背景:人类行为模式涉及心理学之间的相互作用,生理学,和压力,这些都与不同年级的步态有关。
目的:该研究旨在揭示人格之间的相互关系,脑力劳动,和步态模式通过使用惯性传感器捕获步态变化。它还评估个人人格特质并模拟压力以构建步态分类模型。
方法:60名参与者被指示定期执行,低,在走廊上行走以模拟自然环境行走。同时将惯性测量单元(IMU)放置在八个身体部位上。使用听觉n-back任务诱导脑力负荷,并对他们的五大人格特质进行了评估。来自IMU的步态数据被分为九种平均分类,低,和高的五大库存得分与三个级别的精神工作量步行。随后,分割步态数据被用作深度学习模型中分类的输入特征,采用滑动窗口长短期记忆网络对不同人格维度的九种分类。
结果:结果表明,9种分类的开放性平均准确率为83.6%,84.4%的责任心,外向性为82.0%,一致性为85.2%,在所有IMU安置中,神经质占84.5%。值得注意的是,来自下背部IMU的步态数据实现了最高的模型性能,平均准确率为92.7%,在分类不同层次的人格和心理工作量步行。相比之下,左手腕和胸部在普通人群中出现了一些错误分类,低,和高脑力劳动跨越人格特质。
结论:成功的分类可以帮助实时监测个体的精神状态并分析人格维度,提供反馈和建议。本研究表明,步态特征可以为更深刻和个性化的健康信息做出贡献。
BACKGROUND: Human behavior patterns involve mutual interactions among psychology, physiology, and stress, which are all associated with gait at different grades.
OBJECTIVE: The study aims to reveal the interrelationship among personality, mental workload, and gait patterns by capturing gait variations using inertial sensors. It also assesses individual personality traits and simulates stress to construct a gait classification model.
METHODS: Sixty participants were instructed to perform regular, low, and high mental workload walking on the corridor to simulate a natural setting walking. Meanwhile inertial measurement units (IMUs) were placed on eight body parts. Mental workload was induced using the auditory n-back task, and their Big Five personality traits were evaluated. Gait data from IMUs were categorized into nine classifications of average, low, and high Big Five Inventory scores with three levels of mental workload walking. Subsequently, the segmentation gait data were used as input features for classifications in deep learning models, employing a sliding window long short-term memory network for nine classifications for different personality dimensions.
RESULTS: The results indicated average accuracies of nine classifications were 83.6 % for Openness, 84.4 % for Conscientiousness, 82.0 % for Extraversion, 85.2 % for Agreeableness, and 84.5 % for Neuroticism across all IMU placements. Remarkably, gait data from the lower back IMU achieved the highest model performance, with an average accuracy of 92.7 %, in classifying the different levels of personality and mental workload walking. In contrast, the left wrist and chest showed several misclassifications among regular, low, and high mental workload walking across personality traits.
CONCLUSIONS: Successful classification can help monitor an individual\'s mental state in real time and analyze personality dimensions, providing feedback and suggestions. The present study demonstrated that gait characteristics can contribute to more profound and personalized health information.