As the elderly population grows, falling accidents become more frequent, and the need for fall-risk monitoring systems increases. Deep learning models for fall-risk movement detection neglect the connections between the terrain and fall-hazard movements. This issue can result in false alarms, particularly when a person encounters changing terrain. This work introduces a novel multisensor system that integrates terrain perception sensors with an inertial measurement unit (IMU) to monitor fall-risk on diverse terrains. Additionally, a dual-task learning (DTL) architecture that is based on a modified CNNLSTM model is implemented; it is used to determine fall-risk level and the terrain from sensor signals. Three fall-risk levels - "normal," "near-fall," and "fall" - are identified as being associated with "flat ground," "stepping up," and "stepping down" terrains. Ten young subjects performed 16 activities on flat and stepping terrains in a laboratory setting, and ten elderly individuals were recruited to perform four activities in the hospital. The accuracies of classification of fall-risk levels and terrains by the proposed system are 97.6% and 95.2%, respectively. The system detects pre-impact fall movements, with a fall prediction accuracy of 97.7% and an average lead time of 329ms for fall trials, revealing the model's effectiveness. The overall monitoring accuracy for elderly individuals is 99.8%, confirming the robustness of the proposed system. This work discusses the impact of sensor type and the model architecture of DTL on the classification of fall-risk levels across various terrains. The results demonstrate that the proposed method is reliable for monitoring the risk of falling.

This paper presents a reliable and robust palmprint verification approach that involves using a bi-feature, biometric, palmprint feature-point number (FPN) and a histogram of oriented gradient (HOG). The bi-feature was fused and verified using a support vector machine (SVM) at the feature level. The approach has the advantages of capturing palm images in pegless scenarios with a low cost and low-resolution (100 dpi) digital scanner, and one sensor can capture palmprint bi-feature information. The low-resolution images result in a smaller database. Nine thousand palmprint images were collected from 300 people to verify the validity of the proposed approach. The results showed an accurate classification rate of 99.04%. The experimental results demonstrated that the proposed approach is feasible and effective in palmprint verification. Our findings will help extend palmprint verification technology to security access control systems.

A novel approach to personal verification using the thermal images of palm-dorsa vein patterns is presented in this paper. The characteristics of the proposed method are that no prior knowledge about the objects is necessary and the parameters can be set automatically. In our work, an infrared (IR) camera is adopted as the input device to capture the thermal images of the palm-dorsa. In the proposed approach, two of the finger webs are automatically selected as the datum points to define the region of interest (ROI) on the thermal images. Within each ROI, feature points of the vein patterns (FPVPs) are extracted by modifying the basic tool of watershed transformation based on the properties of thermal images. According to the heat conduction law (the Fourier law), multiple features can be extracted from each FPVP for verification. Multiresolution representations of images with FPVPs are obtained using multiple multiresolution filters (MRFs) that extract the dominant points by filtering miscellaneous features for each FPVP. A hierarchical integrating function is then applied to integrate multiple features and multiresolution representations. The former is integrated by an inter-to-intra personal variation ratio and the latter is integrated by a positive Boolean function. We also introduce a logical and reasonable method to select a trained threshold for verification. Experiments were conducted using the thermal images of palm-dorsas and the results are satisfactory with an acceptable accuracy rate (FRR:2.3% and FAR:2.3%). The experimental results demonstrate that our proposed approach is valid and effective for vein-pattern verification.