EVIORA-HAR: A multimodal dataset for elderly activity recognition and safety-controlled fall detection using wearable...

EVIORA-HAR is a multimodal dataset for human activity recognition and fall detection that combines wearable inertial sensing with multi-view RGB video. The data were collected from 104 participants comprising 71 older adults aged 60-93 years performing activities of daily living and 33 healthy adults aged 18-24 years performing safety-controlled fall scenarios. Daily activities were recorded in residential homes and elderly care environments, whereas fall scenarios were acquired in a controlled indoor environment for participant safety. Five wearable inertial measurement unit nodes were attached to the chest, waist, right hand, right thigh, and left ankle. Visual data were captured using three camera configurations: a full-view static camera, a partially occluded static camera, and a dynamic pan-tilt-zoom camera that followed participant movement. Video was recorded at 1280 × 720 resolution and 60 frames per second, while inertial data were recorded at 60 Hz. The dataset contains untrimmed coarse-grained recordings and trimmed fine-grained activity segments representing walking, postural states, postural transitions, and forward, sideward, and backward falls. The trimmed data comprise 11 fine-grained activity classes. Using FV Cam as the reference distribution, the dataset contains 340 walk segments (68.72 min), 323 stand-to-sit (18.18 min), 314 sit-to-stand (16.60 min), 330 sit-to-lay (21.58 min), 321 lay-to-sit (20.31 min), 347 stand (48.17 min), 823 lay (118.59 min), 657 sit (91.58 min), 170 fall-forward (9.45 min), 150 fall-sideward (8.23 min), and 160 fall-backward segments (9.03 min). The released data include privacy-protected RGB videos, inertial signal files, pose-estimation outputs, activity labels encoded in the directory structure and file names, and consistent identifiers supporting modality correspondence. These data can be reused for wearable-sensor, video-based, skeleton-based, multi-view, occlusion-aware, dynamic-camera, and multimodal human activity recognition and fall-detection research.