Summary PsyMo A Dataset for Estimating Psychological Traits from Gait arxiv.org
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PsyMo is a comprehensive dataset containing walking sequences, psychological traits, and demographic information, designed for interdisciplinary research in the fields of AI and psychology.
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Key Points
- PsyMo is a dataset that explores the embodied manifestation of psychological traits in human gait.
- The dataset contains 312 subjects walking under multiple viewpoints and walking variations, annotated with self-assessed psychological traits.
- It covers seven walking variations and includes demographic information such as age, gender, height, and weight.
- PsyMo can be used for interdisciplinary research into human behavior and as a benchmark dataset for gait recognition.
- The dataset is fully anonymized, and only processed gait information is made publicly available.
Summaries
20 word summary
PsyMo is a public dataset with walking sequences, psychological traits, and demographic info for interdisciplinary research in AI and psychology.
76 word summary
PsyMo is a public dataset with walking sequences from 312 subjects, along with self-assessed psychological traits and demographic information. It serves as a benchmark dataset for gait recognition and enables the estimation of psychological traits from gait. PsyMo goes beyond identity recognition by exploring psychological cues from behavior. The dataset provides baseline results for psychological trait estimation and is a valuable resource for interdisciplinary research in AI and psychology, adhering to ethical guidelines and participant privacy.
146 word summary
PsyMo is a public dataset that focuses on the embodied manifestation of psychological traits in human gait. It includes walking sequences from 312 subjects, along with self-assessed psychological traits and demographic information. The aim of PsyMo is to provide a multi-purpose gait database for exploring psychological cues in walking patterns. It serves as a benchmark dataset for gait recognition and enables the estimation of psychological traits from gait. While previous research has focused on identity recognition in gait analysis, PsyMo goes beyond that by exploring psychological cues from behavior. The dataset provides baseline results for psychological trait estimation using different modalities and serves as a valuable resource for interdisciplinary research in AI and psychology. Ethical procedures were followed during data collection, and participant privacy is protected. PsyMo is a valuable dataset for exploring psychological traits through gait analysis, adhering to ethical guidelines and protecting participant privacy.
403 word summary
PsyMo is a public dataset that focuses on the embodied manifestation of psychological traits in human gait. It consists of walking sequences from 312 subjects, along with self-assessed psychological traits and demographic information. The dataset covers seven walking variations and was captured using consumer surveillance cameras to mimic real-world scenarios.
The aim of PsyMo is to provide a multi-purpose gait database that can be used for exploring psychological cues in walking patterns. It is intended for interdisciplinary research in AI and psychology, serving as a benchmark dataset for gait recognition and enabling the estimation of psychological traits from gait. The dataset is fully anonymized, with only processed gait information being publicly available.
While previous research has focused on identity recognition in gait analysis, PsyMo goes beyond that by exploring psychological cues from behavior. Existing gait datasets lack annotations beyond subject identity, highlighting the need for more diverse datasets that include walking variations and viewpoints.
PsyMo provides baseline results for psychological trait estimation using different modalities such as silhouettes and skeletons. GaitFormer, a transformer-based architecture, performs well in subject-level evaluation. The dataset has limitations, but efforts have been made to make it realistic by using consumer surveillance cameras.
PsyMo serves as a valuable resource for researchers interested in studying human behavior from both AI and psychology perspectives. It enables the development of more advanced architectures for psychological trait estimation through its multi-modal nature.
It is important to note that PsyMo is not intended to be a benchmark for all age groups, cultures, and races. Instead, it serves as a starting point for research into psychological manifestations in movement. Narrow models can be developed and interdisciplinary studies can be conducted using this dataset.
Ethical procedures were followed during the data collection process, which involved capturing walking sequences using surveillance cameras and having participants complete questionnaires remotely. The collection process was approved by the Ethics Review Board, and participants consented to having their walking patterns recorded.
PsyMo contains anonymized data without any identifiable or sensitive information. It is distributed through a dedicated website and protected by the CC-BY-NC-ND License.
In conclusion, PsyMo is a valuable dataset for exploring psychological traits through gait analysis. It provides researchers with an opportunity to study human behavior from both AI and psychology perspectives, adhering to ethical guidelines and protecting participant privacy. It can be used to develop models and conduct interdisciplinary studies in the field of psychological manifestations in movement.
484 word summary
PsyMo is the first public dataset that explores the embodied manifestation of psychological traits in human gait. It contains walking sequences from 312 subjects, along with self-assessed psychological traits and demographic information. The dataset covers seven walking variations, captured using consumer surveillance cameras to mimic real-world scenarios.
Estimating psychological traits from movement and appearance is challenging, and previous attempts have used small-scale datasets with body-attached sensors. PsyMo aims to address this gap by providing a multi-purpose gait database that can be used for exploring psychological cues in walking patterns.
The dataset is intended for interdisciplinary research in AI and psychology. It can be used to estimate psychological traits from gait and as a benchmark dataset for gait recognition. The dataset is fully anonymized, with only processed gait information made publicly available.
Previous research has explored psychological cues from behavior, but gait analysis has primarily focused on identity recognition. Existing gait datasets lack annotations beyond subject identity, highlighting the need for more diverse datasets that include walking variations and viewpoints.
PsyMo provides baseline results for psychological trait estimation using different modalities such as silhouettes and skeletons. GaitFormer, a transformer-based architecture, performs well in subject-level evaluation. The dataset has limitations, such as the presence of researchers in the laboratory, but efforts have been made to make it realistic by using consumer surveillance cameras.
In conclusion, PsyMo is a valuable dataset for exploring the embodied manifestation of psychological traits in gait. It provides researchers with an opportunity to study human behavior from both AI and psychology perspectives. Future research can utilize the multi-modal nature of PsyMo to develop more advanced architectures for psychological trait estimation.
PsyMo is not intended to be a benchmark for all age groups, cultures, and races but serves as a starting point for research into psychological manifestations in movement. It should be used for developing narrow models and conducting interdisciplinary studies.
While gait analysis has potential negative impacts, PsyMo is solely intended for research purposes and does not facilitate unethical use or privacy violations. The dataset includes validated psychological questionnaires and demographic information for each participant.
The data collection process involved capturing walking sequences using surveillance cameras and having participants complete the questionnaires remotely. The collection process was approved by the Ethics Review Board, and participants consented to have their walking patterns recorded.
PsyMo contains anonymized data without any identifiable or sensitive information. It is distributed through a dedicated website and protected by the CC-BY-NC-ND License.
The authors recommend splitting the dataset into training and validation sets for various tasks. Ethical procedures were followed during data collection, including obtaining consent and ensuring participant anonymity.
In summary, PsyMo is a dataset designed for exploring psychological traits through gait analysis. It provides valuable data for research purposes, adhering to ethical guidelines and protecting participant privacy. Researchers can utilize this dataset to develop models and conduct interdisciplinary studies in the field of psychological manifestations in movement.
1051 word summary
PsyMo is a dataset that aims to explore the embodied manifestation of psychological traits in human gait. While there has been research on studying personality manifestations in video, walking has been largely unexplored. However, some studies have confirmed significant differences in gait between individuals with different personalities, levels of aggression, depression, and self-esteem. In this context, PsyMo is the first public dataset of its kind and contains 312 subjects walking under multiple viewpoints and walking variations. The dataset is annotated with self-assessed psychological traits from six psychological questionnaires.
Alongside self-assessed psychological traits, participants also submitted their age, gender, height, and weight. The dataset covers seven walking variations: normal walking, changing clothes, slow walking, fast walking, walking with a bag, and two additional dual-tasks: walking while texting and walking while talking on the phone. The walks are captured using three synchronized consumer video surveillance cameras to mimic adverse conditions present in real-world surveillance scenarios.
Estimating psychological traits from external factors such as movement and appearance is a challenging problem in psychology. Previous attempts to tackle this problem have utilized private small-scale datasets with intrusive body-attached sensors. PsyMo aims to address this gap by providing a multi-purpose gait database that can be used for exploring psychological cues manifested in walking patterns.
The dataset is intended for interdisciplinary research into human behavior from both the artificial intelligence and psychology research communities. It can be used to estimate psychological traits from gait and also as a benchmark dataset for gait recognition. The dataset is fully anonymized, and only processed gait information such as silhouettes, 2D/3D skeletons, and 3D human meshes are made publicly available.
Psychological cues from behavior have been explored in various domains such as personality detection and emotion estimation. Previous works have used multimodal datasets for personality detection based on human faces, body postures, and behaviors. Gait analysis has primarily focused on identity recognition rather than exploring psychological cues. Existing gait datasets are not annotated beyond subject identity, and there is a need for more diverse datasets that include walking variations and viewpoints.
PsyMo provides baseline results for psychological trait estimation using different modalities such as 2D skeletons, silhouettes, and SMPL body meshes. The results show that certain psychological factors are more discernible from silhouettes than from skeletons. GaitFormer, a transformer-based architecture, performs well in subject-level evaluation, outperforming other models across modalities.
The dataset has limitations, such as the presence of researchers in the laboratory that may affect the manner of walking. However, efforts have been made to make PsyMo realistic by using consumer surveillance cameras and mimicking real-world surveillance conditions.
In conclusion, PsyMo is a valuable dataset for exploring the embodied manifestation of psychological traits in gait. It provides researchers with an opportunity to study human behavior from both AI and psychology perspectives. The dataset can be used for estimating psychological traits from gait and as a benchmark dataset for gait recognition. Future research can exploit the multi-modal nature of PsyMo and the natural correlations between questionnaire subscales to develop more advanced architectures for psychological trait estimation.
PsyMo is a dataset designed for exploring psychological traits through gait analysis. The dataset primarily consists of walking sequences from Romanian students of similar age, leading to a bias in terms of age group, culture, and upbringing. It is important to note that PsyMo is not intended to be a benchmark for all age groups, cultures, and races, but rather serves as a starting point for research into psychological manifestations in movement. The dataset is meant to be used for developing narrow models and conducting interdisciplinary studies in this domain.
While gait analysis has the potential for negative societal impacts, such as intrusive surveillance practices and unauthorized profiling, it is essential to emphasize that PsyMo is solely intended for research purposes. It enables the exploration of psychological traits through gait analysis and does not facilitate any unethical use or violation of privacy. The dataset was partly supported by research grants and the Google IoT/Wearables Student Grants.
The dataset includes various psychological questionnaires that capture 17 psychometric attributes related to personality, mental health, self-esteem, aggression, fatigue, and general health. The questionnaires used in PsyMo are validated measures commonly used in psychological research. The dataset also includes demographic information such as age, gender, weight, and height for each participant.
The data collection process involved capturing walking sequences using surveillance cameras and having participants complete the psychological questionnaires remotely. The cameras were synchronized and controlled using a custom-built Python program. The participants voluntarily filled out the questionnaires and consented to have their walking patterns recorded for research purposes. The collection process was approved by the Ethics Review Board.
PsyMo contains anonymized data, with no identifiable information that could directly or indirectly link individuals to their data. The dataset does not contain any offensive, confidential, or sensitive information. It is important to note that the dataset does not cover all possible viewpoints and walking variations but focuses on controlled variations to benchmark performance in specific scenarios.
The dataset is self-contained and does not rely on external resources. It is distributed through a dedicated website, and the authors provide access to the dataset for research purposes. The dataset is protected by the CC-BY-NC-ND License, which allows for non-commercial use and prohibits modification or redistribution without permission.
The authors recommend splitting the dataset into training and validation sets in an 80:20 ratio for various tasks. There is no specific mention of future updates to the dataset, but the authors are open to extending or correcting it if necessary.
Ethically, the dataset collection process followed proper procedures, including obtaining consent from participants and ensuring their anonymity. Participants were provided with clear explanations of the study and their rights regarding data privacy. They were given the option to revoke their consent in the future if desired.
No analysis of the potential impact of the dataset and its use on data subjects has been conducted. However, since the dataset is anonymized and does not contain sensitive information, the risk of harm or abuse is minimal.
In summary, PsyMo is a dataset designed for exploring psychological traits through gait analysis. It provides valuable data for research purposes, adhering to ethical guidelines and protecting participant privacy. Researchers can utilize this dataset to develop models and conduct interdisciplinary studies in the field of psychological manifestations in movement.