CSL-SHARE: A Multimodal Wearable Sensor-Based Human Activity Dataset
Hui Liu, Yale Hartmann, Tanja Schultz · Frontiers in Computer Science · 2021
In this digital age, human activity recognition (HAR) plays an increasingly important role in almost all aspects of life to improve people's quality of life, such as auxiliary medical care, rehabilitation technology, and interactive entertainment.Besides external sensing, sensor-based internal sensing for HAR is also intensively studied.A large body of research involves recognizing various kinds of everyday human activities, including walking, standing, jumping, and performing gestures.HAR research relies on large amounts of data, which includes the collection of laboratory data that meet in-house research goals, as well as the usage of external and public databases to verify models and methods.Therefore, data collection is an essential part of our entire HAR research work, for which we will detail this extensive progress.Many public HAR datasets are available online, providing various sorts of collected data, some of which have some similarities with our in-house data acquisition in terms of purpose, sensor selection, or protocol design.For instance, the Opportunity benchmark database (Chavarriaga et al., 2013) contains naturalistic daily living activities recorded with a large set of on-body sensors.The UniMiB SHAR dataset (Micucci et al., 2017) includes 11,771 samples of both human activities and falls divided into 17 fine-grained classes.The GaitAnalysisDataBase (Loose et al., 2020) contains 3D walking kinematics and muscle activity data from healthy adults walking at normal, slow or fast pace on the flat ground or at incremental speeds on a treadmill.The RealWorld dataset (Sztyler and Stuckenschmidt, 2016) covers acceleration, GPS, gyroscope, light, magnetic field, and sound level data of the activities climbing stairs down and up, jumping, lying, standing, sitting, running/jogging, and walking of 15 subjects.The FORTH-TRACE dataset (Karagiannaki et al., 2016) is collected from 15 participants wearing five Shimmer wearable sensor nodes on the left/right wrist, the torso, the right thigh, and the left ankle.The ENABL3S dataset (Hu et al., 2018) contains bilateral electromyography (EMG) and joint and limb kinematics recorded from wearable sensors for ten able-bodied individuals as they freely transitioned between sitting, standing, and five walking-related activities.In this article, we disclose our in-house collected sensor-based dataset, CSL-SHARE (Cognitive Systems Lab Sensor-based Human Activity REcordings).Based on the improvement of the recording plan and organization through the experience gathered from the pilot datasets' collection of CSL17 (one subject, seven activities of daily living, 15 minutes) and CSL18 (four subjects, 18 activities of daily living and sports, 90 minutes), the CSL-SHARE dataset covers 22 types of activities of daily living and sports from 20 subjects in a total time of 691 minutes, of which 363 minutes are segmented and annotated.In this dataset, we used two triaxial accelerometers, two triaxial gyroscopes, four surface electromyography (sEMG) sensors, one biaxial electrogoniometer, and one airborne microphone integrated into a knee bandage, bringing the total number of channels to 19, as these sensors can provide usable and reliable biosignals for HAR research, gait analysis, and health assessment according to existing studies, such as Whittle ( 1996), Rowe et al. (2000), Mathie et al. (2003), Kwapisz et al. (2010), Rebelo et al. (2013), and Teague et al. (2016).We also tried to use a piezoelectric