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.

现有的生理数据集大多采集自静息状态或受控实验场景。因此，基于此类数据开发的算法，在动态、非受控环境下采集的生物信号上的表现往往欠佳。ScientISST MOVE是一款多模态数据集，记录了自然日常活动中的生理信号，活动类型包括搬举椅子、问候他人、手势动作、行走与跑步。该数据集通过三款可穿戴设备采集数据，分别为胸带式设备、臂带式设备以及Empatica E4腕带设备。这套采集方案可同步记录多通道皮肤电活动（Electrodermal Activity, EDA）、光电容积描记术（Photoplethysmography, PPG）与心电图（Electrocardiography, ECG）信号。此外，采集内容还涵盖肱二头肌肌电图（Electromyography, EMG）、腕部体温以及胸带与腕带的活动记录。共有17名健康志愿者参与了本次实验数据采集环节，最终得到各传感器同步采集的有效数据平均时长约37分钟。ScientISST MOVE数据集最初旨在探究日常活动对生理数据采集的影响。由于该数据集采用多款可穿戴设备采集（部分设备可采集相同生理模态数据），因此也可用于信号质量对比相关研究。