This paper presents DCentNet, a novel decentralized multistage signal classification approach for biomedical data obtained from Internet of Things (IoT) wearable sensors, utilizing early exit point (EEP) to improve both energy e fficiency and processing speed. Traditionally, IoT sensor data is processed in a centralized manner on a single node, Cloud-native or Edge-native, which comes with several restrictions, such as significant energy consumption on the edge sensor and greater latency. To address these limitations, we propose DCentNet, a decentralized method based on Convolutional Neural Network (CNN) classifiers, where a single CNN model is partitioned into several sub-networks using one or more EEPs. Our method introduces encoder-decoder pairs at EEPs, which serve to compress large feature maps before transferring them to the next sub-network, drastically reducing wireless data transmission and power consumption. When the input can be confidently classified at an EEP, the processing can terminate early without traversing the entire network. To minimize sensor energy consumption and overall complexity, the initial sub-networks can be set up in the fog or on the edge. We also explore di fferent EEP locations and demonstrate that the choice of EEP can be altered to achieve a trade-o ff between performance and complexity by employing a genetic algorithm approach. DCentNet addresses the limitations of centralized processing in IoT wearable sensor data analysis, o ff ering improved e fficiency and performance. The experimental results of electrocardiogram (ECG) classification validate the success of our proposed method. With one EEP, the system saves 94.54% of wireless data transmission and a corresponding 21% decrease in complexity, while the classification accuracy and sensitivity remain almost una ffected and stay at their original levels. When employing two EEPs, the system demonstrates a sensitivity of 98.36% and an accuracy of 97.74%, concurrently leading to a 91.86% reduction in wireless data transmission and a reduction in complexity by 22%. DCentNet is implemented on an ARM Cortex-M4 based microcontroller unit (MCU).

In this paper, we investigate the effectiveness of contrastive learning methods for predicting grasp outcomes in an unsupervised manner. By utilizing a publicly available dataset, we demonstrate that contrastive learning methods perform well on the task of grasp outcomes prediction. Specifically, the dynamic-dictionary-based method with the momentum updating technique achieves a satisfactory accuracy of 81.83% using data from one single tactile sensor, outperforming other unsupervised methods. Our results reveal the potential of contrastive learning methods for applications in the field of robot grasping and highlight the importance of accurate grasp prediction for achieving stable grasps.