases through providing results of statistics on analysis. Results: The big data centers were built in four hospitals and seven companies for integrating lifelog and clinical data to develop the Lifelog Bigdata Platform. We integrated and loaded lifelog big data and clinical data for 3 years. In the first year, we uploaded 94 types of data on the platform with a total capacity of 221 GB. Conclusion: The Lifelog Bigdata Platform is the first to combine lifelog and clinical data. The proposed standardization guidelines can be used for future platforms to achieve a virtuous cycle structure of lifelogging big data and an industrial ecosystem. [ABSTRACT FROM AUTHOR]

Abstract Background Although depression has a high rate of recurrence, no prior studies have established a method that could identify the warning signs of its recurrence. Methods We collected digital data consisting of individual activity records such as location or mobility information (lifelog data) from 89 patients who were on maintenance therapy for depression for a year, using a smartphone application and a wearable device. We assessed depression and its recurrence using both the Kessler Psychological Distress Scale (K6) and the Patient Health Questionnaire-9. Results A panel vector autoregressive analysis indicated that long sleep time was a important risk factor for the recurrence of depression. Long sleep predicted the recurrence of depression after 3 weeks. Conclusions The panel vector autoregressive approach can identify the warning signs of depression recurrence; however, the convenient sampling of the present cohort may limit the scope towards drawing a generalised conclusion.

Objective: Optimal metabolically healthy status is important to prevent various chronic diseases. This study investigated the association between lifelog-derived physical activity and metabolically healthy status., Methods: This cross-sectional study included 51 Korean adults aged 30-40 years with no history of chronic diseases. Physical activity data were obtained by the International Physical Activity Questionnaire-Short Form (IPAQ-SF). Lifelog-derived physical activity was defined by step counts and walking speed for 1 week, as recorded by the Samsung Health application on both the Samsung Galaxy Fit2 and mobile phones. Participants without metabolic syndrome components were categorized as the metabolically healthy group ( n  = 31) and the remaining participants as the metabolically unhealthy group ( n  = 20). Prevalence ratios and 95% confidence intervals were estimated using Poisson regression models. The predictive ability of each physical activity measure was evaluated according to the area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) values., Results: Among the physical activity measures, lifelog-derived walking speed was significantly inversely associated with prevalent metabolically unhealthy status. The lifelog component model including walking speed, age, and sex had the highest AUC value for metabolically unhealthy status. Adding lifelog-derived step counts to the IPAQ-SF-derived metabolic equivalent (MET) model (including age, sex, and IPAQ-SF-METs) yielded 37% and 13% increases in the NRI and IDI values, respectively. Incorporating walking speed into the IPAQ-SF-derived MET model improved metabolically unhealthy status prediction by 42% and 21% in the NRI and IDI analyses, respectively., Conclusions: Slow walking speed derived from the lifelog was associated with a higher prevalence of metabolically unhealthy status. Lifelog-derived physical activity information may aid in identifying individuals with metabolic abnormalities., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

The personal digital twin extends to individual persons, a concept that originated in engineering to twin complex machines with a digital simulation containing a model of its functions to monitor its past and present behaviour, and repair, correct, improve or otherwise ensure its optimal operation. Several independent trends in technological developments are seen to converge towards the elaboration of the digital replication of individual human data and life history, notably in health industries. Among the main ones, we consider the ubiquitous distribution of digital assistants, the rapid progress of machine learning concurrent with the exponential growth of 'personal' Big Data and the incipient interest in developing lifelogs. The core hypothesis here is that among the psychological effects of the digital transformation, the externalization of cognitive faculties such as memory, planning and judgement, the decision-making processes located within the human person are also emigrating to digital functions, perhaps as a prelude to a later re-integration within the person via brain–computer interfaces. The paper concludes with ethical considerations about these ongoing developments. This article is part of the theme issue 'Towards symbiotic autonomous systems'. [ABSTRACT FROM AUTHOR]

Self-awareness is an essential concept in physiology and psychology. Accurate overall self-awareness benefits the development and well being of an individual. The previous research studies on self-awareness mainly collect and analyze data in the laboratory environment through questionnaires, user study, or field research study. However, these methods are usually not real-time and unavailable for daily life applications. Therefore, we propose a new direction of utilizing lifelog for self-awareness. Lifelog records about daily activities are used for analysis, prediction, and intervention on individual physical and psychological status, which can be automatically processed in real-time. With the help of lifelog, ordinary people are able to understand their condition more precisely, get effective personal advice about health, and even discover physical and mental abnormalities at an early stage. As the first step on using lifelog for self-awareness, we learn from the traditional machine learning problems, and summarize a schema on data collection, feature extraction, label tagging, and model learning in the lifelog scenario. The schema provides a flexible and privacy-protected method for lifelog applications. Following the schema, four topics were conducted: sleep quality prediction, personality detection, mood detection and prediction, and depression detection. Experiments on real datasets show encouraging results on these topics, revealing the significant relation between daily activity records and physical and psychological self-awareness. In the end, we discuss the experiment results and limitations in detail and propose an application, Lifelog Recorder, for multi-dimensional self-awareness lifelog data collection.

Transportation Mode Detection (TMD) is an important task for the Intelligent Transportation System (ITS) and Lifelog. TMD, using smartphone built-in sensors, can be a low-cost and effective solution. In recent years, many studies have focused on TMD, yet they support a limited number of modes and do not consider similar transportation modes and holding places, limiting further applications. In this paper, we propose a new network framework to realize TMD, which combines structural and spatial interaction features, and considers the weights of multiple sensors’ contributions, enabling the recognition of eight transportation modes with four similar transportation modes and four holding places. First, raw data is segmented and transformed into a spectrum image and then ResNet and Vision Transformers (Vit) are used to extract structural and spatial interaction features, respectively. To consider the contribution of different sensors, the weights of each sensor are recalibrated using an ECA module. Finally, Multi-Layer Perceptron (MLP) is introduced to fuse these two different kinds of features. The performance of the proposed method is evaluated on the public Sussex-Huawei Locomotion-Transportation (SHL) dataset, and is found to outperform the baselines by at least 10%.

Lifelog analytics is an emerging research area with technologies embracing the latest advances in machine learning, wearable computing, and data analytics. However, state-of-the-art technologies are still inadequate to distill voluminous multimodal lifelog data into high quality insights. In this article, we propose a novel semantic relevance mapping (SRM) method to tackle the problem of lifelog information access. We formulate lifelog image retrieval as a series of mapping processes where a semantic gap exists for relating basic semantic attributes with high-level query topics. The SRM serves both as a formalism to construct a trainable model to bridge the semantic gap and an algorithm to implement the training process on real-world lifelog data. Based on the SRM, we propose a computational framework of lifelog analytics to support various applications of lifelog information access, such as image retrieval, summarization, and insight visualization. Systematic evaluations are performed on three challenging benchmarking tasks to show the effectiveness of our method [ABSTRACT FROM AUTHOR]

Facing a great deal of Lifelog data and complex information, how to extract valuable information and making it easily understood for users is the most urgent and important issue. This paper introduces a method to find personal trajectories from a Lifelog dataset named informative lifelogging which the author collected over the past 8 years. After introducing the Lifelog system and the data structure, this paper propose a method to find personal trajectory in the dataset. Experiment result in the last chapter shows the correctness of this method. The method proposed in this paper provides some way of managing and retrieving the Lifelog data efficiently. [ABSTRACT FROM AUTHOR]

Lifelog is a digital record of an individual's daily life. It collects, records, and archives a large amount of unstructured data; therefore, techniques are required to organize and summarize those data for easy retrieval. Lifelogging has been utilized for diverse applications including healthcare, self-tracking, and entertainment, among others. With regard to the image-based lifelogging, even though most users prefer to present photos with facial expressions that allow us to infer their emotions, there have been few studies on lifelogging techniques that focus upon users' emotions. In this paper, we develop a system that extracts users' own photos from their smartphones and configures their lifelogs with a focus on their emotions. We design an emotion classifier based on convolutional neural networks (CNN) to predict the users' emotions. To train the model, we create a new dataset by collecting facial images from the CelebFaces Attributes (CelebA) dataset and labeling their facial emotion expressions, and by integrating parts of the Radboud Faces Database (RaFD). Our dataset consists of 4,715 high-resolution images. We propose Representative Emotional Data Extraction Scheme (REDES) to select representative photos based on inferring users' emotions from their facial expressions. In addition, we develop a system that allows users to easily configure diaries for a special day and summarize their lifelogs. Our experimental results show that our method is able to effectively incorporate emotions into lifelog, allowing an enriched experience. [ABSTRACT FROM AUTHOR]

Body mass index (BMI) plays a vital role in determining the health of middle-aged people, and a high BMI is associated with various chronic diseases. This study aims to identify important lifelog factors related to BMI. The sleep, gait, and body data of 47 middle-aged women and 71 middle-aged men were collected using smartwatches. Variables were derived to examine the relationships between these factors and BMI. The data were divided into groups according to height based on the definition of BMI as the most influential variable. The data were analyzed using regression and tree-based models: Ridge Regression, eXtreme Gradient Boosting (XGBoost), and Category Boosting (CatBoost). Moreover, the importance of the BMI variables was visualized and examined using the SHapley Additive Explanations Technique (SHAP). The results showed that total sleep time, average morning gait speed, and sleep efficiency significantly affected BMI. However, the variables with the most substantial effects differed among the height groups. This indicates that the factors most profoundly affecting BMI differ according to body characteristics, suggesting the possibility of developing efficient methods for personalized healthcare.