Effects of Contact Pressure in Reflectance Photoplethysmography in an In Vitro Tissue-Vessel Phantom.

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.

A Tactile-Pattern-Integrated Sensing Window for More Consistent Photoplethysmography (PPG) Measurements.

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.

A Study on Estimation of Joint Force Through Isometric Index Finger Abduction With the Help of SEMG Peaks for Biomedical Applications.

We propose a new method to estimate joint force using a biomechanical muscle model and peaks of surface electromyography (SEMG). The SEMG measurement was carried out from the first dorsal interosseous muscle during isometric index finger abduction. The SEMG peaks were used as the input of the biomechanical muscle model which is a transfer function to generate the force. The force estimation performance ( R(2) ) was evaluated using the proposed method with nine healthy subjects, and a former method using a mean absolute value (MAV), which is the full-wave rectified and averaged (or low-pass filtered) signal of SEMG in a time window, was compared with the proposed method; the performance of the proposed method (0.94 ± 0.03) was better than that of MAV (0.90 ± 0.02). The proposed method could be widely applied to quantitative analysis of muscle activities based on SEMG.

Exercise amount calculation using a wearable half-cell potential sensor for mobile aerobic exercise management.

The obesity has grown to concerning proportions in recent years, and it causes heart disease, type 2 diabetes, breast cancer, and colon cancer. To get healthy weight, commercial wearable devices with a accelerometer have been released to help users to quantitatively manage calories. However, an accelerometer has disadvantages: large power consumption and expensive price. We suggested a new method to measure the exercise amount using a HCP sensor. We performed an experiment to compare accuracies of exercise amount estimation using a HCP sensor with using an accelerometer with five subjects, and the accuracy of the HCP sensor was comparable to it of the accelerometer. Since a HCP sensor has lower power consumption and cheaper price than an accelerometer, wearable sensor can be smaller and cheaper than current commercial devices.

An SEMG computer interface using three myoelectric sites for proportional two-dimensional cursor motion control and clicking for individuals with spinal cord injuries.

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 for each subject. In addition, a user can voluntarily control cursor movement speed by modulating muscle contraction levels. Three SCI patients and 10 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 0.1962±0.0562 b/s for the SCI patients and 0.4356±0.0706 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.

A PD control-based QRS detection algorithm for wearable ECG applications.

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.

Motion artifact reduction in electrocardiogram using adaptive filtering based on half cell potential monitoring.

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.

Synergy matrices to estimate fluid wrist movements by surface electromyography.

Although many efforts have been undertaken to develop an interface using surface electromyography (sEMG) to connect the gap between a human and a wrist prosthesis, most of these efforts have offered only static positioning (ON/OFF) of the prosthesis. This study introduced synergy matrices to extract fluid wrist movement intents by sEMG to allow individuals with wrist amputations to use wrist prostheses. A non-negative muscle synergy matrix was used to map muscle activities in the forearm into four predefined wrist movement intents (flexion/extension and radial/ulnar deviation). The directions of the predefined intents were constrained to two perpendicular axes, so each movement spanned only a one-dimensional space. A joint synergy matrix was used to span the whole two-dimensional space by combining the four wrist movement intents. Ten healthy subjects volunteered for a validation experiment, which was built as a virtual environment in which people with wrist amputation could receive myoelectric control training. The results showed that proportional two-degree-of-freedom (DOF) movements could be estimated by sEMG. This work could be useful not only for wrist prostheses but also for alternative computer interfaces and studies to examine motor adaptation by sEMG.

Development and evaluation of a assistive computer interface by SEMG for individuals with spinal cord injuries.

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.

A physiologically and biomechanically approximate model for surface electromyography amplitude estimation.

Surface electromygraphy (sEMG) provides information of the neural drive to the muscle, so muscle force estimation by sEMG is of high relevance in biomechanical studies and in bionic applications. Even though mean absolute value (MAV) has been widely used for sEMG amplitude estimation due to the probabilistic nature of sEMG, but it has been used without any comprehensive physiological justification. A physiologically and biomechanically approximate model for the force estimation would enable a clear understanding of the relationships between sEMG and the force, and it can be used as sEMG amplitude estimation method. We proposed a new sEMG amplitude estimation method comprising two procedures: MUAP (motor unit action potential) event detection and muscle force indication using a biomechanical muscle model. The estimation performances were evaluated with nine subjects and compared with MAV. The performance (R(2)) of the proposed method (0.94 ± 0.03) outperformed it of MAV (0.90 ± 0.02). The method we proposed should be widely applicable to quantitatively analysis muscle activities by sEMG.

Synergy matrices to extract fluid wrist motion intents via surface electromyography.

This paper presented an estimation method of multi-directional and proportional fluid wrist motion intents via sEMG using a non-negative muscle synergy matrix and a joint synergy matrix. A real-time experiment was performed to validate feasibility of the proposed method, and the experimental environment was realized for the individuals with wrist amputation. Only four wrist movements were predefined (wrist extension, wrist flexion, radial deviation, and ulnar deviation), but the experimental results showed that fluid wrist motion intents (e.g. a combination of wrist extension and ulnar deviation) could be extracted. This work could be useful for the people with wrist amputations to restore their wrist functions using myoelectric powered wrist prosthesis, and also for research to investigate how humans learn myoelectric controls in two-dimensions via training.

Real-time pinch force estimation by surface electromyography using an artificial neural network.

The palmar pinch force estimation is highly relevant not only in biomechanical studies, the analysis of sports activities, and ergonomic design analyses but also in clinical applications such as rehabilitation, in which information about muscle forces influences the physician's decisions on diagnosis and treatment. Force transducers have been used for such purposes, but they are restricted to grasping points and inevitably interfere with the human haptic sense because fingers cannot directly touch the environmental surface. We propose an estimation method of the palmar pinch force using surface electromyography (SEMG). Three myoelectric sites on the skin were selected on the basis of anatomical considerations and a Fisher discriminant analysis (FDA), and SEMG at these sites yields suitable information for pinch force estimation. An artificial neural network (ANN) was implemented to map the SEMG to the force, and its structure was optimized to avoid both under- and over-fitting problems. The resulting network was tested using SEMG signals recorded from the selected myoelectric sites of ten subjects in real time. The training time for each subject was short (approximately 96s), and the estimation results were promising, with a normalized root mean squared error (NRMSE) of 0.081+/-0.023 and a correlation (CORR) of 0.968+/-0.017.

Development and quantitative performance evaluation of a noninvasive EMG computer interface.

This paper describes a noninvasive electromyography (EMG) signal-based computer interface and a performance evaluation method based on Fitts' law. The EMG signals induced by volitional wrist movements were acquired from four sites in the lower arm to extract users' intentions, and six classes of wrist movements were distinguished using an artificial neural network. Using the developed interface, a user can move the cursor, click buttons, and type text on a computer. The test setup was built to evaluate the developed interface, and the mouse was tested by five volunteers with intact limbs. The performance of the developed computer interface and the mouse was tested at 1.299 and 7.733 b/s, respectively, and these results were compared with the performance of a commercial noninvasive brain signal interface (0.386 b/s). The results show that the developed interface performed better than the commercial interface, but less satisfactorily than a computer mouse. Although some issues remain to be resolved, the developed EMG interface has the potential to help people with motor disabilities to access computers and Internet environments in a natural and intuitive manner.

Graphic and haptic modelling of the oesophagus for VR-based medical simulation.

BACKGROUND: Medical simulators with vision and haptic feedback have been applied to many medical procedures in recent years, due to their safe and repetitive nature for training. Among the many technical components of the simulators, realistic and interactive organ modelling stands out as a key issue for judging the fidelity of the simulation. This paper describes the modelling of an oesophagus for a real-time laparoscopic surgical simulator. METHODS: For realistic simulation, organ deformation and tissue cutting in the oesophagus are implemented with geometric organ models segmented from the Visible Human Dataset. The tissue mechanical parameters were obtained from in vivo animal experiments and integrated with graphic and haptic devices into the laparoscopic surgical simulation system inside an abdominal mannequin. RESULTS: This platform can be used to demonstrate deformation and incision of the oesophagus by surgical instruments, where the user can haptically interact with the virtual soft tissues and simultaneously see the corresponding organ deformation on the visual display. CONCLUSIONS: Current laparoscopic surgical training has been transformed from the traditional apprenticeship model to simulation-based methods. The outcome of the model could replace conventional training systems and could be useful in effectively transferring surgical skills to novice surgeons.

Virtual surgery simulation for medical training using multi-resolution organ models.

BACKGROUND: Real-time simulation of organ deformation is one of the biggest challenges in virtual surgery, due to the conflicting requirements of real-time interactivity and simulation realism. In this paper we propose a method to overcome this challenge by introducing a multi-resolution modelling technique. METHODS: In our approach a reasonably coarse global model is locally enhanced, using a mesh subdivision and smoothing algorithm. The global model is based on a discretization of the boundary integral representation of three-dimensional deformable objects. Local refinements are provided at the tool-tissue interaction region by a local subdivision technique. RESULTS: As an example, we have developed a deformable human kidney model generated from the Visible Human Dataset, with tissue properties determined from in vivo animal experiments. A mixed reality laparoscopic surgical training system has been developed, using an abdominal mannequin and force feedback devices. CONCLUSIONS: The use of precomputation and structural re-analysis techniques results in a very rapid computation procedure. Validation of the simulator is in progress.

Classification of afferent signals recorded with a single cuff electrode.

The implementation of systems to restore sensorimotor functions in person with neurological disabilities is an important research area. In the past, many studies have been carried out to develop closed-loop neuroprostheses based on the processing of electroneurographic (ENG) signals recorded from physiological sensors using cuff electrodes. However, the potential of this approach is not completely clear. In this paper, an artificial neural network is used to discriminate afferent ENG signals evoked by different mechanical stimuli and recorded with a single cuff electrode. The preliminary results indicate that even single cuff ENG signals can be useful to extract interesting information with good performance. In the future, the possibility of discriminating additional stimuli using additional channels and more advanced classification techniques will be investigated.

Development of a surgical simulator for laparoscopic esophageal procedures.

This paper describes a surgical simulator of laparoscopic esophageal procedures with virtual reality (VR) technology for training purposes. Because the success of the procedure is highly influenced by the number of practices, a VR based simulator is a promising training medium in terms of safety and expense. For the realistic simulation of tool-tissue interactions, a physically based soft tissue model of the esophagus and an algorithm of cutting on the lower esophagus skin are implemented. The tissue parameters from in vivo animal experiments are combined with geometric organ models segmented from the visible human dataset and integrated into the laparoscopic surgical simulation system consisting of haptic interface devices inside an abdominal mannequin and a graphic display. This system can be used to demonstrate deformation and cutting of the esophagus, where the user can haptically interact with the virtual soft tissues and see the corresponding organ deformation on the visual display simultaneously.