With the continued development and rapid growth of wearable technologies, PPG has become increasingly common in everyday consumer devices such as smartphones and watches. There is, however, minimal knowledge on the effect of the contact pressure exerted by the sensor device on the PPG signal and how it might affect its morphology and the parameters being calculated. This study explores a controlled in vitro study to investigate the effect of continually applied contact pressure on PPG signals (signal-to-noise ratio (SNR) and 17 morphological PPG features) from an artificial tissue-vessel phantom across a range of simulated blood pressure values. This experiment confirmed that for reflectance PPG signal measurements for a given anatomical model, there exists an optimum sensor contact pressure (between 35.1 mmHg and 48.1 mmHg). Statistical analysis shows that temporal morphological features are less affected by contact pressure, lending credit to the hypothesis that for some physiological parameters, such as heart rate and respiration rate, the contact pressure of the sensor is of little significance, whereas the amplitude and geometric features can show significant change, and care must be taken when using morphological analysis for parameters such as SpO2 and assessing autonomic responses.

The electrocardiogram (ECG) is the main measurement parameter for effectively diagnosing chronic disease and guiding cardio-fitness therapy. ECGs contaminated by noise or artifacts disrupt the normal functioning of the automatic analysis algorithm. The objective of this study is to evaluate a method of measuring the HCP variation in motion artifacts through direct monitoring. The proposed wearable sensing device has two channels. One channel is used to measure the ECG through a differential amplifier. The other is for monitoring motion artifacts using the modified electrode and the same differential amplifier. Noise reduction was performed using adaptive filtering, based on a reference signal highly correlated with it. Direct measurement of HCP variations can eliminate the need for additional sensors.

We developed an alternative computer interface using surface electromyography (sEMG) for individuals with spinal cord injuries (SCI) to access a computer. We designed this interface to make a cursor move on a two-dimensional screen and to click using only three muscles: the extensor carpi radialis (R-ECR) and extensor carpi ulnaris (R-ECU) of the right forearm and the extensor carpi radialis (L-ECR) of the left forearm. In addition, a user can voluntarily control cursor movement speed by modulating muscle contraction levels. One SCI patient and 5 healthy subjects volunteered to evaluate the performance of this interface using Fitts' law test in a two-dimensional testing setup. The throughputs (TP) of our interface were 0.2604 b/s for the SCI patient and 0.4295 ± 0.0600 b/s for the healthy subjects. This interface could help SCI patients handle a wider range of activities such as browsing the Internet and communicating with others.

We present a QRS detection algorithm for wearable ECG applications using a proportional-derivative (PD) control. ECG data of arrhythmia have irregular intervals and magnitudes of QRS waves that impede correct QRS detection. To resolve the problem, PD control is applied to avoid missing a small QRS wave followed from a large QRS wave and to avoid falsely detecting noise as QRS waves when an interval between two adjacent QRS waves is large (e.g. bradycardia, pause, and arioventricular block). ECG data was obtained from 78 patients with various cardiovascular diseases and tested for the performance evaluation of the proposed algorithm. The overall sensitivity and positive predictive value were 99.28% and 99.26%, respectively. The proposed algorithm has low computational complexity, so that it can be suitable to apply mobile ECG monitoring system in real time.

Current manual joysticks have been widely used to control various artificial devices, but they are expensive and composed of mechanically bulky frames. To address these issues, we developed a myoelectric joystick using surface electromyogram (sEMG) from six muscles that make a wrist joint move. Fluid wrist movements were estimated by introducing a non-negative muscle synergy matrix and a joint synergy matrix. Only four movements were predefined (wrist extension, wrist flexion, radial deviation, and ulnar deviation) to construct the muscle synergy matrix, but an experimental result showed that a variety of movements (e.g., a combination of wrist extension and ulnar deviation) could be extracted using the joint matrix. This work also could be extended for development of an alternative computer interface and powered wrist prosthesis for individuals with transradial or wrist disarticulation level amputation.

We have demonstrated a tactile-pattern-integrated sensing window for more consistent photoplethysmogram (PPG) measurements. The pattern is composed of two tiny bumps that measure 500μm in diameter and 300μm in height and allow users to position their finger pulps more consistently on the sensing window over different measurement occasions, simply by following their tactile sensation. We experimentally compared the tactile pattern window to a flat window (without any bumps) for 5 test subjects and found that the sensing window with the tactile pattern significantly helped users obtain more consistent PPG signals than the flat window (p < 0.01).The use of PPG sensors in mobile phones and wearable watches have been limited to the measurements of heart rates and blood oxygen saturation in spite of widely-spread efforts to expand their applications. This is due to the fluctuations observed between measurements which largely originate from inconsistent placement of fingers on the sensing windows. The integrated tactile pattern could provide consistent and accurate measurements and lead to more successful commercialization of diverse PPG-based mobile healthcare services.