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In recent years, high-density surface electromyography (HD-sEMG) has shown promising advantages in many robotics applications. Using HD-sEMG can not only reduce the sensitivity of the sensor position on the muscle belly but can also facilitate the acquisition of more muscle activity information due to spatial sampling. As current commercial HD-EMG systems use stationary amplifiers, leading to bulky measurements and poor portability, the interest in developing an HD-EMG sensor has increased. However, the insufficient electrode density and complicated fabrication process are challenges to overcome. In this paper, we propose a flexible HD-EMG sensor with an on-board amplifier capable of a density level of 0.53 channel/cm2, higher than those in previous works. First, we investigated the effects of different sensor parameters (i.e., the electrode material, the inter-electrode distance (IED) and the size of the electrode) on the measured signal quality. Second, a low-cost, easily fabricated, easily customized HD-EMG fabrication method was proposed based on the selected sensor parameters with a signal-to-noise ratio (SNR) comparable to those of commercial sensors. Finally, we applied a muscle activation estimation algorithm to validate the feasibility of the designed HD-EMG sensor, showing higher estimation accuracy levels. The results here demonstrate that the designed HD-EMG sensor can be used as an effective human-machine interface for robotics applications.A surface electromyography (sEMG) detector, that not only removes stimulation artifacts entirely but also increases the recording time, has been developed in this paper. The sEMG detector consists of an sEMG detection circuit and a stimulation isolator. The sEMG detection circuit employs a stimulus isolate switch (SIS), a blanking (BLK) and non-linear feed-back (NFB) circuit to remove the artifacts and to increase the recording time. In the SIS, the connection between stimulator and stimulation electrodes, along with the stimulation electrodes and the ground are controlled by an opto-isolator, and the connection of instrument amplifier and the recording electrodes are controlled by CMOS-based switches. The mode switches of the BLK and the NFB circuit also employs CMOS-based switches. By an accurate timing adjustment, the voluntary EMG can be recorded during electrical stimulation. Two 6 able-bodied experiments have been performed to test the three anti-artifact sEMG detector BLK, BLK&amp;SIS, BLK&amp;SIS&amp;NFB. The results indicate that the BLK&amp;SIS&amp;NFB proposed in this work effectively removes stimulus artifacts and M-waves, and has a longer recording time compared with BLK and BLK&amp;SIS circuits.The cardiac ECG is one of the most important human biometrics. An electrocardiogram (ECG) or EKG, captures the electrical activity of the heart and allows a healthcare professional to evaluate, diagnose, and monitor patient cardiac condition. The standard method to capture electrocardiogram signals (ECG) involves skin preparation and attachment of wet electrodes to the skin, which is not comfortable for the patient and requires a trained technician. In this work, a novel contactless-based ECG system is proposed, where 128 sensors are deployed on a mattress to capture the ECG information from the back of the patient. The proposed system can capture the ECG through clothing and is more comfortable to the patients. The measurements captured by the proposed system provides a 100% accuracy of QRS complex detection and heartbeat rate estimation and a maximum of 4% error in other major ECG features compared to a hospital-grade standard system. This paper shows that ECG features can be accurately extracted from contactless electrodes, through clothing and from the back of the patient.This paper introduces an automatic non-contact monitoring method for measuring the respiratory rate of neonates using a structured light camera. The current monitoring bears several issues causing pressure marks, skin irritations and eczema. A structured light camera provides distance data. Our non-contact approach detects the thorax area automatically using a plane segmentation and calculates the respiratory rate from the movement of the thorax. Our method was tested and validated using the baby simulator SimBaby by Laerdal. We used different breathing rates corresponding to preterm neonates, mature neonates and babies aged up to nine months as well as two different breathing modes with differing breathing strokes. Furthermore, measurements were taken of two positions the baby lying on its back and on its stomach.Assessment of pulmonary function is vital for early detection of chronic diseases such as chronic obstructive pulmonary disease (COPD) in home healthcare. However, monitoring of pulmonary function is often omitted owing to the heavy burden that the use of specific medical devices places on the patients. In this study, we developed a non-contact spirometer using a time-of-flight sensor that measures very small displacements caused by chest wall motion during breathing. However, this sensor occasionally failed when estimating the values from breathing waveforms because their shape depends on the subject test experience. As a result, further measurements were required to address motion artifacts. To accomplish high accuracy estimation in the face of these factors, we developed methods to estimate parameters from a part of the waveform and remove outliers from multiple-region measurements. According to laboratory experiments, the proposed system achieved an absolute error of 5.26 % and a correlation coefficient of 0.88. This study also addressed the limitations of depth sensor measurements, thereby contributing to the implementation of high-accuracy COPD screening.Human-machine interface with muscle signals serves as an important role in the field of wearable robotics. To compensate for the limitations of the existing surface Electromyography (sEMG) based technologies, we previously proposed a noncontact capacitive sensing approach that could record the limb shape changes. The sensing approach frees the human skin from contacting to the metal electrodes, thus enabling the measurement of muscle signals by dressing the sensing front-ends outside of the clothes. We validated the capacitive sensing in human motion intent recognition tasks with the wearable robots and produced comparable results to existing studies. However, the biological significance of the capacitance signals is still unrevealed, which is an indispensable issue for robot intuitive control. In this study, we address the problems of identifying the relationships between the muscle morphological parameters and the capacitance signals. [https://www.selleckchem.com/products/FK-506-(Tacrolimus).html Tacrolimus] We constructed a measurement system that recorded the noncon-tact capacitive sensing signals and the muscle ultrasound (US) images simultaneously.