Different levels of visual fatigue in the human eye depend on different color-formation methods and image quality. This paper uses the high-frequency component of the spectral power of accommodative microfluctuations as a major objective indicator for analyzing the effects of visual fatigue based on various displays, such as color-formation displays and 3D displays. Also, a questionnaire is used as a subjective indicator. The results are that 3D videos cause greater visual fatigue than 2D videos (p<0.001), the shutter-type 3D display causes visual fatigue more than the polarized type (p=0.012), the display of the time-sharing method causes greater visual fatigue than the spatial-formation method (p=0.008), and there is no significance between various light source modules of displays (p=0.162). In general, people with normal color discrimination have more visual fatigue than those with good color discrimination (p<0.001). Therefore, this paper uses the high-frequency component of accommodative microfluctuations to evaluate the physiological stress or strain by overexerting the visual system, and can compare the level of visual fatigue between various displays.

Background EEGs are frequently employed to measure cerebral activations during physical exercise or in response to specific physical tasks. However, few studies have attempted to understand how exercise-state brain activity is modulated by exercise intensity. Methods Ten healthy subjects were recruited for sustained cycle ergometer exercises at low and high resistance, performed on two separate days a week apart. Exercise-state EEG spectral power and phase-locking values (PLV) are analyzed to assess brain activity modulated by exercise intensity. Results The high-resistance exercise produced significant changes in beta-band PLV from early to late pedal stages for electrode pairs F3-Cz, P3-Pz, and P3-P4, and in alpha-band PLV for P3-P4, as well as the significant change rate in alpha-band power for electrodes C3 and P3. On the contrary, the evidence for changes in brain activity during the low-resistance exercise was not found. Conclusion These results show that the cortical activation and cortico-cortical coupling are enhanced to take on more workload, maintaining high-resistance pedaling at the required speed, during the late stage of the exercise period.

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Diabetes is a familiar disease in modern society. In the early stage of diabetes, symptoms are unobvious, but they usually induce diabetic autonomic neuropathy or, worse, cardiovascular autonomic neuropathy. Pupillometers are effective instruments for observing human pupils. This article presents a novel wearable pupillometer design, without external light artifacts, and an embedded algorithm with blinking elimination, which investigates autonomic neuropathy through recording pupil dynamics triggered by an external sensitive invisible light source. The pupillometer is experimented on 36 healthy subjects and 10 diabetic patients under four different colors (white, red, green, and blue) as well as two different light intensities: 50 and 500 mcd. Ten parameters derived from pupil diameter, pupil response time, and pupil response speed will be evaluated for the healthy subjects and diabetic patients. The results show that three in four parameters related to pupil diameters, one in four related to light intensities, and one in two related to pupil response speed could have significant differences (p<0.05) between healthy subjects and diabetic patients. These parameters obtain over 85% sensitivity, 83% specificity, and 88% accuracy. The pupillometer is proven reliable, effective, portable, and inexpensive for diagnosing diabetes in an early stage.

Fall detection and physical activity (PA) classification are important health maintenance issues for the elderly and people with mobility dysfunctions. The literature review showed that most studies concerning fall detection and PA classification addressed these issues individually, and many were based on inertial sensing from the trunk and upper extremities. While shoes are common footwear in daily off-bed activities, most of the aforementioned studies did not focus much on shoe-based measurements. In this paper, we propose a novel footwear approach to detect falls and classify various types of PAs based on a convolutional neural network and recurrent neural network hybrid. The footwear-based detections using deep-learning technology were demonstrated to be efficient based on the data collected from 32 participants, each performing simulated falls and various types of PAs: fall detection with inertial measures had a higher F1-score than detection using foot pressures; the detections of dynamic PAs (jump, jog, walks) had higher F1-scores while using inertial measures, whereas the detections of static PAs (sit, stand) had higher F1-scores while using foot pressures; the combination of foot pressures and inertial measures was most efficient in detecting fall, static, and dynamic PAs.

One major drawback of orthogonal frequency division multiplexing schemes is the high peak-to-average power ratio (PAPR) of the output signal. Selected mapping (SLM) and partial transmit sequences (PTS) are two important techniques for reducing PAPR, but they need to transmit side information to indicate how the transmitter generates the signals. Guided scrambling (GS) SLM and GS-PTS techniques use augmenting bits to set the scrambler's initial condition. With different patterns of the augmenting bits, different candidate signals can be generated. GS-SLM and GS-PTS do not require the transmission of side information, but they still need a bank of inverse fast Fourier transforms (IFFT's), i.e., involving high computational complexity. In this paper, we focus on reducing the high computational complexity of the GS-SLM and GS-PTS methods. We separate the operations of the augmented data word into the operations of the augmenting bits and the operations of the source data word. Different augmenting bits are processed in advance and the results are saved into a read-only memory (ROM). The proposed methods only need one IFFT, few adders, and a ROM and thus significantly reduce the computational complexity of the original GS-SLM and GS-PTS methods. The simulation results also show that the proposed GS-SLM and GS-PTS methods have almost the same PAPR reduction performance as the original ones.

This paper presents the performance analysis of the multiband orthogonal frequency division multiplexing (MB-OFDM) ultra-wideband (UWB) systems for multipath fading and multiuser interference channels. A closed form approximation of the BER performance of the MB-OFDM UWB system with multiple interferences is proposed. Based on the derived approximation, the effects on the BER performance for the choice of the codeword constraint lengths of the convolutional encoder, the length of the cyclic prefix, and the multiuser environments of two or more interferers are thoroughly discussed. Four UWB multipath fading channels are also investigated for the BER performance of the MB-OFDM UWB system. The simulated results provide us with useful information to appropriately choose the parameters of the MB-OFDM UWB system for the sake of achieving the BER performance that conforms to requirement of the FCC standards.

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