Aug 2026· Electronics· Vol 15, pp. 3375· 0 citations· 21 references
TL;DR
CStick 2.0 is a vision-enabled, IoMT-edge based smart walking-stick prototype that combines multimodal fall-risk classification with embedded obstacle awareness and Synchronized older-adult evaluation, device-to-application communication, secure caregiver services, multimodal accessibility feedback, and longitudinal personalization remain future validation stages.
Abstract
Falls among older adults can cause serious injury and loss of independence. cStick 2.0 is a vision-enabled, IoMT-edge based smart walking-stick prototype that combines multimodal fall-risk classification with embedded obstacle awareness. The fall-risk classifiers were evaluated using a 9670-record development dataset, on which the compact DNN achieved 95.40% accuracy, 92.35% balanced accuracy, a macro F1-score of 93.90%, and a ROC-AUC of 97.44%. The Arduino Nicla Vision obstacle module used an INT8 Edge Impulse model with centroid-based direction assignment and time-of-flight distance sensing; 144 controlled trials produced 75.00% obstacle-presence accuracy at approximately 19–20 FPS. Sensor acquisition, GPS, display output, buzzer response, and CSV record accumulation were demonstrated at a prototype level. Synchronized older-adult evaluation, device-to-application communication, secure caregiver services, multimodal accessibility feedback, and longitudinal personalization remain future validation stages.
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
Artificial intelligence can extend the functional capabilities of embedded sensors by extracting application-level information from physical measurements. This study investigates whether footstep-induced floor vibrations acquired with a single triaxial MEMS accelerometer contain sufficient information to characterize pedestrian path orientation and travel sense. A custom sensing platform based on an ADXL355 accelerometer and an ESP32 microcontroller was developed to acquire the structural vibration response at 4 kSPS. Lightweight temporal features were processed using a Random Forest classifier. The primary assessment used leakage-aware event-level cross-validation, with complete footsteps as the data-partitioning units. Under this more conservative protocol, discrimination of the complete A–K movement-label set was poor, whereas a compact 12-dimensional descriptor representation achieved 73.18% accuracy, 71.52% balanced accuracy, and 70.98% macro-F1 for X/Y path-orientation classification. Reliable positive/negative travel-sense discrimination could not be demonstrated from isolated footsteps. For historical comparison, the original sample-level procedure yielded 97.09% accuracy, but this value is retained only as a within-sequence reference because densely sampled observations contain strongly overlapping information. The findings provide proof-of-concept evidence that a single floor-mounted MEMS accelerometer can capture coarse pedestrian path-orientation information without cameras or spatially distributed vibration-sensor networks. Feature extraction and classifier inference were performed offline; broader validation across participants, sessions, floor structures, and realistic disturbances is required before deployment as an embedded edge AI sensing node.
Unknown authors· Italian National Conference...· 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
Millions of people worldwide suffer from motor impairments that have a significant impact on their independence and mobility. Standard wheelchairs need residual limb function and are not accessible to people who do not have it - for example, people with advanced-stage muscular dystrophy, quadriplegia, and ALS. In this paper, we describe IRIS, an affordable, non-invasive, real-time, gaze-controlled wheelchair navigation system to fill this void. This system combines intensity-based iris localization implemented by OpenCV with an L298N motor driver and an Arduino Uno microcontroller to convert gaze direction to the commands of a wheelchair. Under laboratory conditions indoors, the accuracy of gaze classification (five classes) is 87% on average, while the latency of the entire system is less than 200 milliseconds (ms) and the processing rate of the frame is 20-30 frames per second (fps). The three-sensor HC-SR04 ultrasonic obstacle detection module was able to ensure collision avoidance with $\mathbf{1 0 0 \%}$ reliability in a $\mathbf{3 0 ~ c m}$ safety range. The proposed system demonstrates the construction of an effective and low-cost mobility assistive platform without relying on computationintensive deep learning models, and provides a platform in which the embedded inference and advanced gaze estimation algorithms can be integrated in the future.
K. Lakshmi, Manas Shukla, Karthik R Nair et al.· International Conference on...· 0 citations
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