Multi-task deep learning with fisheye cameras for patient fall detection: development and real-world hospital deployment

Falls in hospital wards remain a significant safety concern, and conventional monitoring relying on intermittent observations may delay escalation. We developed a ceiling-mounted, top-view fisheye framework integrating a Panoptic-DeepLab-inspired, multi-task model within real-time ward operations. The network employs an EfficientNetV2-S encoder and bidirectional feature pyramid network decoders with parallel heads for patient detection, eight-class posture recognition, and seven-class human–object interaction classification, supported by auxiliary depth and normal supervision during training. We analyzed a de-identified, non-public actor-staged dataset collected at hospital and hospital-like sites (nine participants, 40 scripted actions) with fine-grained annotations, including skeletons, postures, interactions, and object polygons. Evaluation used leave-one-site-out cross-validation across the recording sites. The proposed system achieved a mean fall-positive precision of 0.9829 ± 0.0297, recall of 0.9219 ± 0.2040, and F1-score of 0.9343 ± 0.1766. During real-world deployment across seven wards over 1,450 monitoring days, the system recorded 325 true falls and 695 false alarms, corresponding to an average false-alarm frequency of 0.48 per monitoring day. Seated falls occurred as frequently as recumbent falls, and over 20,000 non-alarming “fall cautions” reflected the prevalence of bed-edge dynamics in routine care. These findings support the feasibility of context-aware, ceiling-mounted fisheye monitoring for room-scale fall surveillance; however, broader prospective validation remains necessary to establish generalizability across routine clinical environments.