2026· IEEE Transactions on Radar Systems· Vol 4, pp. 1349-1360· 0 citations· 51 references
Abstract
Accidental falls among the elderly demand highly reliable detection systems; however, existing solutions based on wearables, cameras, or WiFi often suffer from environmental interference, privacy concerns, and poor performance in detecting slow-onset falls (e.g., fainting). Despite these challenges, millimeter-wave (mmWave) radar-based methods have emerged as an attractive alternative. In this article, we propose an effective and efficient fall detection method, mmFallBbox, which leverages mmWave radar to track 3-D human bounding box dynamics. By exploiting the correlation between fall states and bounding box evolution, our approach effectively distinguishes between normal activities and slow fall states, such as fainting or medical conditions, which traditional systems struggle to detect. To evaluate the performance of mmFallBbox, we collected a large-scale fall detection dataset consisting of 60 h of radar data synchronized with video annotations. This dataset will be made publicly available to the research community for further development. We achieved an $F1$ score of 0.977 on our dataset and achieved state-of-the-art performance with limited computational complexity. Moreover, extensive experimental results show significant improvements in detecting slow-onset falls and providing explainable outputs, offering a promising solution for real-world fall detection applications.
This survey provides a systematic overview of cutting-edge research on Wi-Fi-enabled indoor human activity detection, classify mainstream technologies along three dimensions: signal processing pipelines, learning paradigms, and application granularity, and further dissect core challenges including environmental adaptability, data scarcity, and system scalability.
Zengqian Song, Jingming Li· Journal of Advances in Engin...· 0 citations
The aging population has elevated falls into a critical public health issue. While camera-based YOLO algorithms offer non-contact detection, standard YOLOv13 struggles with occlusion, similar postures, and high computational demands. To address this, we propose RDD-YOLO, a task-oriented architecture optimized for fall detection accuracy and efficiency. Rather than simply stacking existing modules, RDD-YOLO assigns RepViTBlock to backbone feature extraction, DySample to detail-preserving feature fusion, and DHead to multi-scale regression, so that each component plays a complementary role in the detection pipeline. Evaluated on refined URFD and MCF datasets, RDD-YOLO outperformed YOLOv13, RT-DETR, and Faster-RCNN. For the URFD and MCF datasets, the mAP reached 92.1% and 87.3%, respectively. Speed tests showed that on a laptop (in a WSL2 environment), inference speed increased from approximately 47 FPS to approximately 57 FPS. Through pruning and FP16 half-precision inference, the speed further increased to over 70 FPS, demonstrating the feasibility of this method in real-time, resource-constrained application scenarios.
Overall, the study demonstrates that combining robust biomechanical features with multimodal sensing and deployment-aware resource management provides an effective foundation for practical privacy-preserving fall detection.
Sona Mundody, R. R. Guddeti· IEEE Access· 0 citations
The findings show that CNN-based architectures dominate algorithm choice, edge devices dominate deployment platforms, and optimization remains central to real-time inference on constrained hardware.
Mahammad Nabizade, Réda Yahiaoui, Isabelle Lajoie et al.· Italian National Conference...· 0 citations
Fall detection in multi-person environments, such as nursing homes and rehabilitation centers, is essential for ensuring the safety of vulnerable populations. Despite advances in deep learning, current vision- and skeleton-based fall detection systems often exhibit false negatives and reduced reliability in real-world scenarios due to scene complexity. This study presents a system-level analysis of fall detection errors by comparing four approaches—two skeleton-based methods using ST-GCN and ProtoGCN, a rule-based method, and a VIRA-GCN-based 3D joint method—on 95 RGB video sequences captured under minimally constrained multi-person conditions. We define six error types: skeleton structural interference, localized joint recognition failure, temporal skeleton identity inconsistency, object-to-skeleton association failure, viewpoint-induced observation limitation, and action-level ambiguity with similar activities. Although most methods achieved high event-level recall, their false-positive and false-negative patterns differed. The rule-based approach showed the most balanced performance, under the present experimental conditions, whereas the ST-GCN-based skeleton approach was more sensitive to joint-level and tracking instability. ProtoGCN reduced false positives but increased false negatives, showing a more conservative decision pattern. The VIRA-GCN-based 3D joint approach provided spatial cues but did not eliminate upstream pose and tracking errors. These results highlight the need for skeleton–depth fusion, robust identity tracking, occlusion handling, and enhanced joint recognition in real-world multi-person fall detection.
Han-Ye-Ang Lee, In-Nea Wang, Junho Jeong· Applied Sciences· 0 citations
Falls among older adults are a leading cause of injury and hospitalisation, yet existing fall detection devices, such as wearable sensors or video monitoring, encounter issues due to user compliance and privacy concerns. This paper presents a low-cost, privacy preserving fall detection system using a rotating LiDAR sensor mounted on a servo which is controlled by a simple microcontroller. A full sweep of a room captures approximately ten thousand depth points, which are streamed to a PC for pre-processing, feature extraction, and classification. Key geometric and distributional features are derived from point clouds and classified using a Random Forest model. Evaluation on fifty-three labelled scans achieved ninety-six-point two percent overall accuracy with only two misclassifications. These results demonstrate that affordable LiDAR combined with lightweight machine learning offers a viable path toward privacy-preserving, fall detection in care environments.
Gary Alexander Warner· E3S Web of Conferences· 0 citations
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