Gaggioli Andrea, Pioggia Giovanni, Tartarisco Gennaro, Baldus Giovanni, Ferro Marcello, Cipresso Pietro, Serino Silvia, Popleteev Andrei, Gabrielli Silvia, Maimone Rosa, Riva Giuseppe
Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.
Stud Health Technol Inform. 2012;181:182-6.
Prolonged exposure to stressful environments can lead to serious health problems. Therefore, measuring stress in daily life situations through non-invasive procedures has become a significant research challenge. In this paper, we describe a system for the automatic detection of momentary stress from behavioral and physiological measures collected through wearable sensors. The system's architecture consists of two key components: a) a mobile acquisition module; b) an analysis and decision module. The mobile acquisition module is a smartphone application coupled with a newly developed sensor platform (Personal Biomonitoring System, PBS). The PBS acquires behavioral (motion activity, posture) and physiological (hearth rate) variables, performs low-level, real-time signal preprocessing, and wirelessly communicates with the smartphone application, which in turn connects to a remote server for further signal processing and storage. The decision module is realized on a knowledge basis, using neural network and fuzzy logic algorithms able to combine as input the physiological and behavioral features extracted by the PBS and to classify the level of stress, after previous knowledge acquired during a training phase. The training is based on labeling of physiological and behavioral data through self-reports of stress collected via the smartphone application. After training, the smartphone application can be configured to poll the stress analysis report at fixed time steps or at the request of the user. Preliminary testing of the system is ongoing.
长期暴露在压力环境中会导致严重的健康问题。因此，通过非侵入性程序在日常生活情境中测量压力已成为一项重大的研究挑战。在本文中，我们描述了一种用于从通过可穿戴传感器收集的行为和生理测量数据中自动检测瞬间压力的系统。该系统架构由两个关键组件组成：a）移动采集模块；b）分析与决策模块。移动采集模块是一个智能手机应用程序，与新开发的传感器平台（个人生物监测系统，PBS）相结合。PBS采集行为（运动活动、姿势）和生理（心率）变量，进行低级实时信号预处理，并与智能手机应用程序进行无线通信，智能手机应用程序再连接到远程服务器进行进一步的信号处理和存储。决策模块基于知识实现，使用神经网络和模糊逻辑算法，这些算法能够将PBS提取的生理和行为特征作为输入进行组合，并在训练阶段获取先前知识后对压力水平进行分类。训练基于通过智能手机应用程序收集的压力自我报告对生理和行为数据进行标记。训练后，智能手机应用程序可以配置为按固定时间步长或根据用户请求轮询压力分析报告。该系统的初步测试正在进行中。
