ScientISST MOVE: Annotated Wearable Multimodal Biosignals recorded during Everyday Life Activities in Naturalistic Environments

Existing datasets containing physiological data are mostly acquired at rest or in controlled scenarios. As a result, algorithms developed using such data may not perform as well as with biosignals acquired in dynamic and uncontrolled environments. ScientiSST MOVE is a multimodal dataset recording natural everyday activities, including lifting a chair, greeting, gesticulating, walking and running. Data was collected using three wearable devices, namely: a chestband, an armband, and the Empatica E4 wristband. This setup enabled recording of multi-channel Electrodermal Activity (EDA), Photoplethysmography (PPG) and Electrocardiography (ECG). Additionally, recordings were also made for bicep Electromyography (EMG), wrist temperature and chest and wrist actigraphy. A total of 17 healthy volunteers participated in the experimental data acquisition sessions, resulting in an average of 37 useful minutes of synchronised data from all sensors. ScientISST MOVE has been primarily designed to study the effect of daily activities on physiological data acquisition. Having been acquired with multiple wearable devices, some of which measuring the same modalities, it can also be useful in signal quality comparison studies.

现有生理数据集大多是在静止或受控环境中采集的。因此，使用此类数据开发的算法可能在动态和非受控环境采集的生物信号面前表现不佳。ScientiSST MOVE是一个多模态数据集，记录了自然日常活动，包括抬椅子、问候、手势、行走和跑步。数据采集采用了三种可穿戴设备，具体包括：胸带、臂带以及Empatica E4腕带。该配置使得能够记录多通道的皮肤电活动（EDA）、光电容积描记法（PPG）和心电图（ECG）。此外，还对肱二头肌肌电图（EMG）、腕温以及胸部和腕部的活动计图进行了记录。共有17名健康志愿者参与了实验数据采集环节，从而获得所有传感器同步数据的平均时长为37分钟。ScientiSST MOVE主要设计用于研究日常活动对生理数据采集的影响。由于采用了多种可穿戴设备进行采集，其中一些设备测量相同的模态，因此它也适用于信号质量比较研究。（Electrodermal Activity (EDA), Photoplethysmography (PPG), Electrocardiography (ECG), Bicep Electromyography (EMG), Wrist temperature, Chest and wrist actigraphy）

搜集汇总

背景与挑战

背景概述

ScientISST MOVE是一个多模态可穿戴生物信号数据集，专注于记录自然环境下日常活动（如举椅子、问候、步行和跑步）的生理数据，使用胸带、臂带和腕带设备采集EDA、PPG、ECG、EMG等多种信号。该数据集由17名健康志愿者参与，平均提供37分钟同步数据，旨在研究日常活动对生理数据采集的影响，并支持信号质量比较研究，弥补了现有数据在动态和非受控环境中的不足。