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Capacitive sensing is a prominent technology that is cost-effective and low power consuming with fast recognition speed compared to existing sensing systems. On account of these advantages, Capacitive sensing has been widely studied and commercialized in the domains of touch sensing, localization, existence detection, and contact sensing interface application such as human-computer interaction. However, as a non-contact proximity sensing scheme is easily affected by the disturbance of peripheral objects or surroundings, it requires considerable sensitive data processing than contact sensing, limiting the use of its further utilization. In this paper, we propose a real-time interface control framework based on non-contact hand motion gesture recognition through processing the raw signals, detecting the electric field disturbance triggered by the hand gesture movements near the capacitive sensor using adaptive threshold, and extracting the significant signal frame, covering the authentic signal intervals with 98.8% detection rate and 98.4% frame correction rate. Through the GRU model trained with the extracted signal frame, we classify the 10 hand motion gesture types with 98.79% accuracy. The framework transmits the classification result and maneuvers the interface of the foreground process depending on the input. This study suggests the feasibility of intuitive interface technology, which accommodates the flexible interaction between human to machine similar to Natural User Interface, and uplifts the possibility of commercialization based on measuring the electric field disturbance through non-contact proximity sensing which is state-of-the-art sensing technology.Preprint. Under review.

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As contemporary systems are being operated in dynamic situations alternating into decentralized and distributed environments from conventional centralized frameworks, Federated Learning (FL) has been gaining attention for an effective architecture when aggregating the information and correspondingly trained AI model of geographically distributed datasets. However, the convergence of FL suffers from the skewed and biased non-IID local dataset acquired from the heterogeneous environment, which is common in real-world practice. To address this issue, we propose a novel Label-wise clustering algorithm in FL (FedLC) that guarantees the convergence among local clients that hold unique distribution. We theoretically analyze the non-IID attributes that potentially affect the performance, defining the six non-IID scenarios in hierarchical order. Through conducting experiments on the suggested non-IID scenarios, we empirically show that the vanilla FL aggregation model is incapable of gaining stable convergence, generating a biased model. By pre-evaluating the local dataset prior to training models, FedLC determines the potential contributions with respect to trainability in a global view, and adaptively selects the locals to collaboratively take part in while aggregation. Our experimental results show that the FedLC outperforms the state-of-theart non-IID FL optimization studies, offering a robust convergence in a highly skewed and biased non-IID dataset.INDEX TERMS Convergence optimization in federated learning, federated learning in heterogenous and non-IID dataset, label-wise clustering.

In this paper, we propose a BPR-CNN (Biometric Pattern Recognition-Convolution Neural Network) classifier for hand motion classification as well as a dynamic threshold algorithm for motion signal detection and extraction by EF (Electric Field) sensors. Currently, an EF sensor or EPS (Electric Potential Sensor) system is attracting attention as a next-generation motion sensing technology due to low computation and price, high sensitivity and recognition speed compared to other sensor systems. However, it remains as a challenging problem to accurately detect and locate the authentic motion signal frame automatically in real-time when sensing body-motions such as hand motion, due to the variance of the electric-charge state by heterogeneous surroundings and operational conditions. This hinders the further utilization of the EF sensing; thus, it is critical to design the robust and credible methodology for detecting and extracting signals derived from the motion movement in order to make use and apply the EF sensor technology to electric consumer products such as mobile devices. In this study, we propose a motion detection algorithm using a dynamic offset-threshold method to overcome uncertainty in the initial electrostatic charge state of the sensor affected by a user and the surrounding environment of the subject. This method is designed to detect hand motions and extract its genuine motion signal frame successfully with high accuracy. After setting motion frames, we normalize the signals and then apply them to our proposed BPR-CNN motion classifier to recognize their motion types. Conducted experiment and analysis show that our proposed dynamic threshold method combined with a BPR-CNN classifier can detect the hand motions and extract the actual frames effectively with 97.1% accuracy, 99.25% detection rate, 98.4% motion frame matching rate and 97.7% detection & extraction success rate.