We propose a novel dual-channel electromyography (EMG) spatio-temporal differential (DESTD) method that can estimate volitional electromyography (vEMG) signals during time-varying functional electrical stimulation (FES). The proposed method uses two pairs of EMG signals from the same stimulated muscle to calculate the spatio-temporal difference between the signals. We performed an experimental study with five healthy participants to evaluate the vEMG signal estimation performance of the DESTD method and compare it with that of the conventional comb filter and Gram–Schmidt methods. The normalized root mean square error (NRMSE) values between the semi-simulated raw vEMG signal and vEMG signals which were estimated using the DESTD method and conventional methods, and the two-tailed $\mathit{t}$-test and analysis of variance were conducted. The results showed that under the stimulation of the gastrocnemius muscle with rapid and dynamically modulated stimulation intensity, the DESTD method had a lower NRMSE compared to the conventional methods (p < 0.01) for all stimulation intensities (maximum 5, 10, 15, and 20 mA). We demonstrated that the DESTD method could be applied to wearable EMG-controlled FES systems because it estimated vEMG signals more effectively compared to the conventional methods under dynamic FES conditions and removed unnecessary FES-induced EMG signals.

Full duplex is one of the main strategies for improving the spectral efficiency in DOCSIS 3.1 system. For self-interference cancellation of full duplex environment, various distortion elements should be estimated and compensated. Among the distortion elements of self-interference signal, channel distortion between transmission antenna and reception antenna is most dominant distortion element in the full duplex-based DOCSIS 3.1 system. This paper proposes an efficient frequency domain channel estimation and compensation technique of self-interference signal in DOCSIS 3.1 with full duplex. The proposed technique uses high-power data subcarriers as well as pilot symbols. The proposed technique can accomplish higher MER performance than existing channel estimation technique with only pilot symbols.

In this study, we collected data on human falls, occurring in four directions while walking or standing, and developed a fall recognition system based on the center of mass (COM). Fall data were collected from a lower-body motion data acquisition device comprising five inertial measurement unit sensors driven at 100 Hz and labeled based on the COM-norm. The data were learned to classify which stage of the fall a particular instance belongs to. It was confirmed that both the representative convolutional neural network learning model and the long short-term memory learning model were performed within a time of 10 ms on the embedded platform (Jetson TX2) and the recognition rate exceeded 94%. Accordingly, it is possible to verify the progress of the fall during the unbalanced and falling steps, which are classified by subdividing the critical step in which the real-time fall proceeds with the output of the fall recognition model every 10 ms. In addition, it was confirmed that a real-time fall can be judged by specifying the point of no return (PONR) near the point of entry of the falling down stage.

This paper presents a method for transmitting IP-based broadcast stream, a transport layer protocol of ATSC 3.0 standard adopted as terrestrial UHDTV broadcasting standard, in existing cable broadcasting network without conversion or modification. In addition to re-transmitting the terrestrial UHDTV service to the cable network, the cable broadcasting system uses IP-based broadcasting stream as the transport layer protocol of L2 or higher without changing the transmission method of the physical layer in order to accommodate various IP-based TV services ¹ .